The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials

Abstract

Recent years have witnessed the fast development of machine learning potentials (MLPs) and their widespread applications in chemistry, physics, and material science. By fitting discrete ab initio data faithfully to continuous and symmetry-preserving mathematical forms, MLPs have enabled accurate and efficient atomistic simulations in a large scale from first principles. In this review, we provide an overview of the evolution of MLPs in the past two decades and focus on the state-of-the-art MLPs proposed in the last a few years for molecules, reactions, and materials. We discuss some representative applications of MLPs and the trend of developing universal potentials across a variety of systems. Finally, we outline a list of open challenges and opportunities in the development and applications of MLPs.

87 pages,8 figures

References in corpus (80)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• Machine learning and the physical sciences
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• SchNet - a deep learning architecture for molecules and materials
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• The Open Catalyst 2020 (OC20) Dataset and Community Challenges
• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
• Machine learning prediction errors better than DFT accuracy
• DeePMD-kit v2: A software package for Deep Potential models
• The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
• A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
• Physics-inspired structural representations for molecules and materials
• Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
• How van der Waals interactions determine the unique properties of water
• GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
• Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements
• Machine learning for electronically excited states of molecules
• Machine-learning interatomic potentials for materials science
• Ab initio thermodynamics of liquid and solid water
• The Phase Diagram of a Deep Potential Water Model
• SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
• FCHL revisited: faster and more accurate quantum machine learning
• Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network
• Neural Network Based in Silico Simulation of Combustion Reactions
• Hierarchical modeling of molecular energies using a deep neural network
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
• Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
• A deep potential model with long-range electrostatic interactions
• Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
• Incorporating long-range physics in atomic-scale machine learning
• Deep Potentials for Materials Science
• General-purpose machine-learned potential for 16 elemental metals and their alloys
• On the Completeness of Atomic Structure Representations
• Inclusion of machine learning kernel ridge regression potential energy surfaces in on-the-fly nonadiabatic molecular dynamics simulation
• Structure-based Sampling and Self-correcting Machine Learning for Accurate Calculations of Potential Energy Surfaces and Vibrational Levels
• Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
• Representing molecule-surface interactions with symmetry-adapted neural networks
• Self-Consistent Determination of Long-Range Electrostatics in Neural Network Potentials
• DPA-2: a large atomic model as a multi-task learner
• q-AQUA: a many-body CCSD(T) water potential, including 4-body interactions, demonstrates the quantum nature of water from clusters to the liquid phase
• Unified Graph Neural Network Force-field for the Periodic Table
• TeaNet: universal neural network interatomic potential inspired by iterative electronic relaxations
• Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
• Bayesian machine learning for quantum molecular dynamics
• Physically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and Nonlocality
• Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals
• Breaking the Coupled Cluster Barrier for Machine Learned Potentials of Large Molecules: The Case of 15-atom Acetylacetone
• SchNetPack 2.0: A neural network toolbox for atomistic machine learning
• Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties
• Recursive evaluation and iterative contraction of $N$-body equivariant features
• BIGDML: Towards Exact Machine Learning Force Fields for Materials
• Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
• Universal Machine Learning for the Response of Atomistic Systems to External Fields
• PANNA: Properties from Artificial Neural Network Architectures
• Strong Vibrational Relaxation of NO Scattered from Au(111): Importance of an Accurate Adiabatic Potential Energy Surface
• Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects
• GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials
• Molecular dynamics simulation with finite electric fields using Perturbed Neural Network Potentials
• Developments and Further Applications of Ephemeral Data Derived Potentials
• Accelerating Atomistic Simulations with Piecewise Machine Learned Ab Initio Potentials at Classical Force Field-like Cost
• Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimer
• The MD17 Datasets from the Perspective of Datasets for Gas-Phase "Small" Molecule Potentials
• E(n)-Equivariant Cartesian Tensor Passing Potential
• Mechanical Vibrational Relaxation of NO Scattering from Metal and Insulator Surfaces: When and Why Are They Different?
• Completeness of Atomic Structure Representations
• No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
• MOLPIPx: an end-to-end differentiable package for permutationally invariant polynomials in Python and Rust

Cited by in corpus (1)

• Active Δ-learning with universal potentials for global structure optimization