1
|
Zare M, Sahsah D, Saleheen M, Behler J, Heyden A. Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces. J Chem Theory Comput 2024. [PMID: 39254514 DOI: 10.1021/acs.jctc.4c00869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Performing reliable computer simulations of elementary processes occurring at metal-water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Due to the large system sizes needed for studying processes at liquid water-metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate-metal interactions. Next, for the water-water and water-adsorbate interactions, we used established classical force fields. Finally, for the water-Ru surface interaction, for which no reliable force fields have been published, we used Behler-Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water.
Collapse
Affiliation(s)
- Mehdi Zare
- Department of Chemical Engineering, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Dia Sahsah
- Department of Chemical Engineering, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Mohammad Saleheen
- Department of Chemical Engineering, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Jörg Behler
- Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, Bochum 44780, Germany
- Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, Bochum 44780, Germany
| | - Andreas Heyden
- Department of Chemical Engineering, University of South Carolina, Columbia, South Carolina 29208, United States
| |
Collapse
|
2
|
Hou P, Tian Y, Meng X. Improving Molecular-Dynamics Simulations for Solid-Liquid Interfaces with Machine-Learning Interatomic Potentials. Chemistry 2024; 30:e202401373. [PMID: 38877181 DOI: 10.1002/chem.202401373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 06/13/2024] [Accepted: 06/14/2024] [Indexed: 06/16/2024]
Abstract
Emerging developments in artificial intelligence have opened infinite possibilities for material simulation. Depending on the powerful fitting of machine learning algorithms to first-principles data, machine learning interatomic potentials (MLIPs) can effectively balance the accuracy and efficiency problems in molecular dynamics (MD) simulations, serving as powerful tools in various complex physicochemical systems. Consequently, this brings unprecedented enthusiasm for researchers to apply such novel technology in multiple fields to revisit the major scientific problems that have remained controversial owing to the limitations of previous computational methods. Herein, we introduce the evolution of MLIPs, provide valuable application examples for solid-liquid interfaces, and present current challenges. Driven by solving multitudinous difficulties in terms of the accuracy, efficiency, and versatility of MLIPs, this booming technique, combined with molecular simulation methods, will provide an underlying and valuable understanding of interdisciplinary scientific challenges, including materials, physics, and chemistry.
Collapse
Affiliation(s)
- Pengfei Hou
- Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), College of Physics, Jilin University, Changchun, 130012, China
- Key Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun, 130012, China
| | - Yumiao Tian
- Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), College of Physics, Jilin University, Changchun, 130012, China
- Key Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun, 130012, China
| | - Xing Meng
- Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), College of Physics, Jilin University, Changchun, 130012, China
- Key Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun, 130012, China
| |
Collapse
|
3
|
Devereux M, Boittier ED, Meuwly M. Systematic improvement of empirical energy functions in the era of machine learning. J Comput Chem 2024; 45:1899-1913. [PMID: 38695412 DOI: 10.1002/jcc.27367] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/13/2024] [Accepted: 02/21/2024] [Indexed: 07/05/2024]
Abstract
The impact of targeted replacement of individual terms in empirical force fields is quantitatively assessed for pure water, dichloromethane (CH 2 Cl 2 ), and solvated K + and Cl - ions. For the electrostatic interactions, point charges (PCs) and machine learning (ML)-based minimally distributed charges (MDCM) fitted to the molecular electrostatic potential are evaluated together with electrostatics based on the Coulomb integral. The impact of explicitly including second-order terms is investigated by adding a fragment molecular orbital (FMO)-derived polarization energy to an existing force field, in this case CHARMM. It is demonstrated that anisotropic electrostatics reduce the RMSE for water (by 1.4 kcal/mol), CH 2 Cl 2 (by 0.8 kcal/mol) and for solvated Cl - clusters (by 0.4 kcal/mol). An additional polarization term can be neglected for CH 2 Cl 2 but further improves the models for pure water (by ∼ 1.0 kcal/mol) and hydrated Cl - (by 0.4 kcal/mol), and is key for solvated K + , reducing the RMSE by 2.3 kcal/mol. A 12-6 Lennard-Jones functional form performs satisfactorily with PC and MDCM electrostatics, but is not appropriate for descriptions that account for the electrostatic penetration energy. The importance of many-body contributions is assessed by comparing a strictly 2-body approach with self-consistent reference data. Two-body interactions suffice for CH 2 Cl 2 whereas water and solvated K + and Cl - ions require explicit many-body corrections. Finally, a many-body-corrected dimer potential energy surface exceeds the accuracy attained using a conventional empirical force field, potentially reaching that of an FMO calculation. The present work systematically quantifies which terms improve the performance of an existing force field and what reference data to use for parametrizing these terms in a tractable fashion for ML fitting of pure and heterogeneous systems.
Collapse
Affiliation(s)
- Mike Devereux
- Department of Chemistry, University of Basel, Basel, Switzerland
| | - Eric D Boittier
- Department of Chemistry, University of Basel, Basel, Switzerland
| | - Markus Meuwly
- Department of Chemistry, University of Basel, Basel, Switzerland
| |
Collapse
|
4
|
Limmer DT, Götz AW, Bertram TH, Nathanson GM. Molecular Insights into Chemical Reactions at Aqueous Aerosol Interfaces. Annu Rev Phys Chem 2024; 75:111-135. [PMID: 38360527 DOI: 10.1146/annurev-physchem-083122-121620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Atmospheric aerosols facilitate reactions between ambient gases and dissolved species. Here, we review our efforts to interrogate the uptake of these gases and the mechanisms of their reactions both theoretically and experimentally. We highlight the fascinating behavior of N2O5 in solutions ranging from pure water to complex mixtures, chosen because its aerosol-mediated reactions significantly impact global ozone, hydroxyl, and methane concentrations. As a hydrophobic, weakly soluble, and highly reactive species, N2O5 is a sensitive probe of the chemical and physical properties of aerosol interfaces. We employ contemporary theory to disentangle the fate of N2O5 as it approaches pure and salty water, starting with adsorption and ending with hydrolysis to HNO3, chlorination to ClNO2, or evaporation. Flow reactor and gas-liquid scattering experiments probe even greater complexity as added ions, organic molecules, and surfactants alter the interfacial composition and reaction rates. Together, we reveal a new perspective on multiphase chemistry in the atmosphere.
Collapse
Affiliation(s)
- David T Limmer
- Department of Chemistry, University of California, Berkeley, California, USA;
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- Kavli Energy NanoScience Institute, Berkeley, California, USA
- Chemical Science Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California, USA;
| | - Timothy H Bertram
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; ,
| | - Gilbert M Nathanson
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; ,
| |
Collapse
|
5
|
Duignan TT. The Potential of Neural Network Potentials. ACS PHYSICAL CHEMISTRY AU 2024; 4:232-241. [PMID: 38800721 PMCID: PMC11117678 DOI: 10.1021/acsphyschemau.4c00004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 05/29/2024]
Abstract
In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by the combination of recent advances in quantum chemistry and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are a breakthrough new tool that are already enabling us to simulate systems at the molecular scale with unprecedented accuracy and speed, relying on nothing but fundamental physical laws. The continued development of this approach will realize Paul Dirac's 80-year-old vision of using quantum mechanics to unify physics with chemistry and providing invaluable tools for understanding materials science, biology, earth sciences, and beyond. The era of highly accurate and efficient first-principles molecular simulations will provide a wealth of training data that can be used to build automated computational methodologies, using tools such as diffusion models, for the design and optimization of systems at the molecular scale. Large language models (LLMs) will also evolve into increasingly indispensable tools for literature review, coding, idea generation, and scientific writing.
Collapse
|
6
|
Zhai Y, Rashmi R, Palos E, Paesani F. Many-body interactions and deep neural network potentials for water. J Chem Phys 2024; 160:144501. [PMID: 38587225 DOI: 10.1063/5.0203682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 03/23/2024] [Indexed: 04/09/2024] Open
Abstract
We present a detailed assessment of deep neural network potentials developed within the Deep Potential Molecular Dynamics (DeePMD) framework and trained on the MB-pol data-driven many-body potential energy function. Specific focus is directed at the ability of DeePMD-based potentials to correctly reproduce the accuracy of MB-pol across various water systems. Analyses of bulk and interfacial properties as well as many-body interactions characteristic of water elucidate inherent limitations in the transferability and predictive accuracy of DeePMD-based potentials. These limitations can be traced back to an incomplete implementation of the "nearsightedness of electronic matter" principle, which may be common throughout machine learning potentials that do not include a proper representation of self-consistently determined long-range electric fields. These findings provide further support for the "short-blanket dilemma" faced by DeePMD-based potentials, highlighting the challenges in achieving a balance between computational efficiency and a rigorous, physics-based representation of the properties of water. Finally, we believe that our study contributes to the ongoing discourse on the development and application of machine learning models in simulating water systems, offering insights that could guide future improvements in the field.
Collapse
Affiliation(s)
- Yaoguang Zhai
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, USA
| | - Richa Rashmi
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Etienne Palos
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
- Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, USA
- Halicioğlu Data Science Institute, University of California San Diego, La Jolla, California 92093, USA
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, USA
| |
Collapse
|
7
|
Litman Y, Chiang KY, Seki T, Nagata Y, Bonn M. Surface stratification determines the interfacial water structure of simple electrolyte solutions. Nat Chem 2024; 16:644-650. [PMID: 38225269 PMCID: PMC10997511 DOI: 10.1038/s41557-023-01416-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 12/07/2023] [Indexed: 01/17/2024]
Abstract
The distribution of ions at the air/water interface plays a decisive role in many natural processes. Several studies have reported that larger ions tend to be surface-active, implying ions are located on top of the water surface, thereby inducing electric fields that determine the interfacial water structure. Here we challenge this view by combining surface-specific heterodyne-detected vibrational sum-frequency generation with neural network-assisted ab initio molecular dynamics simulations. Our results show that ions in typical electrolyte solutions are, in fact, located in a subsurface region, leading to a stratification of such interfaces into two distinctive water layers. The outermost surface is ion-depleted, and the subsurface layer is ion-enriched. This surface stratification is a key element in explaining the ion-induced water reorganization at the outermost air/water interface.
Collapse
Affiliation(s)
- Yair Litman
- Max Planck Institute for Polymer Research, Mainz, Germany.
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
| | | | - Takakazu Seki
- Max Planck Institute for Polymer Research, Mainz, Germany
| | - Yuki Nagata
- Max Planck Institute for Polymer Research, Mainz, Germany
| | - Mischa Bonn
- Max Planck Institute for Polymer Research, Mainz, Germany.
| |
Collapse
|
8
|
Wen M, Chang X, Xu Y, Chen D, Chu Q. Determining the mechanical and decomposition properties of high energetic materials (α-RDX, β-HMX, and ε-CL-20) using a neural network potential. Phys Chem Chem Phys 2024; 26:9984-9997. [PMID: 38477375 DOI: 10.1039/d4cp00017j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Molecular simulations of high energetic materials (HEMs) are limited by efficiency and accuracy. Recently, neural network potential (NNP) models have achieved molecular simulations of millions of atoms while maintaining the accuracy of density functional theory (DFT) levels. Herein, an NNP model covering typical HEMs containing C, H, N, and O elements is developed. The mechanical and decomposition properties of 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), hexahydro-1,3,5-trinitro-1,3,5-triazine (HMX), and 2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (CL-20) are determined by employing the molecular dynamics (MD) simulations based on the NNP model. The calculated results show that the mechanical properties of α-RDX, β-HMX, and ε-CL-20 agree with previous experiments and theoretical results, including cell parameters, equations of state, and elastic constants. In the thermal decomposition simulations, it is also found that the initial decomposition reactions of the three crystals are N-NO2 homolysis, corresponding radical intermediates formation, and NO2-induced reactions. This decomposition trajectory is mainly divided into two stages separating from the peak of NO2: pyrolysis and oxidation. Overall, the NNP model for C/H/N/O elements in this work is an alternative reactive force field for RDX, HMX, and CL-20 HEMs, and it opens up new potential for future kinetic study of nitramine explosives.
Collapse
Affiliation(s)
- Mingjie Wen
- State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, P. R. China.
| | - Xiaoya Chang
- State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, P. R. China.
| | - Yabei Xu
- State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, P. R. China.
| | - Dongping Chen
- State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, P. R. China.
| | - Qingzhao Chu
- State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, P. R. China.
| |
Collapse
|
9
|
Kapil V, Kovács DP, Csányi G, Michaelides A. First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects. Faraday Discuss 2024; 249:50-68. [PMID: 37799072 PMCID: PMC10845015 DOI: 10.1039/d3fd00113j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/18/2023] [Indexed: 10/07/2023]
Abstract
Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena. However, extracting structural and dynamical information from vibrational spectra is a challenge that requires first-principles simulations, including non-Condon and quantum nuclear effects. We address this challenge by developing a machine-learning enhanced first-principles framework to speed up predictive modelling of infrared, Raman, and sum-frequency generation spectra. Our approach uses machine learning potentials that encode quantum nuclear effects to generate quantum trajectories using simple molecular dynamics efficiently. In addition, we reformulate bulk and interfacial selection rules to express them unambiguously in terms of the derivatives of polarisation and polarisabilities of the whole system and predict these derivatives efficiently using fully-differentiable machine learning models of dielectric response tensors. We demonstrate our framework's performance by predicting the IR, Raman, and sum-frequency generation spectra of liquid water, ice and the water-air interface by achieving near quantitative agreement with experiments at nearly the same computational efficiency as pure classical methods. Finally, to aid the experimental discovery of new phases of nanoconfined water, we predict the temperature-dependent vibrational spectra of monolayer water across the solid-hexatic-liquid phases transition.
Collapse
Affiliation(s)
- Venkat Kapil
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
| | | | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, UK
| | - Angelos Michaelides
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
| |
Collapse
|
10
|
Dasgupta S, Palos E, Pan Y, Paesani F. Balance between Physical Interpretability and Energetic Predictability in Widely Used Dispersion-Corrected Density Functionals. J Chem Theory Comput 2024; 20:49-67. [PMID: 38150541 DOI: 10.1021/acs.jctc.3c00903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
We assess the performance of different dispersion models for several popular density functionals across a diverse set of noncovalent systems, ranging from the benzene dimer to molecular crystals. By analyzing the interaction energies and their individual components, we demonstrate that there exists variability across different systems for empirical dispersion models, which are calibrated for reproducing the interaction energies of specific systems. Thus, parameter fitting may undermine the underlying physics, as dispersion models rely on error compensation among the different components of the interaction energy. Energy decomposition analyses reveal that, the accuracy of revPBE-D3 for some aqueous systems originates from significant compensation between dispersion and charge transfer energies. However, revPBE-D3 is less accurate in describing systems where error compensation is incomplete, such as the benzene dimer. Such cases highlight the propensity for unpredictable behavior in various dispersion-corrected density functionals across a wide range of molecular systems, akin to the behavior of force fields. On the other hand, we find that SCAN-rVV10, a targeted-dispersion approach, affords significant reductions in errors associated with the lattice energies of molecular crystals, while it has limited accuracy in reproducing structural properties. Given the ubiquitous nature of noncovalent interactions and the key role of density functional theory in computational sciences, the future development of dispersion models should prioritize the faithful description of the dispersion energy, a shift that promises greater accuracy in capturing the underlying physics across diverse molecular and extended systems.
Collapse
Affiliation(s)
- Saswata Dasgupta
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Etienne Palos
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Yuanhui Pan
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
- Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, United States
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, United States
| |
Collapse
|
11
|
Shayestehpour O, Zahn S. Efficient Molecular Dynamics Simulations of Deep Eutectic Solvents with First-Principles Accuracy Using Machine Learning Interatomic Potentials. J Chem Theory Comput 2023; 19:8732-8742. [PMID: 37972596 PMCID: PMC10720642 DOI: 10.1021/acs.jctc.3c00944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023]
Abstract
In recent years, deep eutectic solvents emerged as highly tunable and ecofriendly alternatives to common organic solvents and liquid electrolytes. In the present work, the ability of machine learning (ML) interatomic potentials for molecular dynamics (MD) simulations of these liquids is explored, showcasing a trained neural network potential for a 1:2 ratio mixture of choline chloride and urea (reline). Using the ML potentials trained on density functional theory data, MD simulations for large systems of thousands of atoms and nanosecond-long time scales are feasible at a fraction of the computational cost of the target first-principles simulations. The obtained structural and dynamical properties of reline from MD simulations using our machine learning models are in good agreement with the first-principles MD simulations and experimental results. Running a single MD simulation is highlighted as a general shortcoming of typical first-principles studies if the dynamic properties are investigated. Furthermore, velocity cross-correlation functions are employed to study the collective dynamics of the molecular components in reline.
Collapse
Affiliation(s)
| | - Stefan Zahn
- Leibniz Institute of Surface Engineering, 04318 Leipzig, Germany
| |
Collapse
|
12
|
Huguenin-Dumittan K, Loche P, Haoran N, Ceriotti M. Physics-Inspired Equivariant Descriptors of Nonbonded Interactions. J Phys Chem Lett 2023; 14:9612-9618. [PMID: 37862712 PMCID: PMC10626632 DOI: 10.1021/acs.jpclett.3c02375] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/13/2023] [Indexed: 10/22/2023]
Abstract
One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrostatic or dispersion interactions. We present an extension of the long distance equivariant (LODE) framework that can handle diverse LR interactions in a consistent way and seamlessly integrates with preexisting methods by building new sets of atom centered features. We provide a direct physical interpretation of these using the multipole expansion, which allows for simpler and more efficient implementations. The framework is applied to simple toy systems as proof of concept and a heterogeneous set of molecular dimers to push the method to its limits. By generalizing LODE to arbitrary asymptotic behaviors, we provide a coherent approach to treat arbitrary two- and many-body nonbonded interactions in the data-driven modeling of matter.
Collapse
Affiliation(s)
- Kevin
K. Huguenin-Dumittan
- Laboratory
of Computational Science and Modeling, IMX,
École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Philip Loche
- Laboratory
of Computational Science and Modeling, IMX,
École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Ni Haoran
- Laboratory
of Computational Science and Modeling, IMX,
École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Michele Ceriotti
- Laboratory
of Computational Science and Modeling, IMX,
École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| |
Collapse
|
13
|
Litman Y, Lan J, Nagata Y, Wilkins DM. Fully First-Principles Surface Spectroscopy with Machine Learning. J Phys Chem Lett 2023; 14:8175-8182. [PMID: 37671886 PMCID: PMC10510433 DOI: 10.1021/acs.jpclett.3c01989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/29/2023] [Indexed: 09/07/2023]
Abstract
Our current understanding of the structure and dynamics of aqueous interfaces at the molecular level has grown substantially due to the continuous development of surface-specific spectroscopies, such as vibrational sum-frequency generation (VSFG). As in other vibrational spectroscopies, we must turn to atomistic simulations to extract all of the information encoded in the VSFG spectra. The high computational cost associated with existing methods means that they have limitations in representing systems with complex electronic structure or in achieving statistical convergence. In this work, we combine high-dimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression to overcome these constraints. We show that it is possible to model VSFG signals with fully ab initio accuracy using machine learning and illustrate the versatility of our approach on the water/air interface. Our strategy allows us to identify the main sources of theoretical inaccuracy and establish a clear pathway toward the modeling of surface-sensitive spectroscopy of complex interfaces.
Collapse
Affiliation(s)
- Yair Litman
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
- Max
Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Jinggang Lan
- Department
of Chemistry, New York University, New York, New York 10003, United States
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
| | - Yuki Nagata
- Max
Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - David M. Wilkins
- Centre
for Quantum Materials and Technologies School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdom
| |
Collapse
|
14
|
Zeng J, Zhang D, Lu D, Mo P, Li Z, Chen Y, Rynik M, Huang L, Li Z, Shi S, Wang Y, Ye H, Tuo P, Yang J, Ding Y, Li Y, Tisi D, Zeng Q, Bao H, Xia Y, Huang J, Muraoka K, Wang Y, Chang J, Yuan F, Bore SL, Cai C, Lin Y, Wang B, Xu J, Zhu JX, Luo C, Zhang Y, Goodall REA, Liang W, Singh AK, Yao S, Zhang J, Wentzcovitch R, Han J, Liu J, Jia W, York DM, E W, Car R, Zhang L, Wang H. DeePMD-kit v2: A software package for deep potential models. J Chem Phys 2023; 159:054801. [PMID: 37526163 PMCID: PMC10445636 DOI: 10.1063/5.0155600] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/03/2023] [Indexed: 08/02/2023] Open
Abstract
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.
Collapse
Affiliation(s)
- Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | | | - Denghui Lu
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, People’s Republic of China
| | - Pinghui Mo
- College of Electrical and Information Engineering, Hunan University, Changsha, People’s Republic of China
| | - Zeyu Li
- Yuanpei College, Peking University, Beijing 100871, People’s Republic of China
| | - Yixiao Chen
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08540, USA
| | - Marián Rynik
- Department of Experimental Physics, Comenius University, Mlynská Dolina F2, 842 48 Bratislava, Slovakia
| | - Li’ang Huang
- Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
| | | | - Shaochen Shi
- ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
| | | | - Haotian Ye
- Yuanpei College, Peking University, Beijing 100871, People’s Republic of China
| | - Ping Tuo
- AI for Science Institute, Beijing 100080, People’s Republic of China
| | - Jiabin Yang
- Baidu, Inc., Beijing, People’s Republic of China
| | | | - Yifan Li
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | | | - Qiyu Zeng
- Department of Physics, National University of Defense Technology, Changsha, Hunan 410073, People’s Republic of China
| | | | - Yu Xia
- ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
| | | | - Koki Muraoka
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Yibo Wang
- DP Technology, Beijing 100080, People’s Republic of China
| | | | - Fengbo Yuan
- DP Technology, Beijing 100080, People’s Republic of China
| | - Sigbjørn Løland Bore
- Hylleraas Centre for Quantum Molecular Sciences and Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, 0315 Oslo, Norway
| | | | - Yinnian Lin
- Wangxuan Institute of Computer Technology, Peking University, Beijing 100871, People’s Republic of China
| | - Bo Wang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry and Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, People’s Republic of China
| | - Jiayan Xu
- School of Chemistry and Chemical Engineering, Queen’s University Belfast, Belfast BT9 5AG, United Kingdom
| | - Jia-Xin Zhu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People’s Republic of China
| | - Chenxing Luo
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
| | - Yuzhi Zhang
- DP Technology, Beijing 100080, People’s Republic of China
| | | | - Wenshuo Liang
- DP Technology, Beijing 100080, People’s Republic of China
| | - Anurag Kumar Singh
- Department of Data Science, Indian Institute of Technology, Palakkad, Kerala, India
| | - Sikai Yao
- DP Technology, Beijing 100080, People’s Republic of China
| | - Jingchao Zhang
- NVIDIA AI Technology Center (NVAITC), Santa Clara, California 95051, USA
| | | | - Jiequn Han
- Center for Computational Mathematics, Flatiron Institute, New York, New York 10010, USA
| | - Jie Liu
- College of Electrical and Information Engineering, Hunan University, Changsha, People’s Republic of China
| | | | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | | | - Roberto Car
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | | | - Han Wang
- Author to whom correspondence should be addressed:
| |
Collapse
|
15
|
Kabylda A, Vassilev-Galindo V, Chmiela S, Poltavsky I, Tkatchenko A. Efficient interatomic descriptors for accurate machine learning force fields of extended molecules. Nat Commun 2023; 14:3562. [PMID: 37322039 PMCID: PMC10272221 DOI: 10.1038/s41467-023-39214-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/17/2023] [Indexed: 06/17/2023] Open
Abstract
Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF. We found that non-local features (atoms separated by as far as 15 Å in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5 Å). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size.
Collapse
Affiliation(s)
- Adil Kabylda
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
| | - Valentin Vassilev-Galindo
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
| | - Stefan Chmiela
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, 10587, Berlin, Germany
| | - Igor Poltavsky
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg.
| |
Collapse
|
16
|
Sanchez-Burgos I, Muniz MC, Espinosa JR, Panagiotopoulos AZ. A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water. J Chem Phys 2023; 158:2889532. [PMID: 37158636 DOI: 10.1063/5.0144500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/30/2023] [Indexed: 05/10/2023] Open
Abstract
Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here, we utilize the Deep Potential methodology-a machine learning approach-to study this ubiquitous phase transition, starting from the phase diagram in the liquid-vapor coexistence regime. The machine learning model is trained on ab initio energies and forces based on the SCAN density functional, which has been previously shown to reproduce solid phases and other properties of water. Here, we compute the surface tension, saturation pressure, and enthalpy of vaporization for a range of temperatures spanning from 300 to 600 K and evaluate the Deep Potential model performance against experimental results and the semiempirical TIP4P/2005 classical model. Moreover, by employing the seeding technique, we evaluate the free energy barrier and nucleation rate at negative pressures for the isotherm of 296.4 K. We find that the nucleation rates obtained from the Deep Potential model deviate from those computed for the TIP4P/2005 water model due to an underestimation in the surface tension from the Deep Potential model. From analysis of the seeding simulations, we also evaluate the Tolman length for the Deep Potential water model, which is (0.091 ± 0.008) nm at 296.4 K. Finally, we identify that water molecules display a preferential orientation in the liquid-vapor interface, in which H atoms tend to point toward the vapor phase to maximize the enthalpic gain of interfacial molecules. We find that this behavior is more pronounced for planar interfaces than for the curved interfaces in bubbles. This work represents the first application of Deep Potential models to the study of liquid-vapor coexistence and water cavitation.
Collapse
Affiliation(s)
- Ignacio Sanchez-Burgos
- Maxwell Centre, Cavendish Laboratory, Department of Physics, University of Cambridge, J J Thomson Avenue,Cambridge CB3 0HE, United Kingdom
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Maria Carolina Muniz
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Jorge R Espinosa
- Maxwell Centre, Cavendish Laboratory, Department of Physics, University of Cambridge, J J Thomson Avenue,Cambridge CB3 0HE, United Kingdom
- Departamento de Química Fisica, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | | |
Collapse
|
17
|
de Las Heras D, Zimmermann T, Sammüller F, Hermann S, Schmidt M. Perspective: How to overcome dynamical density functional theory. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2023; 35:271501. [PMID: 37023762 DOI: 10.1088/1361-648x/accb33] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
We argue in favour of developing a comprehensive dynamical theory for rationalizing, predicting, designing, and machine learning nonequilibrium phenomena that occur in soft matter. To give guidance for navigating the theoretical and practical challenges that lie ahead, we discuss and exemplify the limitations of dynamical density functional theory (DDFT). Instead of the implied adiabatic sequence of equilibrium states that this approach provides as a makeshift for the true time evolution, we posit that the pending theoretical tasks lie in developing a systematic understanding of the dynamical functional relationships that govern the genuine nonequilibrium physics. While static density functional theory gives a comprehensive account of the equilibrium properties of many-body systems, we argue that power functional theory is the only present contender to shed similar insights into nonequilibrium dynamics, including the recognition and implementation of exact sum rules that result from the Noether theorem. As a demonstration of the power functional point of view, we consider an idealized steady sedimentation flow of the three-dimensional Lennard-Jones fluid and machine-learn the kinematic map from the mean motion to the internal force field. The trained model is capable of both predicting and designing the steady state dynamics universally for various target density modulations. This demonstrates the significant potential of using such techniques in nonequilibrium many-body physics and overcomes both the conceptual constraints of DDFT as well as the limited availability of its analytical functional approximations.
Collapse
Affiliation(s)
- Daniel de Las Heras
- Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, D-95447 Bayreuth, Germany
| | - Toni Zimmermann
- Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, D-95447 Bayreuth, Germany
| | - Florian Sammüller
- Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, D-95447 Bayreuth, Germany
| | - Sophie Hermann
- Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, D-95447 Bayreuth, Germany
| | - Matthias Schmidt
- Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, D-95447 Bayreuth, Germany
| |
Collapse
|
18
|
Anstine D, Isayev O. Machine Learning Interatomic Potentials and Long-Range Physics. J Phys Chem A 2023; 127:2417-2431. [PMID: 36802360 PMCID: PMC10041642 DOI: 10.1021/acs.jpca.2c06778] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/03/2023] [Indexed: 02/23/2023]
Abstract
Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted in short-range models that can infer interaction energies with near ab initio accuracy and orders of magnitude reduced computational cost. For many atom systems, including macromolecules, biomolecules, and condensed matter, model accuracy can become reliant on the description of short- and long-range physical interactions. The latter terms can be difficult to incorporate into an MLIP framework. Recent research has produced numerous models with considerations for nonlocal electrostatic and dispersion interactions, leading to a large range of applications that can be addressed using MLIPs. In light of this, we present a Perspective focused on key methodologies and models being used where the presence of nonlocal physics and chemistry are crucial for describing system properties. The strategies covered include MLIPs augmented with dispersion corrections, electrostatics calculated with charges predicted from atomic environment descriptors, the use of self-consistency and message passing iterations to propagated nonlocal system information, and charges obtained via equilibration schemes. We aim to provide a pointed discussion to support the development of machine learning-based interatomic potentials for systems where contributions from only nearsighted terms are deficient.
Collapse
Affiliation(s)
- Dylan
M. Anstine
- Department of Chemistry,
Mellon College of Science, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Olexandr Isayev
- Department of Chemistry,
Mellon College of Science, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| |
Collapse
|
19
|
Gao A, Remsing RC, Weeks JD. Local Molecular Field Theory for Coulomb Interactions in Aqueous Solutions. J Phys Chem B 2023; 127:809-821. [PMID: 36669139 DOI: 10.1021/acs.jpcb.2c06988] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Coulomb interactions play a crucial role in a wide array of processes in aqueous solutions but present conceptual and computational challenges to both theory and simulations. We review recent developments in an approach addressing these challenges─local molecular field (LMF) theory. LMF theory exploits an exact and physically suggestive separation of intermolecular Coulomb interactions into strong short-range and uniformly slowly varying long-range components. This allows us to accurately determine the averaged effects of the long-range components on the short-range structure using effective single particle fields and analytical corrections, greatly reducing the need for complex lattice summation techniques used in most standard approaches. The simplest use of these ideas in aqueous solutions leads to the short solvent (SS) model, where both solvent-solvent and solute-solvent Coulomb interactions have only short-range components. Here we use the SS model to give a simple description of pairing of nucleobases and biologically relevant ions in water.
Collapse
Affiliation(s)
- Ang Gao
- Department of Physics, Beijing University of Posts and Telecommunications, Beijing, China 100876
| | - Richard C Remsing
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - John D Weeks
- Institute for Physical Science and Technology and Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, United States
| |
Collapse
|
20
|
Chmiela S, Vassilev-Galindo V, Unke OT, Kabylda A, Sauceda HE, Tkatchenko A, Müller KR. Accurate global machine learning force fields for molecules with hundreds of atoms. SCIENCE ADVANCES 2023; 9:eadf0873. [PMID: 36630510 PMCID: PMC9833674 DOI: 10.1126/sciadv.adf0873] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/28/2022] [Indexed: 05/25/2023]
Abstract
Global machine learning force fields, with the capacity to capture collective interactions in molecular systems, now scale up to a few dozen atoms due to considerable growth of model complexity with system size. For larger molecules, locality assumptions are introduced, with the consequence that nonlocal interactions are not described. Here, we develop an exact iterative approach to train global symmetric gradient domain machine learning (sGDML) force fields (FFs) for several hundred atoms, without resorting to any potentially uncontrolled approximations. All atomic degrees of freedom remain correlated in the global sGDML FF, allowing the accurate description of complex molecules and materials that present phenomena with far-reaching characteristic correlation lengths. We assess the accuracy and efficiency of sGDML on a newly developed MD22 benchmark dataset containing molecules from 42 to 370 atoms. The robustness of our approach is demonstrated in nanosecond path-integral molecular dynamics simulations for supramolecular complexes in the MD22 dataset.
Collapse
Affiliation(s)
- Stefan Chmiela
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- Berlin Institute for the Foundations of Learning and Data – BIFOLD, Germany
| | - Valentin Vassilev-Galindo
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Oliver T. Unke
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- Google Research, Brain Team, Berlin, Germany
| | - Adil Kabylda
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Huziel E. Sauceda
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- Berlin Institute for the Foundations of Learning and Data – BIFOLD, Germany
- Departamento de Materia Condensada, Instituto de Física, Universidad Nacional Autónoma de México, Cd. de México C.P. 04510, Mexico
- BASLEARN - TU Berlin/BASF Joint Lab for Machine Learning, Technische Universität Berlin, 10587 Berlin, Germany
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- Berlin Institute for the Foundations of Learning and Data – BIFOLD, Germany
- Google Research, Brain Team, Berlin, Germany
- Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany
- Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea
| |
Collapse
|
21
|
Hao H, Ruiz Pestana L, Qian J, Liu M, Xu Q, Head‐Gordon T. Chemical transformations and transport phenomena at interfaces. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Hongxia Hao
- Kenneth S. Pitzer Theory Center and Department of Chemistry University of California Berkeley California USA
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
| | - Luis Ruiz Pestana
- Department of Civil and Architectural Engineering University of Miami Coral Gables Florida USA
| | - Jin Qian
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
| | - Meili Liu
- Department of Civil and Architectural Engineering University of Miami Coral Gables Florida USA
| | - Qiang Xu
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
| | - Teresa Head‐Gordon
- Kenneth S. Pitzer Theory Center and Department of Chemistry University of California Berkeley California USA
- Chemical Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA
- Department of Bioengineering and Chemical and Biomolecular Engineering University of California Berkeley California USA
| |
Collapse
|
22
|
Zhang L, Wang H, Muniz MC, Panagiotopoulos AZ, Car R, E W. A deep potential model with long-range electrostatic interactions. J Chem Phys 2022; 156:124107. [DOI: 10.1063/5.0083669] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make molecular simulations with the accuracy of quantum mechanical density functional theory possible at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation, we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei + core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the DP long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples: the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal.
Collapse
Affiliation(s)
| | - Han Wang
- Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, People’s Republic of China
| | - Maria Carolina Muniz
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | | | - Roberto Car
- Department of Chemistry, Department of Physics, Program in Applied and Computational Mathematics, Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, USA
| | - Weinan E
- School of Mathematical Sciences, Peking University, Beijing 100871, People’s Republic of China
- AI for Science Institute, Beijing, People’s Republic of China
- Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA
| |
Collapse
|
23
|
Gao A, Remsing RC. Self-consistent determination of long-range electrostatics in neural network potentials. Nat Commun 2022; 13:1572. [PMID: 35322046 PMCID: PMC8943018 DOI: 10.1038/s41467-022-29243-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/07/2022] [Indexed: 12/19/2022] Open
Abstract
Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. Neural networks can model interactions with the accuracy of quantum mechanics-based calculations, but with a fraction of the cost, enabling simulations of large systems over long timescales. However, implicit in the construction of neural network potentials is an assumption of locality, wherein atomic arrangements on the nanometer-scale are used to learn interatomic interactions. Because of this assumption, the resulting neural network models cannot describe long-range interactions that play critical roles in dielectric screening and chemical reactivity. Here, we address this issue by introducing the self-consistent field neural network - a general approach for learning the long-range response of molecular systems in neural network potentials that relies on a physically meaningful separation of the interatomic interactions - and demonstrate its utility by modeling liquid water with and without applied fields.
Collapse
Affiliation(s)
- Ang Gao
- Department of Physics, Beijing University of Posts and Telecommunications, 100876, Beijing, China.
| | - Richard C Remsing
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, 08854, USA.
| |
Collapse
|
24
|
Cruzeiro VWD, Galib M, Limmer DT, Götz AW. Uptake of N 2O 5 by aqueous aerosol unveiled using chemically accurate many-body potentials. Nat Commun 2022; 13:1266. [PMID: 35273144 PMCID: PMC8913772 DOI: 10.1038/s41467-022-28697-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 01/28/2022] [Indexed: 11/24/2022] Open
Abstract
The reactive uptake of N2O5 to aqueous aerosol is a major loss channel for nitrogen oxides in the troposphere. Despite its importance, a quantitative picture of the uptake mechanism is missing. Here we use molecular dynamics simulations with a data-driven many-body model of coupled-cluster accuracy to quantify thermodynamics and kinetics of solvation and adsorption of N2O5 in water. The free energy profile highlights that N2O5 is selectively adsorbed to the liquid-vapor interface and weakly solvated. Accommodation into bulk water occurs slowly, competing with evaporation upon adsorption from gas phase. Leveraging the quantitative accuracy of the model, we parameterize and solve a reaction-diffusion equation to determine hydrolysis rates consistent with experimental observations. We find a short reaction-diffusion length, indicating that the uptake is dominated by interfacial features. The parameters deduced here, including solubility, accommodation coefficient, and hydrolysis rate, afford a foundation for which to consider the reactive loss of N2O5 in more complex solutions.
Collapse
Affiliation(s)
- Vinícius Wilian D Cruzeiro
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Mirza Galib
- Department of Chemistry, University of California, Berkeley, CA, USA
| | - David T Limmer
- Department of Chemistry, University of California, Berkeley, CA, USA.
- Kavli Energy NanoScience Institute, Berkeley, CA, USA.
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Chemical Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, 92093, USA.
| |
Collapse
|
25
|
Montes-Campos H, Carrete J, Bichelmaier S, Varela LM, Madsen GKH. A Differentiable Neural-Network Force Field for Ionic Liquids. J Chem Inf Model 2022; 62:88-101. [PMID: 34941253 PMCID: PMC8757435 DOI: 10.1021/acs.jcim.1c01380] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Indexed: 01/11/2023]
Abstract
We present NeuralIL, a model for the potential energy of an ionic liquid that accurately reproduces first-principles results with orders-of-magnitude savings in computational cost. Built on the basis of a multilayer perceptron and spherical Bessel descriptors of the atomic environments, NeuralIL is implemented in such a way as to be fully automatically differentiable. It can thus be trained on ab initio forces instead of just energies, to make the most out of the available data, and can efficiently predict arbitrary derivatives of the potential energy. Using ethylammonium nitrate as the test system, we obtain out-of-sample accuracies better than 2 meV atom-1 (<0.05 kcal mol-1) in the energies and 70 meV Å-1 in the forces. We show that encoding the element-specific density in the spherical Bessel descriptors is key to achieving this. Harnessing the information provided by the forces drastically reduces the amount of atomic configurations required to train a neural network force field based on atom-centered descriptors. We choose the Swish-1 activation function and discuss the role of this choice in keeping the neural network differentiable. Furthermore, the possibility of training on small data sets allows for an ensemble-learning approach to the detection of extrapolation. Finally, we find that a separate treatment of long-range interactions is not required to achieve a high-quality representation of the potential energy surface of these dense ionic systems.
Collapse
Affiliation(s)
- Hadrián Montes-Campos
- Grupo
de Nanomateriais, Fotónica e Materia Branda, Departamento de
Física de Partículas, Universidade de Santiago de Compostela, Campus Vida s/n E-15782 Santiago de Compostela, Spain
| | - Jesús Carrete
- Institute
of Materials Chemistry, TU Wien, 1060 Vienna, Austria
| | | | - Luis M. Varela
- Grupo
de Nanomateriais, Fotónica e Materia Branda, Departamento de
Física de Partículas, Universidade de Santiago de Compostela, Campus Vida s/n E-15782 Santiago de Compostela, Spain
| | | |
Collapse
|