Benchmarking DFT approaches for the calculation of polarizability inputs for refractive index predictions in organic polymers

Computer-Aided Drug Design in Research on Chinese Materia Medica: Methods, Applications, Advantages, and Challenges

Chinese materia medica (CMM) refers to the medicinal substances used in traditional Chinese medicine. In recent years, CMM has become globally prevalent, and scientific research on CMM has increasingly garnered attention. Computer-aided drug design (CADD) has been employed in Western medicine research for many years, contributing significantly to its progress. However, the role of CADD in CMM research has not been systematically reviewed. This review briefly introduces CADD methods in CMM research from the perspectives of computational chemistry (including quantum chemistry, molecular mechanics, and quantum mechanics/molecular mechanics) and informatics (including cheminformatics, bioinformatics, and data mining). Then, it provides an exhaustive discussion of the applications of these CADD methods in CMM research through rich cases. Finally, the review outlines the advantages and challenges of CADD in CMM research. In conclusion, despite the current challenges, CADD still offers unique advantages over traditional experiments. With the development of the CMM industry and computer science, especially driven by artificial intelligence, CADD is poised to play an increasingly pivotal role in advancing CMM research.

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

Although solar fuels photocatalysis offers the promise of converting carbon dioxide directly with sunlight as commercially scalable solutions have remained elusive over the past few decades, despite significant advancements in photocatalysis band-gap engineering and atomic site activity. The primary challenge lies not in the discovery of new catalyst materials, which are abundant, but in overcoming the bottlenecks related to material-photoreactor synergy. These factors include achieving photogeneration and charge-carrier recombination at reactive sites, utilizing high mass transfer efficiency supports, maximizing solar collection, and achieving uniform light distribution within a reactor. Addressing this multi-dimensional problem necessitates harnessing machine learning techniques to analyze real-world data from photoreactors and material properties. In this perspective, the challenges are outlined associated with each bottleneck factor, review relevant data analysis studies, and assess the requirements for developing a comprehensive solution that can unlock the full potential of solar fuels photocatalysis technology. Physics-informed machine learning (or Physics Neural Networks) may be the key to advancing this important area from disparate data towards optimal reactor solutions.

The Influence of Large Pendent Groups on Chain Anisotropy and Electrical Energy Loss of Polyimides at High Frequency through All-Atomic Molecular Simulation

Polyimide is a potential material for high-performance printed circuit boards because of its chemical stability and excellent thermal and mechanical properties. Flexible printed circuit boards must have a low static dielectric constant and dielectric loss to reduce signal loss in high-speed communication devices. Engineering the molecular structure of polyimides with large pendant groups is a strategy to reduce their dielectric constant. However, there is no systematic study on how the large pendant groups influence electrical energy loss. We integrated all-atomic molecular dynamics and semi-empirical quantum mechanical calculations to examine the influence of pendant groups on polymer chain anisotropy and electrical energy loss at high frequencies. We analyzed the radius of gyration, relative shape anisotropy, dipole moment, and degree of polarization of the selected polyimides (TPAHF, TmBPHF, TpBPHF, MPDA, TriPMPDA, m-PDA, and m-TFPDA). The simulation results show that anisotropy perpendicular to chain direction and local chain rigidity correlate to electrical energy loss rather than dipole moment magnitudes. Polyimides with anisotropic pendant groups and significant local chain rigidity reduce electrical energy loss. The degree of polarization correlated well with the dielectric loss with a moderate computational cost, and difficulties in directly calculating the dielectric loss were circumvented.

Accurate prediction of global-density-dependent range-separation parameters based on machine learning

In this work, we develop an accurate and efficient XGBoost machine learning model for predicting the global-density-dependent range-separation parameter, ωGDD, for long-range corrected functional (LRC)-ωPBE. This ωGDDML model has been built using a wide range of systems (11 466 complexes, ten different elements, and up to 139 heavy atoms) with fingerprints for the local atomic environment and histograms of distances for the long-range atomic correlation for mapping the quantum mechanical range-separation values. The promising performance on the testing set with 7046 complexes shows a mean absolute error of 0.001 117 a0-1 and only five systems (0.07%) with an absolute error larger than 0.01 a0-1, which indicates the good transferability of our ωGDDML model. In addition, the only required input to obtain ωGDDML is the Cartesian coordinates without electronic structure calculations, thereby enabling rapid predictions. LRC-ωPBE(ωGDDML) is used to predict polarizabilities for a series of oligomers, where polarizabilities are sensitive to the asymptotic density decay and are crucial in a variety of applications, including the calculations of dispersion corrections and refractive index, and surpasses the performance of all other popular density functionals except for the non-tuned LRC-ωPBE. Finally, LRC-ωPBE (ωGDDML) combined with (extended) symmetry-adapted perturbation theory is used in calculating noncovalent interactions to further show that the traditional ab initio system-specific tuning procedure can be bypassed. The present study not only provides an accurate and efficient way to determine the range-separation parameter for LRC-ωPBE but also shows the synergistic benefits of fusing the power of physically inspired density functional LRC-ωPBE and the data-driven ωGDDML model.

First-Principles Calculations to Investigate the Stability and Thermodynamic Properties of a Newly Exposed Lithium-Gallium-Iridium-Based Full-Heusler Compound

The structural, optical, electrical, thermodynamic, superconducting, and mechanical characteristics of LiGa₂Ir full-Heusler alloys with the MnCu₂Al configuration were comprehensively examined in this work using the first-principles computation approach premised upon density functional analysis. This theoretical approach is the first to investigate the influence of pressure on the mechanical and optical characteristics of LiGa₂Ir. The structural and chemical bonding analysis shows that hydrostatic pressure caused a decrease in the lattice constant, volume, and bond length of each cell. According to the mechanical property calculations, the LiGa₂Ir cubic Heusler alloy exhibits mechanical stability. It also has ductility and anisotropic behavior. This metallic substance shows no band gap throughout the applied pressure range. The physical characteristics of the LiGa₂Ir full-Heusler alloy are analyzed in the operating pressure range of 0-10 GPa. The quasi-harmonic Debye model is employed to analyze thermodynamic properties. The Debye temperature (291.31 K at 0 Pa) increases with hydrostatic pressure. A newly invented structure attracted a lot of attention around the globe for its superior superconductivity (T_c ∼ 2.95 K). Optical functions have also been improved after applying stress to utilize it in optoelectronic/nanoelectric devices. The optical function analysis is supported strongly by the electronic properties. Due to these reasons, LiGa₂Ir imposed an essential guiding principle for relevant future research and could be a credible candidate substance for industrial settings.

Combining ab initio and ab initio molecular dynamics simulations to predict the complex refractive indices of organic polymers

Organic polymers have attracted widespread interest in various fields ranging from optic and optoelectronic devices to optical system design owing to their light weight, high machinability, excellent thermal performance and reasonable costs. The complex refractive index is an inherent property of organic polymers and directly affects the accuracy of optical system simulation. This study introduces a theoretical protocol to accurately predict the complex refractive indices of organic polymers in the 0-5000 cm^-1 region for guiding the discovery and design of high-refractive index materials. In the proposed protocol, we computed the refractive indices of polymers with different monomer units using ab initio calculated static polarizability and mass density obtained by classical isothermal-isobaric ensemble simulations based on the Lorentz-Lorenz equation; we proposed a "Polymer Polarizability Fragment Segmentation" method to extrapolate the polarizabilities of polymers with longer chain lengths; meanwhile, the imaginary part of the dielectric functions of the polymers was calculated using the ab initio molecular dynamics (AIMD) method, and the real part of the dielectric functions was obtained using the Kramers-Kronig relation. We calculated the complex refractive indices of four commonly used organic polymers, i.e. polyethylene, polyvinyl chloride, polyvinyl alcohol and polylactic acid, to demonstrate the performance of the theoretical protocol. The approach combining ab initio and AIMD simulations is effective and economical to predict the complex refractive indices of organic polymers and other organic materials.

Interexcited State Photophysics I: Benchmarking Density Functionals for Computing Nonadiabatic Couplings and Internal Conversion Rate Constants

We present the first benchmarking study of nonadiabatic matrix coupling elements (NACMEs) calculated using different density functionals. Using the S₁ → S₀ transition in perylene solvated in toluene as a case study, we calculate the photophysical properties and corresponding rate constants for a variety of density functionals from each rung of Jacob's ladder. The singlet photoluminescence quantum yield (sPLQY) is taken as a measure of accuracy, measured experimentally here as 0.955. Important quantum chemical parameters such as geometries, absorption, emission, and adiabatic energies, NACMEs, Hessians, and transition dipole moments were calculated for each density functional basis set combination (data set) using density functional theory based multireference configuration interaction (DFT/MRCI) and compared to experiment where possible. We were able to derive simple relations between the TDDFT and DFT/MRCI photophysical properties; with semiempirical damping factors of ∼0.843 ± 0.017 and ∼0.954 ± 0.064 for TDDFT transition dipole moments and energies to DFT/MRCI level approximations, respectively. NACMEs were dominated by out-of-plane derivative components belonging to the center-most ring atoms with weaker contributions from perturbations along the transverse and longitudinal axes. Calculated theoretical spectra compared well to both experiment and literature, with fluorescence lifetimes between 7.1 and 12.5 ns, agreeing within a factor of 2 with experiment. Internal conversion (IC) rates were then calculated and were found to vary wildly between 10⁶-10¹⁶ s^-1 compared with an experimental rate of the order 10⁷ s^-1. Following further testing by mixing data sets, we found a strong dependence on the method used to obtain the Hessian. The 5 characterized data sets ranked in order of most promising are PBE0/def2-TZVP, ωB97XD/def2-TZVP, HCTH407/TZVP, PBE/TZVP, and PBE/def2-TZVP.

Evaluating fast methods for static polarizabilities on extended conjugated oligomers

Given the importance of accurate polarizability calculations to many chemical applications, coupled with the need for efficiency when calculating the properties of sets of molecules or large oligomers, we present a benchmark study examining possible calculation methods for polarizable materials. We first investigate the accuracy of the additive model used in GFN2, a highly-efficient semi-empirical tight-binding method, and the D4 dispersion model, comparing its predicted additive polarizabilities to ωB97XD results for a subset of PubChemQC and a compiled benchmark set of molecules spanning polarizabilities from approximately 3 ų to 600 ų, with some compounds in the range of approximately 1200-1400 ų. Although we find additive GFN2 polarizabilities, and thus D4, to have large errors with polarizability calculations on large conjugated oligomers, it would appear an empirical quadratic correction can largely remedy this. We also compare the accuracy of DFT polarizability calculations run using basis sets of varying size and level of augmentation, determining that a non-augmented basis set may be used for large, highly polarizable species in conjunction with a linear correction factor to achieve accuracy extremely close to that of aug-cc-pVTZ.

Refractive properties of the α-BaGeO3 crystal and their origins: a density functional theory study

Density functional theory calculations show that α-BaGeO₃ is a promising birefringent crystal used in the mid-IR region; its unique refractive characteristics are associated with the Ba–O bonds and their spatial orientations.

A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.

The process of developing new compounds and materials is increasingly driven by computational modeling and simulation, which allow us to characterize candidates before pursuing them in the laboratory. One of the non-trivial properties of interest for organic materials is their packing in the bulk, which is highly dependent on their molecular structure. By controlling the latter, we can realize materials with a desired density (as well as other target properties). Molecular dynamics simulations are a popular and reasonably accurate way to compute the bulk density of molecules, however, since these calculations are computationally intensive, they are not a practically viable option for high-throughput screening studies that assess material candidates on a massive scale. In this work, we employ machine learning to develop a data-derived prediction model that is an alternative to physics-based simulations, and we utilize it for the hyperscreening of 1.5 million small organic molecules as well as to gain insights into the relationship between structural makeup and packing density. We also use this study to analyze the learning curve of the employed neural network approach and gain empirical data on the dependence of model performance and training data size, which will inform future investigations.

Computational Molecular Modeling of Pin1 Inhibition Activity of Quinazoline, Benzophenone, and Pyrimidine Derivatives

Pin1 (peptidyl-prolyl cis-trans isomerase NIMA-interacting 1) is directly involved in cancer cell-cycle regulation because it catalyses the cis-trans isomerization of prolyl amide bonds in proteins. In this sense, a modeling evaluation of the inhibition of Pin1 using quinazoline, benzophenone, and pyrimidine derivatives was performed by using multilinear, random forest, SMOreg, and IBK regression algorithms on a dataset of 51 molecules, which was divided randomly in 78% for the training and 22% for the test set. Topological descriptors were used as independent variables and the biological activity (pIC50) as a dependent variable. The most robust individual model contained 9 features, and its predictive capability was statistically validated by the correlation coefficient for adjusting, 10-fold cross validation, test set, and bootstrapping with values of 0.910, 0.819, 0.841, and 0.803, respectively. In order to improve the prediction of the pIC50 values, the aggregation of the individual models was performed through the construction of an ensemble, and the most robust one was constructed by two individual models (LR3 and RF1) by applying the IBK algorithm, and a substantial improvement in predictive performance is reflected in the values of R2ADJ = 0.982, Q2CV = 0.962, and Q2EXT = 0.918. Mean square errors <0.165 and good fitting between calculated and experimental pIC50 values suggest a robustness on the prediction of pIC50. Regarding the docking simulation, a binding affinity between the molecules and the active site for the Pin1 inhibition into the protein (3jyj) was estimated through the calculation of the binding free energy (BE), with values in the range of −5.55 to −8.00 kcal/mol, implying a stabilizing interaction molecule receptor. The ligand interaction diagrams between the drugs and amino acid in the binding site for the three most active compounds denoted a good wrapper of these organic compounds into the protein mainly by polar amino acids.