Challenges in large scale quantum mechanical calculations

AGDIFF: Attention-Enhanced Diffusion for Molecular Geometry Prediction

Accurate prediction of molecular geometries is crucial for drug discovery and materials science. Existing fast conformer prediction algorithms often rely on approximate empirical energy functions, resulting in low accuracy. More accurate methods like ab initio molecular dynamics and Markov chain Monte Carlo can be computationally expensive due to the need for evaluating quantum mechanical energy functions. To address this, we introduce AGDIFF, a novel machine learning framework that utilizes diffusion models for efficient and accurate molecular structure prediction. AGDIFF extends previous models (such as GeoDiff) by enhancing the global, local, and edge encoders with attention mechanisms, an improved SchNet architecture, batch normalization, and feature expansion techniques. AGDIFF outperforms GeoDiff on both the GEOM-QM9 and GEOM-Drugs data sets. For GEOM-QM9, with a threshold (δ) of 0.5 Å, AGDIFF achieves a mean COV-R of 93.08% and a mean MAT-R of 0.1965 Å. On the more complex GEOM-Drugs data set, using δ = 1.25 Å, AGDIFF attains a median COV-R of 100.00% and a mean MAT-R of 0.8237 Å. These findings demonstrate AGDIFF's potential to advance molecular modeling techniques, enabling more efficient and accurate prediction of molecular geometries, thus contributing to computational chemistry, drug discovery, and materials design. https://github.com/ADicksonLab/AGDIFF.

Machine-Learning-Accelerated DFT Conformal Sampling of Catalytic Processes

Computational modeling of catalytic processes at gas/solid interfaces plays an increasingly important role in chemistry, enabling accelerated materials and process optimization and rational design. However, efficiency, accuracy, thoroughness, and throughput must be enhanced to maximize its practical impact. By combining interpolation of DFT energetics via highly accurate Machine-Learning Potentials with conformal techniques for building the training database, we present here an original approach (that we name Conformal Sampling of Catalytic Processes, CSCP), to accelerate and achieve an accurate and thorough sampling of novel systems by exporting existing information on a worked-out case. We use methanol decomposition (of interest in the field of hydrogen production and storage) as a test catalytic reaction. Starting from worked-out Pt-based systems, we show that after only two iterations of active-learning CSCP is able to provide reaction energy diagrams for a set of 7 diverse systems (Pd, Ni, Au, Ag, Cu, Co, Fe) leading to DFT-accuracy-level predictions. Cases exhibiting a change in adsorption sites and mechanisms are also successfully reproduced as tests of catalytic path modification. The CSCP approach thus offers itself as an operative tool to fully take advantage of accumulated information to achieve high-throughput sampling of catalytic processes.

Self-supervised generative models for crystal structures

Inspired by advancements in natural language processing, we utilize self-supervised learning and an equivariant graph neural network to develop a unified platform for training generative models capable of generating inorganic crystal structures, as well as efficiently adapting to downstream tasks in material property prediction. To mitigate the challenge of evaluating the reliability of generated structures during training, we employ a generative adversarial network (GAN) with its discriminator being a cost-effective reliability evaluator, significantly enhancing model performance. We demonstrate the utility of our model in optimizing crystal structures under predefined conditions. Without external properties acquired experimentally or numerically, our model further displays its capability to help understand inorganic crystal formation by grouping chemically similar elements. This paper extends an invitation to further explore the scientific understanding of material structures through generative models, offering a fresh perspective on the scope and efficacy of machine learning in material science.

Accelerating Relativistic Exact-Two-Component Density Functional Theory Calculations with Graphical Processing Units

Numerical integration of the exchange-correlation potential is an inherently parallel problem that can be significantly accelerated by graphical processing units (GPUs). In this Letter, we present the first implementation of GPU-accelerated exchange-correlation potential in the GauXC library for relativistic, 2-component density functional theory. By benchmarking against copper, silver, and gold coinage metal clusters, we demonstrate the speed and efficiency of our implementation, achieving significant speedup compared to CPU-based calculations. One GPU card provides computational power equivalent to roughly 400 CPU cores in the context of this work. The speedup further increases for larger systems, highlighting the potential of our approach for future, more computationally demanding simulations. Our implementation supports arbitrary angular momentum basis functions, enabling the simulation of systems with heavy elements and providing substantial speedup to relativistic electronic structure calculations. This advancement paves the way for more efficient and extensive computational studies in the field of density functional theory.

dxtb-An efficient and fully differentiable framework for extended tight-binding

Automatic differentiation (AD) emerged as an integral part of machine learning, accelerating model development by enabling gradient-based optimization without explicit analytical derivatives. Recently, the benefits of AD and computing arbitrary-order derivatives with respect to any variable were also recognized in the field of quantum chemistry. In this work, we present dxtb-an open-source, fully differentiable framework for semiempirical extended tight-binding (xTB) methods. Developed entirely in Python and leveraging PyTorch for array operations, dxtb facilitates extensibility and rapid prototyping while maintaining computational efficiency. Through comprehensive code vectorization and optimization, we essentially reach the speed of compiled xTB programs for high-throughput calculations of small molecules. The excellent performance also scales to large systems, and batch operability yields additional benefits for execution on parallel hardware. In particular, energy evaluations are on par with existing programs, whereas the speed of automatically differentiated nuclear derivatives is only 2 to 5 times slower compared to their analytical counterparts. We showcase the utility of AD in dxtb by calculating various molecular and spectroscopic properties, highlighting its capacity to enhance and simplify such evaluations. Furthermore, the framework streamlines optimization tasks and offers seamless integration of semiempirical quantum chemistry in machine learning, paving the way for physics-inspired end-to-end differentiable models. Ultimately, dxtb aims to further advance the capabilities of semiempirical methods, providing an extensible foundation for future developments and hybrid machine learning applications. The framework is accessible at https://github.com/grimme-lab/dxtb.

Nitrile Vibrational Lifetimes as Probes of Local Electric Fields

Optical measurements of electric fields have wide-ranging applications in the fields of chemistry and biology. Previously, such measurements focused on shifts in intensity or frequency. Here, we show that nitrile vibrational lifetimes can report local electric fields through ultrasensitive picosecond mid-infrared-near-infrared double-resonance fluorescence spectro-microscopy on Rhodamine 800. Using a robust convolution fitting approach, we observe that the nitrile vibrational lifetimes are strongly linearly correlated (R2 = 0.841) with solvent reaction fields. Supported by density functional theory, we rationalize this trend through a doorway model of intramolecular vibrational energy redistribution. This work provides new fundamental insights into the nature of vibrational energy flow in large polyatomic molecular systems and establishes a theoretical basis for electric field sensing with vibrational lifetimes, offering a new experimental dimension for probing intracellular electrostatics.

Characterization of iron(III) in aqueous and alkaline environments with ab initio and ReaxFF potentials

The iron(III) complexes [Fe(H2O)n(OH)m]3-m (n + m = 5, 6, m ≤ 3) and corresponding proton transfer reactions are studied with total energy calculations, the nudged elastic band (NEB) method, and molecular dynamics (MD) simulations using ab initio and a modification of reactive force field potentials, the ReaxFF-AQ potentials, based on the implementation according to Böhm et al. [J. Phys. Chem. C 120, 10849-10856 (2016)]. Applying ab initio potentials, the energies for the reactions [Fe(H2O)n(OH)m]3-m + H2O → [Fe(H2O)n-1(OH)m+1]2-m + H3O+ in a gaseous environment are in good agreement with comparable theoretical results. In an aqueous (aq) or alkaline environment, with the aid of NEB computations, respective minimum energy paths with energy barriers of up to 14.6 kcal/mol and a collective transfer of protons are modeled. Within MD simulations at room temperature, a permanent transfer of protons around the iron(III) ion is observed. The information gained concerning the geometrical and energetic properties of water and the [Fe(H2O)n(OH)m]3-m complexes from the ab initio computations has been used as reference data to optimize parameters for the O-H-Fe interaction within the ReaxFF-AQ approach. For the optimized ReaxFF-AQ parameter set, the statistical properties of the basic water model, such as the radial distribution functions and the proton hopping functions, are evaluated. For the [Fe(H2O)n(OH)m]3-m complexes, it was found that while geometrical and energetic properties are in good agreement with the ab initio data for gaseous environment, the statistical properties as obtained from the MD simulations are only partly in accordance with the ab initio results for the iron(III) complexes in aqueous or alkaline environments.

Positivity preserving density matrix minimization at finite temperatures via square root

We present a Wave Operator Minimization (WOM) method for calculating the Fermi-Dirac density matrix for electronic structure problems at finite temperature while preserving physicality by construction using the wave operator, i.e., the square root of the density matrix. WOM models cooling a state initially at infinite temperature down to the desired finite temperature. We consider both the grand canonical (constant chemical potential) and canonical (constant number of electrons) ensembles. Additionally, we show that the number of steps required for convergence is independent of the number of atoms in the system. We hope that the discussion and results presented in this article reinvigorate interest in density matrix minimization methods.

Phase-space path-integral representation of the quantum density of states: Monte Carlo simulation of strongly correlated soft-sphere fermions

The Wigner formulation of quantum mechanics is used to derive a path-integral representation of the quantum density of states (DOS) of strongly correlated fermions in the canonical ensemble. A path-integral Monte Carlo approach for the simulation of DOS and other thermodynamic functions is suggested. The derived Wigner function in the phase space resembles the Maxwell-Boltzmann distribution but allows for quantum effects. We consider a three-dimensional quantum system of strongly correlated soft-sphere fermions at different densities and temperatures. The calculated properties include the DOS, momentum distribution functions, spin-resolved radial distribution functions, potentials of mean force, and related energy levels obtained from the Bohr-Sommerfeld condition. We observe sharp peaks on DOS and momentum distribution curves, which are explained by the appearance of fermionic bound states.

Elastocaloric Effect in Graphene Kirigami

Kirigami, a traditional Japanese art of paper cutting, has recently been explored for its elastocaloric effect (ECE) in kirigami-based materials (KMs), where an applied strain induces temperature changes. Importantly, the feasibility of a nanoscale graphene kirigami monolayer was experimentally demonstrated. Here, we investigate the ECE in GK representing the thinnest possible KM to better understand this phenomenon. Through molecular dynamics simulations, we analyze the temperature change and coefficient of performance (COP) of GK. Our findings reveal that while GKs lack the intricate temperature changes observed in macroscopic KMs, they exhibit a substantial temperature change of approximately 9.32 K (23 times higher than that of macroscopic KMs, which is about 0.4 K) for heating and -3.50 K for cooling. Furthermore, they demonstrate reasonable COP values of approximately 1.57 and 0.62, respectively. It is noteworthy that the one-atom-thick graphene configuration prevents the occurrence of the complex temperature distribution observed in macroscopic KMs.

Computer-Aided Design of Eco-Friendly Imprinted Polymer Decorated Sensors Augmented by Self-Validated Ensemble Modeling Designs for the Quantitation of Drotaverine Hydrochloride in Dosage Form and Human Plasma

BACKGROUND

Computationally designed molecular imprinted polymer (MIP) incorporation into electrochemical sensors has many advantages to the performance of the designed sensors. The innovative self-validated ensemble modeling (SVEM) approach is a smart machine learning-based (ML) technique that enables the design of more accurate predictive models using smaller data sets.

OBJECTIVE

The novel SVEM experimental design methodology is exploited here exclusively to optimize the composition of four eco-friendly PVC membranes augmented by a computationally designed magnetic molecularly imprinted polymer to quantitatively determine drotaverine hydrochloride (DVN) in its combined dosage form and human plasma. Furthermore, the application of hybrid computational simulations such as molecular dynamics and quantum mechanical calculations (MD/QM) is a time-saving and eco-friendly provider for the tailored design of the MIP particles.

METHOD

Here, for the first time, the predictive power of ML is assembled with computational simulations to develop four PVC-based sensors decorated by computationally designed MIP particles using four different experimental designs known as central composite, SVEM-LASSO, SVEM-FWD, and SVEM-PFWD. The pioneering AGREE approach further assessed the greenness of the analytical methods, proving their eco-friendliness.

RESULTS

The proposed sensors showed decent Nernstian responses toward DVN in the range of 58.60-59.09 mV/decade with a linear quantitative range of 1 × 10-7 - 1 × 10-2 M and limits of detection in the range of 9.55 × 10-8 to 7.08 × 10-8 M. Moreover, the proposed sensors showed ultimate eco-friendliness and selectivity for their target in its combined dosage form and spiked human plasma.

CONCLUSIONS

The proposed sensors were validated in accordance with International Union of Pure and Applied Chemistry (IUPAC) recommendations, proving their sensitivity and selectivity for drotaverine determination in dosage form and human plasma.

HIGHLIGHTS

This work presents the first ever application of both the innovative SVEM designs and MD/QM simulations in the optimization and fabrication of drotaverine-sensitive and selective MIP-decorated PVC sensors.

Biochemical and Phylogenetic Analysis of Italian Phaseolus vulgaris Cultivars as Sources of α-Amylase and α-Glucosidase Inhibitors

Phaseolus vulgaris α-amylase inhibitor (α-AI) is a protein that has recently gained commercial interest, as it inhibits mammalian α-amylase activity, reducing the absorption of dietary carbohydrates. Numerous studies have reported the efficacy of preparations based on this protein on the control of glycaemic peaks in type-2 diabetes patients and in overweight subjects. A positive influence on microbiota regulation has also been described. In this work, ten insufficiently studied Italian P. vulgaris cultivars were screened for α-amylase- and α-glucosidase-inhibiting activity, as well as for the absence of antinutritional compounds, such as phytohemagglutinin (PHA). All the cultivars presented α-glucosidase-inhibitor activity, while α-AI was missing in two of them. Only the Nieddone cultivar (ACC177) had no haemagglutination activity. In addition, the partial nucleotide sequence of the α-AI gene was identified with the degenerate hybrid oligonucleotide primer (CODEHOP) strategy to identify genetic variability, possibly linked to functional α-AI differences, expression of the α-AI gene, and phylogenetic relationships. Molecular studies showed that α-AI was expressed in all the cultivars, and a close similarity between the Pisu Grogu and Fasolu cultivars' α-AI and α-AI-4 isoform emerged from the comparison of the partially reconstructed primary structures. Moreover, mechanistic models revealed the interaction network that connects α-AI with the α-amylase enzyme characterized by two interaction hotspots (Asp38 and Tyr186), providing some insights for the analysis of the α-AI primary structure from the different cultivars, particularly regarding the structure-activity relationship. This study can broaden the knowledge about this class of proteins, fuelling the valorisation of Italian agronomic biodiversity through the development of commercial preparations from legume cultivars.

Accelerating the density-functional tight-binding method using graphical processing units

Acceleration of the density-functional tight-binding (DFTB) method on single and multiple graphical processing units (GPUs) was accomplished using the MAGMA linear algebra library. Two major computational bottlenecks of DFTB ground-state calculations were addressed in our implementation: the Hamiltonian matrix diagonalization and the density matrix construction. The code was implemented and benchmarked on two different computer systems: (1) the SUMMIT IBM Power9 supercomputer at the Oak Ridge National Laboratory Leadership Computing Facility with 1-6 NVIDIA Volta V100 GPUs per computer node and (2) an in-house Intel Xeon computer with 1-2 NVIDIA Tesla P100 GPUs. The performance and parallel scalability were measured for three molecular models of 1-, 2-, and 3-dimensional chemical systems, represented by carbon nanotubes, covalent organic frameworks, and water clusters.

GA, Jensen SR, Eikås RDR, Frediani L. MRChem Multiresolution Analysis Code for Molecular Electronic Structure Calculations: Performance and Scaling Properties

MRChem is a code for molecular electronic structure calculations, based on a multiwavelet adaptive basis representation. We provide a description of our implementation strategy and several benchmark calculations. Systems comprising more than a thousand orbitals are investigated at the Hartree-Fock level of theory, with an emphasis on scaling properties. With our design, terms that formally scale quadratically with the system size in effect have a better scaling because of the implicit screening introduced by the inherent adaptivity of the method: all operations are performed to the requested precision, which serves the dual purpose of minimizing the computational cost and controlling the final error precisely. Comparisons with traditional Gaussian-type orbitals-based software show that MRChem can be competitive with respect to performance.

Benefits of hybrid QM/MM over traditional classical mechanics in pharmaceutical systems

Hybrid quantum mechanics/molecular mechanics (QM/MM) is one of the most reliable approaches for accurately modeling and studying the complex pharmaceutical discovery system. Classical mechanics has significantly accelerated the drug discovery process in the past decade. However, the current challenge is the large pool of false positives, which require extensive validation. Hybrid QM/MM is an effective solution for accurately studying ligand binding, structural mechanisms, free energy evaluation, and spectroscopic characterization. This article highlights the methodological details relevant to cost-effective hybrid QM/MM methods. This approach, combined with traditional pharmacoinformatics methods, could be a reliable strategy to balance the cost and accuracy of the calculations.

Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures

We employ a recently developed complexity-reduction quantum mechanical (QM-CR) approach, based on complexity reduction of density functional theory calculations, to characterize the interactions of the SARS-CoV-2 spike receptor binding domain (RBD) with ACE2 host receptors and antibodies. QM-CR operates via ab initio identification of individual amino acid residue's contributions to chemical binding and leads to the identification of the impact of point mutations. Here, we especially focus on the E484K mutation of the viral spike protein. We find that spike residue 484 hinders the spike's binding to the human ACE2 receptor (hACE2). In contrast, the same residue is beneficial in binding to the bat receptor Rhinolophus macrotis ACE2 (macACE2). In agreement with empirical evidence, QM-CR shows that the E484K mutation allows the spike to evade categories of neutralizing antibodies like C121 and C144. The simulation also shows how the Delta variant spike binds more strongly to hACE2 compared to the original Wuhan strain, and predicts that a E484K mutation can further improve its binding. Broad agreement between the QM-CR predictions and experimental evidence supports the notion that ab initio modeling has now reached the maturity required to handle large intermolecular interactions central to biological processes.

Extending machine learning beyond interatomic potentials for predicting molecular properties

Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used to establish a relationship between a molecular structure and its chemical properties. This Review highlights developments in the use of ML to evaluate chemical properties such as partial atomic charges, dipole moments, spin and electron densities, and chemical bonding, as well as to obtain a reduced quantum-mechanical description. We overview several modern neural network architectures, their predictive capabilities, generality and transferability, and illustrate their applicability to various chemical properties. We emphasize that learned molecular representations resemble quantum-mechanical analogues, demonstrating the ability of the models to capture the underlying physics. We also discuss how ML models can describe non-local quantum effects. Finally, we conclude by compiling a list of available ML toolboxes, summarizing the unresolved challenges and presenting an outlook for future development. The observed trends demonstrate that this field is evolving towards physics-based models augmented by ML, which is accompanied by the development of new methods and the rapid growth of user-friendly ML frameworks for chemistry.

Local electronic structure analysis by ab initio elongation method: A benchmark using DNA block polymers

The ab initio elongation (ELG) method based on a polymerization concept is a feasible way to perform linear-scaling electronic structure calculations for huge aperiodic molecules while maintaining computational accuracy. In the method, the electronic structures are sequentially elongated by repeating (1) the conversion of canonical molecular orbitals (CMOs) to region-localized MOs (RLMOs), that is, active RLMOs localized onto a region close to an attacking monomer or frozen RLMOs localized onto the remaining region, and the subsequent (2) partial self-consistent-field calculations for an interaction space composed of the active RLMOs and the attacking monomer. For each ELG process, one can obtain local CMOs for the interaction space and the corresponding local orbital energies. Local site information, such as the local highest-occupied/lowest-unoccupied MOs, can be acquired with linear-scaling efficiency by correctly including electronic effects from the frozen region. In this study, we performed a local electronic structure analysis using the ELG method for various DNA block polymers with different sequential patterns. This benchmark aimed to confirm the effectiveness of the method toward the efficient detection of a singular local electronic structure in unknown systems as a future practical application. We discussed the high-throughput efficiency of our method and proposed a strategy to detect singular electronic structures by combining with a machine learning technique.

Small-Basis Set Density-Functional Theory Methods Corrected with Atom-Centered Potentials

Density functional theory (DFT) is currently the most popular method for modeling noncovalent interactions and thermochemistry. The accurate calculation of noncovalent interaction energies, reaction energies, and barrier heights requires choosing an appropriate functional and, typically, a relatively large basis set. Deficiencies of the density-functional approximation and the use of a limited basis set are the leading sources of error in the calculation of noncovalent and thermochemical properties in molecular systems. In this article, we present three new DFT methods based on the BLYP, M06-2X, and CAM-B3LYP functionals in combination with the 6-31G* basis set and corrected with atom-centered potentials (ACPs). ACPs are one-electron potentials that have the same form as effective-core potentials, except they do not replace any electrons. The ACPs developed in this work are used to generate energy corrections to the underlying DFT/basis-set method such that the errors in predicted chemical properties are minimized while maintaining the low computational cost of the parent methods. ACPs were developed for the elements H, B, C, N, O, F, Si, P, S, and Cl. The ACP parameters were determined using an extensive training set of 118655 data points, mostly of complete basis set coupled-cluster level quality. The target molecular properties for the ACP-corrected methods include noncovalent interaction energies, molecular conformational energies, reaction energies, barrier heights, and bond separation energies. The ACPs were tested first on the training set and then on a validation set of 42567 additional data points. We show that the ACP-corrected methods can predict the target molecular properties with accuracy close to complete basis set wavefunction theory methods, but at a computational cost of double-ζ DFT methods. This makes the new BLYP/6-31G*-ACP, M06-2X/6-31G*-ACP, and CAM-B3LYP/6-31G*-ACP methods uniquely suited to the calculation of noncovalent, thermochemical, and kinetic properties in large molecular systems.

Fast and Accurate Quantum Mechanical Modeling of Large Molecular Systems Using Small Basis Set Hartree-Fock Methods Corrected with Atom-Centered Potentials

There has been significant interest in developing fast and accurate quantum mechanical methods for modeling large molecular systems. In this work, by utilizing a machine learning regression technique, we have developed new low-cost quantum mechanical approaches to model large molecular systems. The developed approaches rely on using one-electron Gaussian-type functions called atom-centered potentials (ACPs) to correct for the basis set incompleteness and the lack of correlation effects in the underlying minimal or small basis set Hartree-Fock (HF) methods. In particular, ACPs are proposed for ten elements common in organic and bioorganic chemistry (H, B, C, N, O, F, Si, P, S, and Cl) and four different base methods: two minimal basis sets (MINIs and MINIX) plus a double-ζ basis set (6-31G*) in combination with dispersion-corrected HF (HF-D3/MINIs, HF-D3/MINIX, HF-D3/6-31G*) and the HF-3c method. The new ACPs are trained on a very large set (73 832 data points) of noncovalent properties (interaction and conformational energies) and validated additionally on a set of 32 048 data points. All reference data are of complete basis set coupled-cluster quality, mostly CCSD(T)/CBS. The proposed ACP-corrected methods are shown to give errors in the tenths of a kcal/mol range for noncovalent interaction energies and up to 2 kcal/mol for molecular conformational energies. More importantly, the average errors are similar in the training and validation sets, confirming the robustness and applicability of these methods outside the boundaries of the training set. In addition, the performance of the new ACP-corrected methods is similar to complete basis set density functional theory (DFT) but at a cost that is orders of magnitude lower, and the proposed ACPs can be used in any computational chemistry program that supports effective-core potentials without modification. It is also shown that ACPs improve the description of covalent and noncovalent bond geometries of the underlying methods and that the improvement brought about by the application of the ACPs is directly related to the number of atoms to which they are applied, allowing the treatment of systems containing some atoms for which ACPs are not available. Overall, the ACP-corrected methods proposed in this work constitute an alternative accurate, economical, and reliable quantum mechanical approach to describe the geometries, interaction energies, and conformational energies of systems with hundreds to thousands of atoms.

Forces from Stochastic Density Functional Theory under Nonorthogonal Atom-Centered Basis Sets

We develop a formalism for calculating forces on the nuclei within the linear-scaling stochastic density functional theory (sDFT) in a nonorthogonal atom-centered basis set representation (Fabian et al. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2019, 9, e1412, 10.1002/wcms.1412) and apply it to the Tryptophan Zipper 2 (Trp-zip2) peptide solvated in water. We use an embedded-fragment approach to reduce the statistical errors (fluctuation and systematic bias), where the entire peptide is the main fragment and the remaining 425 water molecules are grouped into small fragments. We analyze the magnitude of the statistical errors in the forces and find that the systematic bias is of the order of 0.065 eV/Å (∼1.2 × 10-3Eh/a0) when 120 stochastic orbitals are used, independently of system size. This magnitude of bias is sufficiently small to ensure that the bond lengths estimated by stochastic DFT (within a Langevin molecular dynamics simulation) will deviate by less than 1% from those predicted by a deterministic calculation.

Absorption Properties of Large Complex Molecular Systems: The DFTB/Fluctuating Charge Approach

We report on the first formulation of a novel polarizable QM/MM approach, where the density functional tight binding (DFTB) is coupled to the fluctuating charge (FQ) force field. The resulting method (DFTB/FQ) is then extended to the linear response within the TD-DFTB framework and challenged to study absorption spectra of large condensed-phase systems.

Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors

A rational therapeutic strategy is urgently needed for combating SARS-CoV-2 infection. Viral infection initiates when the SARS-CoV-2 receptor-binding domain (RBD) binds to the ACE2 receptor, and thus, inhibiting RBD is a promising therapeutic for blocking viral entry. In this study, the structure of lead antiviral candidate binder (LCB1), which has three alpha-helices (H1, H2, and H3), is used as a template to design and simulate several miniprotein RBD inhibitors. LCB1 undergoes two modifications: structural modification by truncation of the H3 to reduce its size, followed by single and double amino acid substitutions to enhance its binding with RBD. We use molecular dynamics (MD) simulations supported by ab initio density functional theory (DFT) calculations. Complete binding profiles of all miniproteins with RBD have been determined. The MD investigations reveal that the H3 truncation results in a small inhibitor with a -1.5 kcal/mol tighter binding to RBD than original LCB1, while the best miniprotein with higher binding affinity involves D17R or E11V + D17R mutation. DFT calculations provide atomic-scale details on the role of hydrogen bonding and partial charge distribution in stabilizing the minibinder:RBD complex. This study provides insights into general principles for designing potential therapeutics for SARS-CoV-2.

Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

Artificial intelligence: machine learning for chemical sciences

Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years. These methods have had phenomenal success in the areas of computer vision, speech recognition, natural language processing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems in natural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been applied to various domains in molecular sciences by providing much faster and sometimes more accurate solutions compared to traditional methods like Quantum Mechanical (QM) calculations, Density Functional Theory (DFT) or Molecular Mechanics (MM) based methods, etc. Some of the areas where the potential of ML methods are shown to be effective are in drug design, prediction of high-level quantum mechanical energies, molecular design, molecular dynamics materials, and retrosynthesis of organic compounds, etc. This article intends to conceptually introduce various modern ML methods and their relevance and applications in computational natural sciences. Synopsis Recent surge in the application of machine learning (ML) methods in fundamental sciences has led to a perspective that these methods may become important tools in chemical science. This perspective provides an overview of the modern ML methods and their successful applications in chemistry during the last few years.

Learning Electron Densities in the Condensed Phase

We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centered auxiliary basis, which enables an accurate expansion of the all-electron density in a form suitable for learning isolated and periodic systems alike. We show that, using this formulation, the electron densities of metals, semiconductors, and molecular crystals can all be accurately predicted using symmetry-adapted Gaussian process regression models, properly adjusted for the nonorthogonal nature of the basis. These predicted densities enable the efficient calculation of electronic properties, which present errors on the order of tens of meV/atom when compared to ab initio density-functional calculations. We demonstrate the key power of this approach by using a model trained on ice unit cells containing only 4 water molecules to predict the electron densities of cells containing up to 512 molecules and see no increase in the magnitude of the errors of derived electronic properties when increasing the system size. Indeed, we find that these extrapolated derived energies are more accurate than those predicted using a direct machine-learning model. Finally, on heterogeneous data sets SALTED can predict electron densities with errors below 4%.

Projector-Based Quantum Embedding for Molecular Systems: An Investigation of Three Partitioning Approaches

Projector-based embedding is a relatively recent addition to the collection of methods that seek to utilize chemical locality to provide improved computational efficiency. This work considers the interactions between the different proposed procedures for this method and their effects on the accuracy of the results. The interplay between the embedded background, projector type, partitioning scheme, and level of atomic orbital (AO) truncation are investigated on a selection of reactions from the literature. The Huzinaga projection approach proves to be more reliable than the level-shift projection when paired with other procedural options. Active subsystem partitioning from the subsystem projected AO decomposition (SPADE) procedure proves slightly better than the combination of Pipek-Mezey localization and Mulliken population screening (PMM). Along with these two options, a new partitioning criteria is proposed based on subsystem von Neumann entropy and the related subsystem orbital occupancy. This new method overlaps with the previous PMM method, but the screening process is computationally simpler. Finally, AO truncation proves to be a robust option for the tested systems when paired with the Huzinaga projection, with satisfactory results being acquired at even the most severe truncation level.

PyRad: A software shell for simulating radiolysis with Qball package

The assessment of the radiolytic stability of media is an important task in the fields of nuclear power engineering and radiochemistry. Such studies must be carried out in special laboratory conditions with the use of sources of ionizing radiation, which may increase personal doses of the staff. In addition, difficulties arise in studying the products of irradiated media. While it is impossible to abandon experiments to obtain reliable results in this area, computational methods of quantum chemistry can reduce the number of experiments and help understand the mechanisms of the reactions that occur during radiolysis. Here we would like to present a software shell of the Qb@ll program performing time-dependent density functional theory simulations of the radiolysis process.

Removing artifacts in polarizable embedding calculations of one- and two-photon absorption spectra of fluorescent proteins

The multiscale calculations involving excited states may suffer from the electron spill-out (ESO) problem. This seems to be especially the case when the environment of the core region, described with the electronic structure method, is approximated by a polarizable force field. The ESO effect often leads to incorrect physical character of electronic excitations, spreading outside the quantum region, which, in turn, results in erroneous absorption spectra. In this work, we investigate means to remove the artifacts in one-photon absorption (OPA) and two-photon absorption (TPA) spectra of green and yellow fluorescent protein representatives. This includes (i) using different basis sets, (ii) extending the core subsystem beyond the chromophore, (iii) modification of polarization interaction between the core region and its environment, and (iv) including the Pauli repulsion through effective core potentials (ECPs). Our results clearly show that ESO is observed when diffuse functions are used to assemble the multielectron wave function regardless of the exchange-correlation functional used. Furthermore, extending the core region, thus accounting for exchange interactions between the chromophore and its environment, leads to even more spurious excited states. Also, damping the interactions between the core subsystem and the polarizable force field is hardly helpful. In contrast, placing ECPs in the position of sites creating the embedding potential leads to the removal of artificious excited states that presumably should not be observed in the OPA and TPA spectra. We prove that it is a reliable and cost-effective approach for systems where the covalent bond(s) between the core region and its environment must be cut.

Superior Monitoring of Chemical Exposure Using Nanoconfinement Technology

INTRODUCTION

Military personnel are exposed to a broad range of potentially toxic compounds that can affect their health. These hazards are unpredictable because military service occurs in a wide array of uncontrolled environments. Therefore, a novel sorbent was developed that allows the fabrication of lightweight personal samplers that are both capable of sorbing an extremely wide range of organic chemical types and able to stabilize reactive compounds.

MATERIALS AND METHODS

OSU-6, a nanoporous silica, was provided by XploSafe LLC. The sorption capacity for several volatile organic compounds, the temperatures required for thermal desorption of adsorbed compounds, and the sampling rates for targeted analytes were determined.

RESULTS

The uptake capacity was found to be on average 1.5 g/g of sorbent. Analytes were not only held tightly but also could be desorbed upon heating the sorbate to temperatures below 150°C. Sampling rates for volatile organic compound by an OSU-6 sampler badge were on average, 5.7 times higher than those for a commercially available activated carbon badge. Theoretical calculations showed that sorption of volatile organic compounds on the surface of the tightly curved pore walls in OSU-6 is because of exceptionally strong cumulative addition of Van der Waals forces. Analytes could readily be analyzed by either solvent extraction or thermal desorption gas chromatography/mass spectrometry techniques. Excellent sampling rates, high concentrations of analytes in the OSU-6 sorbent matrix, and high desorption efficiencies (recoveries) were obtained using the thermal desorption method.

CONCLUSIONS

The performance of the OSU-6 sorbent makes it highly capable of meeting the need for personal samplers that enable Individual Longitudinal Exposure Records development. It can adsorb an extremely wide array of different volatile organic compounds, it can stabilize reactive compounds, it has high sampling rates coupled with high capacity that provide both sensitivity and resistance to saturation, and it is unique in being very amenable to thermal desorption in combination with having strong sorbate binding and high capacity and surface area.

From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape

Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems. As the hardware and software have evolved, so have the theoretical and computational chemistry methods and algorithms. Parallel computers clearly changed the common computing paradigm in the late 1970s and 80s, and the field has again seen a paradigm shift with the advent of graphical processing units. This review explores the challenges and some of the solutions in transforming software from the terascale to the petascale and now to the upcoming exascale computers. While discussing the field in general, NWChem and its redesign, NWChemEx, will be highlighted as one of the early codesign projects to take advantage of massively parallel computers and emerging software standards to enable large scientific challenges to be tackled.

On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters

The predominance of Kohn–Sham density functional theory (KS-DFT) for the theoretical treatment of large experimentally relevant systems in molecular chemistry and materials science relies primarily on the existence of efficient software implementations which are capable of leveraging the latest advances in modern high-performance computing (HPC). With recent trends in HPC leading toward increasing reliance on heterogeneous accelerator-based architectures such as graphics processing units (GPU), existing code bases must embrace these architectural advances to maintain the high levels of performance that have come to be expected for these methods. In this work, we purpose a three-level parallelism scheme for the distributed numerical integration of the exchange-correlation (XC) potential in the Gaussian basis set discretization of the Kohn–Sham equations on large computing clusters consisting of multiple GPUs per compute node. In addition, we purpose and demonstrate the efficacy of the use of batched kernels, including batched level-3 BLAS operations, in achieving high levels of performance on the GPU. We demonstrate the performance and scalability of the implementation of the purposed method in the NWChemEx software package by comparing to the existing scalable CPU XC integration in NWChem.

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. We finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

GPU algorithms for density matrix methods on MOPAC: linear scaling electronic structure calculations for large molecular systems

Purification of the density matrix methods should be employed when dealing with complex chemical systems containing many atoms. The running times for these methods scale linearly with the number of atoms if we consider the sparsity from the density matrix. Since the efficiency expected from those methods is closely tied to the underlying parallel implementations of the linear algebra operations (e.g., P² = P × P), we proposed a central processing unit (CPU) and graphics processing unit (GPU) parallel matrix-matrix multiplication in SVBR (symmetrical variable block row) format for energy calculations through the SP2 algorithm. This algorithm was inserted in MOPAC's MOZYME method, using the original LMO Fock matrix assembly, and the atomic integral calculation implemented on it. Correctness and performance tests show that the implemented SP2 is accurate and fast, as the GPU is able to achieve speedups up to 40 times for a water cluster system with 42,312 orbitals running in one NVIDIA K40 GPU card compared to the single-threaded version. The GPU-accelerated SP2 algorithm using the MOZYME LMO framework enables the calculations of semiempirical wavefunction with stricter SCF criteria for localized charged molecular systems, as well as the single-point energies of molecules with more than 100.000 LMO orbitals in less than 1 h. Graphical abstract Parallel CPU and GPU purification algorithms for electronic structure calculations were implemented in MOPAC's MOZYME method. Some matrices in these calculations, e.g., electron density P, are compressed, and the developed linear algebra operations deal with non-zero entries only. We employed the NVIDIA/CUDA platform to develop GPU algorithms, and accelerations up to 40 times for larger systems were achieved.

Density Functional Theory in the Prediction of Mutagenicity: A Perspective

As a field, computational toxicology is concerned with using in silico models to predict and understand the origins of toxicity. It is fast, relatively inexpensive, and avoids the ethical conundrum of using animals in scientific experimentation. In this perspective, we discuss the importance of computational models in toxicology, with a specific focus on the different model types that can be used in predictive toxicological approaches toward mutagenicity (SARs and QSARs). We then focus on how quantum chemical methods, such as density functional theory (DFT), have previously been used in the prediction of mutagenicity. It is then discussed how DFT allows for the development of new chemical descriptors that focus on capturing the steric and energetic effects that influence toxicological reactions. We hope to demonstrate the role that DFT plays in understanding the fundamental, intrinsic chemistry of toxicological reactions in predictive toxicology.

The ONETEP linear-scaling density functional theory program

We present an overview of the onetep program for linear-scaling density functional theory (DFT) calculations with large basis set (plane-wave) accuracy on parallel computers. The DFT energy is computed from the density matrix, which is constructed from spatially localized orbitals we call Non-orthogonal Generalized Wannier Functions (NGWFs), expressed in terms of periodic sinc (psinc) functions. During the calculation, both the density matrix and the NGWFs are optimized with localization constraints. By taking advantage of localization, onetep is able to perform calculations including thousands of atoms with computational effort, which scales linearly with the number or atoms. The code has a large and diverse range of capabilities, explored in this paper, including different boundary conditions, various exchange-correlation functionals (with and without exact exchange), finite electronic temperature methods for metallic systems, methods for strongly correlated systems, molecular dynamics, vibrational calculations, time-dependent DFT, electronic transport, core loss spectroscopy, implicit solvation, quantum mechanical (QM)/molecular mechanical and QM-in-QM embedding, density of states calculations, distributed multipole analysis, and methods for partitioning charges and interactions between fragments. Calculations with onetep provide unique insights into large and complex systems that require an accurate atomic-level description, ranging from biomolecular to chemical, to materials, and to physical problems, as we show with a small selection of illustrative examples. onetep has always aimed to be at the cutting edge of method and software developments, and it serves as a platform for developing new methods of electronic structure simulation. We therefore conclude by describing some of the challenges and directions for its future developments and applications.

Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding

With the development of low order scaling methods for performing Kohn-Sham density functional theory, it is now possible to perform fully quantum mechanical calculations of systems containing tens of thousands of atoms. However, with an increase in the size of the system treated comes an increase in complexity, making it challenging to analyze such large systems and determine the cause of emergent properties. To address this issue, in this paper, we present a systematic complexity reduction methodology which can break down large systems into their constituent fragments and quantify interfragment interactions. The methodology proposed here requires no a priori information or user interaction, allowing a single workflow to be automatically applied to any system of interest. We apply this approach to a variety of different systems and show how it allows for the derivation of new system descriptors, the design of QM/MM partitioning schemes, and the novel application of graph metrics to molecules and materials.

Machine learning for interatomic potential models

The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal providing an overview of some of the leading methods in this field. In this perspective, we provide an updated discussion of recent developments, emerging trends, and promising areas for future research in this field. We include in this discussion an overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression.

Molecular Geometry Prediction using a Deep Generative Graph Neural Network

A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different. In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner. On three large-scale datasets containing small molecules, we show that our method generates a set of conformations that on average is far more likely to be close to the corresponding reference conformations than are those obtained from conventional force field methods. Our method maintains geometrical diversity by generating conformations that are not too similar to each other, and is also computationally faster. We also show that our method can be used to provide initial coordinates for conventional force field methods. On one of the evaluated datasets we show that this combination allows us to combine the best of both methods, yielding generated conformations that are on average close to reference conformations with some very similar to reference conformations.

Toward Efficient GW Calculations Using Numerical Atomic Orbitals: Benchmarking and Application to Molecular Dynamics Simulations

The use of atomic orbitals in Hedin's GW approximation provides, in principle, an inexpensive alternative to plane-wave basis sets, especially when modeling large molecules. However, benchmarking of the algorithms and basis sets is essential for a careful balance between cost and accuracy. In this paper, we present an implementation of the GW approximation using numerical atomic orbitals and a pseudopotential treatment of core electrons. The combination of a contour deformation technique with a one-shot extraction of quasiparticle energies provides an efficient scheme for many applications. The performance of the implementation with respect to the basis set convergence and the effect of the use of pseudopotentials has been tested for the 117 closed-shell molecules from the G2/97 test set and 24 larger acceptor molecules from another recently proposed test set. Moreover, to demonstrate the potential of our method, we compute the thermally averaged GW density of states of a large photochromic compound by sampling ab initio molecular dynamics trajectories at different temperatures. The computed thermal line widths indicate approximately twice as large electron-phonon couplings with GW than with standard DFT-GGA calculations. This is further confirmed using frozen-phonon calculations.