Machine Learning Force Fields

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 10142 - 10186

Published: March 11, 2021

In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out reach due to complexity traditional electronic-structure methods. One most promising applications is construction ML-based force fields (FFs), with aim narrow gap between accuracy ab initio methods and efficiency classical FFs. The key idea learn statistical relation chemical structure potential energy without relying on a preconceived notion fixed bonds or knowledge about relevant interactions. Such universal ML approximations are principle only limited by quality quantity reference data used train them. This review gives an overview ML-FFs insights that can be obtained from core concepts underlying described detail, step-by-step guide for constructing testing them scratch given. text concludes discussion challenges remain overcome next generation ML-FFs.

Language: Английский

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Gaussian Process Regression for Materials and Molecules

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 10073 - 10141

Published: Aug. 16, 2021

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on atomistic properties: particular, construction interatomic potentials, or force fields, Approximation Potential (GAP) framework; beyond this, we also discuss fitting arbitrary scalar, vectorial, tensorial quantities. Methodological aspects reference data generation, representation, regression, as well question how a data-driven model may be validated, are reviewed critically discussed. A survey applications variety research questions chemistry illustrates rapid growth field. vision outlined for development methodology years come.

Language: Английский

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Quantum Chemistry in the Age of Machine Learning

The Journal of Physical Chemistry Letters, Journal Year: 2020, Volume and Issue: 11(6), P. 2336 - 2347

Published: March 3, 2020

As the quantum chemistry (QC) community embraces machine learning (ML), number of new methods and applications based on combination QC ML is surging. In this Perspective, a view current state affairs in exciting research field offered, challenges using are described, potential future developments outlined. Specifically, examples how used to improve accuracy accelerate chemical shown. Generalization classification existing techniques provided ease navigation sea literature guide researchers entering field. The emphasis Perspective supervised learning.

Language: Английский

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Machine-learned potentials for next-generation matter simulations

Nature Materials, Journal Year: 2021, Volume and Issue: 20(6), P. 750 - 761

Published: May 27, 2021

Language: Английский

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Machine Learning for Electronically Excited States of Molecules

Chemical Reviews, Journal Year: 2020, Volume and Issue: 121(16), P. 9873 - 9926

Published: Nov. 19, 2020

Electronically excited states of molecules are at the heart photochemistry, photophysics, as well photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but branch artificial intelligence can be used advance exciting research field all its aspects. Discussed applications for include dynamics simulations, static calculations absorption spectra, many others. order put these studies into context, discuss promises pitfalls involved techniques. Since latter mostly based chemistry provide short introduction electronic structure methods approaches nonadiabatic describe tricks problems when using them molecules.

Language: Английский

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Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: Nov. 11, 2020

Abstract Combustion is a complex chemical system which involves thousands of reactions and generates hundreds molecular species radicals during the process. In this work, neural network-based dynamics (MD) simulation carried out to simulate benchmark combustion methane. During MD simulation, detailed reaction processes leading creation specific including various intermediate products are intimately revealed characterized. Overall, total 798 different were recorded some new pathways discovered. We believe that present work heralds dawn era in reactive can be practically applied simulating important systems at ab initio level, provides atomic-level understanding as well discovery an unprecedented level detail beyond what laboratory experiments could accomplish.

Language: Английский

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High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas–Surface Scattering Processes from Machine Learning

The Journal of Physical Chemistry Letters, Journal Year: 2020, Volume and Issue: 11(13), P. 5120 - 5131

Published: June 9, 2020

In this Perspective, we review recent advances in constructing high-fidelity potential energy surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs, albeit with substantial initial investments, provide significantly higher efficiency than direct dynamics methods and/or high accuracy at a level that is not affordable by on-the-fly approaches. These PESs only are necessity for quantum dynamical studies because of delocalization wave packets but also enable the study low-probability and long-time events (quasi-)classical treatments. Our focus here on inelastic reactive scattering processes, which more challenging bound systems involvement continua. Relevant applications developments processes both gas phase gas-surface interfaces discussed.

Language: Английский

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Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

The Journal of Physical Chemistry Letters, Journal Year: 2020, Volume and Issue: 11(10), P. 3828 - 3834

Published: April 20, 2020

In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. this work, we show how can be used to advance the research field photochemistry by all important properties-multiple energies, forces, different couplings-for photodynamics simulations. We simplify such simulations substantially (i) phase-free training skipping costly preprocessing raw data; (ii) rotationally covariant nonadiabatic couplings, which either trained or (iii) alternatively approximated from only ML potentials, their gradients, Hessians; (iv) incorporating spin-orbit couplings. As deep-learning method, employ SchNet with its automatically determined representation molecular structures extend it for multiple electronic states. combination dynamics program SHARC, approach termed SchNarc tested on two polyatomic molecules paves way toward efficient complex systems.

Language: Английский

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Molecular excited states through a machine learning lens

Nature Reviews Chemistry, Journal Year: 2021, Volume and Issue: 5(6), P. 388 - 405

Published: May 20, 2021

Language: Английский

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Perspective on integrating machine learning into computational chemistry and materials science

The Journal of Chemical Physics, Journal Year: 2021, Volume and Issue: 154(23)

Published: June 21, 2021

Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established the construction high-dimensional interatomic potentials. Not a day goes by without another proof principle published on how can represent predict quantum mechanical properties-be they observable, such as polarizabilities, or not, atomic charges. As is becoming pervasive simulation, we provide an overview atomistic computational modeling transformed incorporation approaches. From perspective practitioner field, assess common workflows to structure, dynamics, spectroscopy affected ML. Finally, discuss tighter lasting integration with chemistry materials science be achieved what it will mean for research practice, software development, postgraduate training.

Language: Английский

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