1
|
Matin S, Allen AEA, Smith J, Lubbers N, Jadrich RB, Messerly R, Nebgen B, Li YW, Tretiak S, Barros K. Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment. J Chem Theory Comput 2024. [PMID: 38307009 DOI: 10.1021/acs.jctc.3c01051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2024]
Abstract
Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. We investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.
Collapse
Affiliation(s)
- Sakib Matin
- Department of Physics, Boston University, Boston, Massachusetts 02215, United States
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Alice E A Allen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Justin Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- NVIDIA Corp., Santa Clara, California 95051, United States
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ryan B Jadrich
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Richard Messerly
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Ying Wai Li
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| |
Collapse
|
2
|
Fedik N, Nebgen B, Lubbers N, Barros K, Kulichenko M, Li YW, Zubatyuk R, Messerly R, Isayev O, Tretiak S. Synergy of semiempirical models and machine learning in computational chemistry. J Chem Phys 2023; 159:110901. [PMID: 37712780 DOI: 10.1063/5.0151833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/11/2023] [Indexed: 09/16/2023] Open
Abstract
Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort-design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.
Collapse
Affiliation(s)
- Nikita Fedik
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Maksim Kulichenko
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Ying Wai Li
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Roman Zubatyuk
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Richard Messerly
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Olexandr Isayev
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Integrated Nanotechnologies Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| |
Collapse
|
3
|
Tkachenko NV, Tkachenko AA, Nebgen B, Tretiak S, Boldyrev AI. Neural network atomistic potentials for global energy minima search in carbon clusters. Phys Chem Chem Phys 2023; 25:21173-21182. [PMID: 37490276 DOI: 10.1039/d3cp02317f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
The global energy optimization problem is an acute and important problem in chemistry. It is crucial to know the geometry of the lowest energy isomer (global minimum, GM) of a given compound for the evaluation of its chemical and physical properties. This problem is especially relevant for atomic clusters. Due to the exponential growth of the number of local minima geometries with the increase of the number of atoms in the cluster, it is important to find a computationally efficient and reliable method to navigate the energy landscape and locate a true global minima structure. Newly developed neural network (NN) atomistic potentials offer a numerically efficient and relatively accurate approach for molecular structure optimization. An important question that needs to be answered is "Can NN potentials, trained on a given set, represent the potential energy surface (PES) of a neighboring domain?". In this work, we tested the applicability of ANI-1ccx and ANI-nr NN atomistic potentials for the global minima optimization of carbon clusters Cn (n = 3-10). We showed that with the introduction of the cluster connectivity restriction and consequent DFT or ab initio calculations, ANI-1ccx and ANI-nr can be considered as robust PES pre-samplers that can capture the GM structure even for large clusters such as C20.
Collapse
Affiliation(s)
- Nikolay V Tkachenko
- Department of Chemistry and Biochemistry, Utah State University, Logan, Utah 84322-0300, USA.
| | | | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Alexander I Boldyrev
- Department of Chemistry and Biochemistry, Utah State University, Logan, Utah 84322-0300, USA.
| |
Collapse
|
4
|
Chigaev M, Smith JS, Anaya S, Nebgen B, Bettencourt M, Barros K, Lubbers N. Lightweight and effective tensor sensitivity for atomistic neural networks. J Chem Phys 2023; 158:2889493. [PMID: 37158328 DOI: 10.1063/5.0142127] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/20/2023] [Indexed: 05/10/2023] Open
Abstract
Atomistic machine learning focuses on the creation of models that obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invariances. In many of these schemes, translation and rotation invariance are achieved by building on scalar invariants, e.g., distances between atom pairs. There is growing interest in molecular representations that work internally with higher rank rotational tensors, e.g., vector displacements between atoms, and tensor products thereof. Here, we present a framework for extending the Hierarchically Interacting Particle Neural Network (HIP-NN) with Tensor Sensitivity information (HIP-NN-TS) from each local atomic environment. Crucially, the method employs a weight tying strategy that allows direct incorporation of many-body information while adding very few model parameters. We show that HIP-NN-TS is more accurate than HIP-NN, with negligible increase in parameter count, for several datasets and network sizes. As the dataset becomes more complex, tensor sensitivities provide greater improvements to model accuracy. In particular, HIP-NN-TS achieves a record mean absolute error of 0.927 kcalmol for conformational energy variation on the challenging COMP6 benchmark, which includes a broad set of organic molecules. We also compare the computational performance of HIP-NN-TS to HIP-NN and other models in the literature.
Collapse
Affiliation(s)
- Michael Chigaev
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Justin S Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- NVIDIA, 2788 San Tomas Expy, Santa Clara, California 95051, USA
| | - Steven Anaya
- High Performance Computing Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | | | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| |
Collapse
|
5
|
Kulichenko M, Barros K, Lubbers N, Fedik N, Zhou G, Tretiak S, Nebgen B, Niklasson AMN. Semi-Empirical Shadow Molecular Dynamics: A PyTorch Implementation. J Chem Theory Comput 2023. [PMID: 37163680 DOI: 10.1021/acs.jctc.3c00234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based software PySeQM. The implementation includes finite electronic temperatures, canonical density matrix perturbation theory, and an adaptive Krylov subspace approximation for the integration of the electronic equations of motion within the XL-BOMB approach (KSA-XL-BOMD). The PyTorch implementation leverages the use of GPU and machine learning hardware accelerators for the simulations. The new XL-BOMD formulation allows studying more challenging chemical systems with charge instabilities and low electronic energy gaps. The current public release of PySeQM continues our development of modular architecture for large-scale simulations employing semi-empirical quantum-mechanical treatment. Applied to molecular dynamics, simulation of 840 carbon atoms, one integration time step executes in 4 s on a single Nvidia RTX A6000 GPU.
Collapse
Affiliation(s)
- Maksim Kulichenko
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nikita Fedik
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Guoqing Zhou
- NVIDIA Corporation, 2788 San Tomas Expy, Santa Clara, California 95051, United States
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Anders M N Niklasson
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| |
Collapse
|
6
|
Habib A, Lubbers N, Tretiak S, Nebgen B. Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles. J Phys Chem A 2023; 127:3768-3778. [PMID: 37078657 PMCID: PMC10165650 DOI: 10.1021/acs.jpca.2c08757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for realization of their full energy generating potential. Addressing this challenge requires detailed understanding of physical processes from plasmon excitation in the metal to their collection in a molecule or a semiconductor, where atomistic theoretical investigation may be particularly beneficial. Unfortunately, first-principles theoretical modeling of these processes is extremely costly, preventing a detailed analysis over a large number of potential nanostructures and limiting the analysis to systems with a few 100s of atoms. Recent advances in machine learned interatomic potentials suggest that dynamics can be accelerated with surrogate models which replace the full solution of the Schrödinger Equation. Here, we modify an existing neural network, Hierarchically Interacting Particle Neural Network (HIP-NN), to predict plasmon dynamics in Ag nanoparticles. The model takes as a minimum as three time steps of the reference real-time time-dependent density functional theory (rt-TDDFT) calculated charges as history and predicts trajectories for 5 fs in great agreement with the reference simulation. Further, we show that a multistep training approach in which the loss function includes errors from future time-step predictions can stabilize the model predictions for the entire simulated trajectory (∼25 fs). This extends the model's capability to accurately predict plasmon dynamics in large nanoparticles of up to 561 atoms, not present in the training data set. More importantly, with machine learning models on GPUs, we gain a speed-up factor of ∼103 as compared with the rt-TDDFT calculations when predicting important physical quantities such as dynamic dipole moments in Ag55 and a factor of ∼104 for extended nanoparticles that are 10 times larger. This underscores the promise of future machine learning accelerated electron/nuclear dynamics simulations for understanding fundamental properties of plasmon-driven hot carrier devices.
Collapse
Affiliation(s)
- Adela Habib
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Nicholas Lubbers
- Computer, Computational and Statistical Sciences (CCS) Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Integrated Nanotechnologies Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| |
Collapse
|
7
|
Kulichenko M, Barros K, Lubbers N, Li YW, Messerly R, Tretiak S, Smith JS, Nebgen B. Uncertainty-driven dynamics for active learning of interatomic potentials. Nat Comput Sci 2023; 3:230-239. [PMID: 38177878 PMCID: PMC10766548 DOI: 10.1038/s43588-023-00406-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/24/2023] [Indexed: 01/06/2024]
Abstract
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the data set. Here we develop a strategy to more rapidly discover configurations that meaningfully augment the training data set. The approach, uncertainty-driven dynamics for active learning (UDD-AL), modifies the potential energy surface used in molecular dynamics simulations to favor regions of configuration space for which there is large model uncertainty. The performance of UDD-AL is demonstrated for two AL tasks: sampling the conformational space of glycine and sampling the promotion of proton transfer in acetylacetone. The method is shown to efficiently explore the chemically relevant configuration space, which may be inaccessible using regular dynamical sampling at target temperature conditions.
Collapse
Affiliation(s)
- Maksim Kulichenko
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Ying Wai Li
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Richard Messerly
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Justin S Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
- Nvidia Corporation, Santa Clara, CA, USA.
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
| |
Collapse
|
8
|
Fedik N, Zubatyuk R, Kulichenko M, Lubbers N, Smith JS, Nebgen B, Messerly R, Li YW, Boldyrev AI, Barros K, Isayev O, Tretiak S. Publisher Correction: Extending machine learning beyond interatomic potentials for predicting molecular properties. Nat Rev Chem 2022. [DOI: 10.1038/s41570-022-00446-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
9
|
Fedik N, Zubatyuk R, Kulichenko M, Lubbers N, Smith JS, Nebgen B, Messerly R, Li YW, Boldyrev AI, Barros K, Isayev O, Tretiak S. Extending machine learning beyond interatomic potentials for predicting molecular properties. Nat Rev Chem 2022; 6:653-672. [PMID: 37117713 DOI: 10.1038/s41570-022-00416-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2022] [Indexed: 11/09/2022]
Abstract
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.
Collapse
|
10
|
Sifain AE, Lystrom L, Messerly RA, Smith JS, Nebgen B, Barros K, Tretiak S, Lubbers N, Gifford BJ. Predicting phosphorescence energies and inferring wavefunction localization with machine learning. Chem Sci 2021; 12:10207-10217. [PMID: 34447529 PMCID: PMC8336587 DOI: 10.1039/d1sc02136b] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 06/28/2021] [Indexed: 11/29/2022] Open
Abstract
Phosphorescence is commonly utilized for applications including light-emitting diodes and photovoltaics. Machine learning (ML) approaches trained on ab initio datasets of singlet-triplet energy gaps may expedite the discovery of phosphorescent compounds with the desired emission energies. However, we show that standard ML approaches for modeling potential energy surfaces inaccurately predict singlet-triplet energy gaps due to the failure to account for spatial localities of spin transitions. To solve this, we introduce localization layers in a neural network model that weight atomic contributions to the energy gap, thereby allowing the model to isolate the most determinative chemical environments. Trained on the singlet-triplet energy gaps of organic molecules, we apply our method to an out-of-sample test set of large phosphorescent compounds and demonstrate the substantial improvement that localization layers have on predicting their phosphorescence energies. Remarkably, the inferred localization weights have a strong relationship with the ab initio spin density of the singlet-triplet transition, and thus infer localities of the molecule that determine the spin transition, despite the fact that no direct electronic information was provided during training. The use of localization layers is expected to improve the modeling of many localized, non-extensive phenomena and could be implemented in any atom-centered neural network model.
Collapse
Affiliation(s)
- Andrew E Sifain
- Theoretical Division, Los Alamos National Laboratory Los Alamos NM USA 87545
- Center for Nonlinear Studies, Los Alamos National Laboratory Los Alamos NM USA 87545
| | - Levi Lystrom
- Theoretical Division, Los Alamos National Laboratory Los Alamos NM USA 87545
- Center for Nonlinear Studies, Los Alamos National Laboratory Los Alamos NM USA 87545
| | - Richard A Messerly
- Theoretical Division, Los Alamos National Laboratory Los Alamos NM USA 87545
| | - Justin S Smith
- Theoretical Division, Los Alamos National Laboratory Los Alamos NM USA 87545
- Center for Nonlinear Studies, Los Alamos National Laboratory Los Alamos NM USA 87545
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory Los Alamos NM USA 87545
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory Los Alamos NM USA 87545
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory Los Alamos NM USA 87545
- Center for Nonlinear Studies, Los Alamos National Laboratory Los Alamos NM USA 87545
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory Los Alamos NM USA 87545
- Center for Nonlinear Studies, Los Alamos National Laboratory Los Alamos NM USA 87545
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory Los Alamos NM USA 87545
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory Los Alamos NM USA 87545
| | - Brendan J Gifford
- Theoretical Division, Los Alamos National Laboratory Los Alamos NM USA 87545
- Center for Nonlinear Studies, Los Alamos National Laboratory Los Alamos NM USA 87545
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory Los Alamos NM USA 87545
| |
Collapse
|
11
|
Zubatiuk T, Nebgen B, Lubbers N, Smith JS, Zubatyuk R, Zhou G, Koh C, Barros K, Isayev O, Tretiak S. Machine learned Hückel theory: Interfacing physics and deep neural networks. J Chem Phys 2021; 154:244108. [PMID: 34241371 DOI: 10.1063/5.0052857] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Overall, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.
Collapse
Affiliation(s)
- Tetiana Zubatiuk
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA
| | - Nicholas Lubbers
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Justin S Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA
| | - Roman Zubatyuk
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Guoqing Zhou
- Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, USA
| | - Christopher Koh
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA
| | - Olexandr Isayev
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA
| |
Collapse
|
12
|
Kulichenko M, Smith JS, Nebgen B, Li YW, Fedik N, Boldyrev AI, Lubbers N, Barros K, Tretiak S. The Rise of Neural Networks for Materials and Chemical Dynamics. J Phys Chem Lett 2021; 12:6227-6243. [PMID: 34196559 DOI: 10.1021/acs.jpclett.1c01357] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.
Collapse
Affiliation(s)
- Maksim Kulichenko
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Department of Chemistry and Biochemistry, Utah State University, Logan, Utah 84322, United States
| | - Justin S Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ying Wai Li
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nikita Fedik
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Department of Chemistry and Biochemistry, Utah State University, Logan, Utah 84322, United States
| | - Alexander I Boldyrev
- Department of Chemistry and Biochemistry, Utah State University, Logan, Utah 84322, United States
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| |
Collapse
|
13
|
Malone W, Nebgen B, White A, Zhang Y, Song H, Bjorgaard JA, Sifain AE, Rodriguez-Hernandez B, Freixas VM, Fernandez-Alberti S, Roitberg AE, Nelson TR, Tretiak S. NEXMD Software Package for Nonadiabatic Excited State Molecular Dynamics Simulations. J Chem Theory Comput 2020; 16:5771-5783. [DOI: 10.1021/acs.jctc.0c00248] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Walter Malone
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Benjamin Nebgen
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Alexander White
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Yu Zhang
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Huajing Song
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Josiah A. Bjorgaard
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Andrew E. Sifain
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland 21005, United States
| | | | - Victor M. Freixas
- Universidad Nacional de Quilmes/CONICET, Roque Saenz Peña 352, B1876BXD Bernal, Argentina
| | | | - Adrian E. Roitberg
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Tammie R. Nelson
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Sergei Tretiak
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| |
Collapse
|
14
|
Nebgen B, Vangara R, Hombrados-Herrera MA, Kuksova S, Alexandrov B. A neural network for determination of latent dimensionality in Nonnegative Matrix Factorization. Mach Learn : Sci Technol 2020. [DOI: 10.1088/2632-2153/aba372] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
|
15
|
Nelson TR, White AJ, Bjorgaard JA, Sifain AE, Zhang Y, Nebgen B, Fernandez-Alberti S, Mozyrsky D, Roitberg AE, Tretiak S. Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials. Chem Rev 2020; 120:2215-2287. [PMID: 32040312 DOI: 10.1021/acs.chemrev.9b00447] [Citation(s) in RCA: 179] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Optically active molecular materials, such as organic conjugated polymers and biological systems, are characterized by strong coupling between electronic and vibrational degrees of freedom. Typically, simulations must go beyond the Born-Oppenheimer approximation to account for non-adiabatic coupling between excited states. Indeed, non-adiabatic dynamics is commonly associated with exciton dynamics and photophysics involving charge and energy transfer, as well as exciton dissociation and charge recombination. Understanding the photoinduced dynamics in such materials is vital to providing an accurate description of exciton formation, evolution, and decay. This interdisciplinary field has matured significantly over the past decades. Formulation of new theoretical frameworks, development of more efficient and accurate computational algorithms, and evolution of high-performance computer hardware has extended these simulations to very large molecular systems with hundreds of atoms, including numerous studies of organic semiconductors and biomolecules. In this Review, we will describe recent theoretical advances including treatment of electronic decoherence in surface-hopping methods, the role of solvent effects, trivial unavoided crossings, analysis of data based on transition densities, and efficient computational implementations of these numerical methods. We also emphasize newly developed semiclassical approaches, based on the Gaussian approximation, which retain phase and width information to account for significant decoherence and interference effects while maintaining the high efficiency of surface-hopping approaches. The above developments have been employed to successfully describe photophysics in a variety of molecular materials.
Collapse
Affiliation(s)
- Tammie R Nelson
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Alexander J White
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Josiah A Bjorgaard
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Andrew E Sifain
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States.,U.S. Army Research Laboratory , Aberdeen Proving Ground , Maryland 21005 , United States
| | - Yu Zhang
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Benjamin Nebgen
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | | | - Dmitry Mozyrsky
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Adrian E Roitberg
- Department of Chemistry , University of Florida , Gainesville , Florida 32611 , United States
| | - Sergei Tretiak
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| |
Collapse
|
16
|
Kidwell NM, Nebgen B, Slipchenko LV, Zwier TS. The effects of site asymmetry on near-degenerate state-to-state vibronic mixing in flexible bichromophores. J Chem Phys 2019; 151:084313. [PMID: 31470719 DOI: 10.1063/1.5107423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Nathanael M. Kidwell
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907-2084, USA
- Department of Chemistry, The College of William and Mary, Williamsburg, Virginia 23187-8795, USA
| | - Benjamin Nebgen
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907-2084, USA
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA
| | | | - Timothy S. Zwier
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907-2084, USA
| |
Collapse
|
17
|
Nebgen B, Lubbers N, Smith JS, Sifain AE, Lokhov A, Isayev O, Roitberg AE, Barros K, Tretiak S. Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks. J Chem Theory Comput 2018; 14:4687-4698. [PMID: 30064217 DOI: 10.1021/acs.jctc.8b00524] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The ability to accurately and efficiently compute quantum-mechanical partial atomistic charges has many practical applications, such as calculations of IR spectra, analysis of chemical bonding, and classical force field parametrization. Machine learning (ML) techniques provide a possible avenue for the efficient prediction of atomic partial charges. Modern ML advances in the prediction of molecular energies [i.e., the hierarchical interacting particle neural network (HIP-NN)] has provided the necessary model framework and architecture to predict transferable, extensible, and conformationally dynamic atomic partial charges based on reference density functional theory (DFT) simulations. Utilizing HIP-NN, we show that ML charge prediction can be highly accurate over a wide range of molecules (both small and large) across a variety of charge partitioning schemes such as the Hirshfeld, CM5, MSK, and NBO methods. To demonstrate transferability and size extensibility, we compare ML results with reference DFT calculations on the COMP6 benchmark, achieving errors of 0.004e- (elementary charge). This is remarkable since this benchmark contains two proteins that are multiple times larger than the largest molecules in the training set. An application of our atomic charge predictions on nonequilibrium geometries is the generation of IR spectra for organic molecules from dynamical trajectories on a variety of organic molecules, which show good agreement with calculated IR spectra with reference method. Critically, HIP-NN charge predictions are many orders of magnitude faster than direct DFT calculations. These combined results provide further evidence that ML (specifically HIP-NN) provides a pathway to greatly increase the range of feasible simulations while retaining quantum-level accuracy.
Collapse
Affiliation(s)
- Benjamin Nebgen
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Nicholas Lubbers
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Justin S Smith
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States.,Department of Chemistry , University of Florida , Gainesville , Florida 32611 , United States
| | - Andrew E Sifain
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States.,Department of Physics and Astronomy , University of Southern California , Los Angeles , California 90037 , United States
| | - Andrey Lokhov
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Olexandr Isayev
- UNC Eshelman School of Pharmacy University of North Carolina Chapel Hill , Chapel Hill , North Carolina 27599 , United States
| | - Adrian E Roitberg
- Department of Chemistry , University of Florida , Gainesville , Florida 32611 , United States
| | - Kipton Barros
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| | - Sergei Tretiak
- Theoretical Division , Los Alamos National Laboratory , Los Alamos , New Mexico 87545 , United States
| |
Collapse
|
18
|
Affiliation(s)
- Benjamin Nebgen
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907-2084, USA
| | | |
Collapse
|
19
|
Pillsbury NR, Kidwell NM, Nebgen B, Slipchenko LV, Douglass KO, Cable JR, Plusquellic DF, Zwier TS. Vibronic coupling in asymmetric bichromophores: Experimental investigation of diphenylmethane-d5. J Chem Phys 2014; 141:064316. [DOI: 10.1063/1.4892344] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|