Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine Learning Potential

Nanomaterials, Vol. 13 (2023), Iss. 9 P.1576

Scientific discovery in the age of artificial intelligence

Nature, Vol. 620 (2023), Iss. 7972 P.47

Advances in the ocean-3 spectroscopy package

Vinson, John

Physical Chemistry Chemical Physics, Vol. 24 (2022), Iss. 21 P.12787

Domain structures and stacking sequences of Mg–Zn–Y long-period stacking ordered (LPSO) structures predicted by Deep-learning Potential

Materials Today Communications, Vol. 38 (2024), Iss. P.108301

A machine learning protocol for geometric information retrieval from molecular spectra

Artificial Intelligence Chemistry, Vol. 2 (2024), Iss. 1 P.100031

Solidification of the glass-forming Al86Ni2Co6Gd6 melt under high pressure

Materialia, Vol. 28 (2023), Iss. P.101713

Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks

The Journal of Physical Chemistry Letters, Vol. 9 (2018), Iss. 19 P.5733

Data-driven prediction of complex crystal structures of dense lithium

Nature Communications, Vol. 14 (2023), Iss. 1

Revolutionizing physics: a comprehensive survey of machine learning applications

Frontiers in Physics, Vol. 12 (2024), Iss.

Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems

Physical Review B, Vol. 109 (2024), Iss. 21

Aqueous solution chemistryin silicoand the role of data-driven approaches

Chemical Physics Reviews, Vol. 5 (2024), Iss. 2

SwitchNet: A Neural Network Model for Forward and Inverse Scattering Problems

SIAM Journal on Scientific Computing, Vol. 41 (2019), Iss. 5 P.A3182

Large-scale atomistic simulation of dislocation core structure in face-centered cubic metal with Deep Potential method

Computational Materials Science, Vol. 218 (2023), Iss. P.111941

From Graphene Oxide to Graphene: Changes in Interfacial Water Structure and Reactivity Using Deep Neural Network Force Fields

The Journal of Physical Chemistry C, Vol. 128 (2024), Iss. 39 P.16437

A Deep Potential model for liquid–vapor equilibrium and cavitation rates of water

The Journal of Chemical Physics, Vol. 158 (2023), Iss. 18

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

Computer Physics Communications, Vol. 253 (2020), Iss. P.107206

Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface

Journal of Chemical Theory and Computation, Vol. 19 (2023), Iss. 21 P.7972

An equivariant neural operator for developing nonlocal tensorial constitutive models

Journal of Computational Physics, Vol. 488 (2023), Iss. P.112243

Simulation studies of the stability and growth kinetics of Pt-Sn phases using a machine learning interatomic potential

Computational Materials Science, Vol. 229 (2023), Iss. P.112388

SAIBench: Benchmarking AI for Science

BenchCouncil Transactions on Benchmarks, Standards and Evaluations, Vol. 2 (2022), Iss. 2 P.100063

Synthesis of computer simulation and machine learning for achieving the best material properties of filled rubber

Scientific Reports, Vol. 10 (2020), Iss. 1

Application of machine learning potentials to predict grain boundary properties in fcc elemental metals

Physical Review Materials, Vol. 4 (2020), Iss. 12

Deep Learning for Nonadiabatic Excited-State Dynamics

The Journal of Physical Chemistry Letters, Vol. 9 (2018), Iss. 23 P.6702

Understanding the sluggish and highly variable transport kinetics of lithium ions in LiFePO4

Science China Chemistry, Vol. 66 (2023), Iss. 11 P.3297

Accurate Fe–He machine learning potential for studying He effects in BCC-Fe

Journal of Nuclear Materials, Vol. 574 (2023), Iss. P.154183

Machine learning phase space quantum dynamics approaches

The Journal of Chemical Physics, Vol. 154 (2021), Iss. 18

Machine learning potentials for multicomponent systems: The Ti-Al binary system

Seko, Atsuto

Physical Review B, Vol. 102 (2020), Iss. 17

Faster exact exchange in periodic systems using single-precision arithmetic

Vinson, John

The Journal of Chemical Physics, Vol. 153 (2020), Iss. 20

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Chemical Reviews, Vol. 121 (2021), Iss. 16 P.9816

Formation and Dynamics of Imidazole Supramolecular Chains Investigated by Deep Potential Molecular Dynamics Simulation

Langmuir, Vol. 40 (2024), Iss. 45 P.23864

Extended Lagrangian Born–Oppenheimer molecular dynamics: from density functional theory to charge relaxation models

Niklasson, Anders M. N.

The European Physical Journal B, Vol. 94 (2021), Iss. 8

DeePCG: Constructing coarse-grained models via deep neural networks

The Journal of Chemical Physics, Vol. 149 (2018), Iss. 3

Learned Reconstruction of Protein Folding Trajectories from Noisy Single-Molecule Time Series

Journal of Chemical Theory and Computation, Vol. 19 (2023), Iss. 14 P.4654

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

Nature Communications, Vol. 11 (2020), Iss. 1

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

npj Computational Materials, Vol. 8 (2022), Iss. 1

Raman spectrum and polarizability of liquid water from deep neural networks

Physical Chemistry Chemical Physics, Vol. 22 (2020), Iss. 19 P.10592

Deep potentials for materials science

Materials Futures, Vol. 1 (2022), Iss. 2 P.022601

Neural Network Potentials: A Concise Overview of Methods

Annual Review of Physical Chemistry, Vol. 73 (2022), Iss. 1 P.163

Developing Potential Energy Surfaces for Graphene-Based 2D–3D Interfaces From Modified High-Dimensional Neural Networks for Applications in Energy Storage

Journal of Electrochemical Energy Conversion and Storage, Vol. 19 (2022), Iss. 4

Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture

npj Computational Materials, Vol. 8 (2022), Iss. 1

Anisotropic molecular coarse-graining by force and torque matching with neural networks

The Journal of Chemical Physics, Vol. 159 (2023), Iss. 2

Computing infrastructure construction and optimization for high-performance computing and artificial intelligence

CCF Transactions on High Performance Computing, Vol. 3 (2021), Iss. 4 P.331

Classical and machine learning interatomic potentials for BCC vanadium

Physical Review Materials, Vol. 6 (2022), Iss. 11

i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations

The Journal of Chemical Physics, Vol. 161 (2024), Iss. 6

Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery

The Journal of Physical Chemistry Letters, Vol. 11 (2020), Iss. 20 P.8710

Adaptive coupling of a deep neural network potential to a classical force field

The Journal of Chemical Physics, Vol. 149 (2018), Iss. 15

Band alignment of CoO(100)–water and CoO(111)–water interfaces accelerated by machine learning potentials

The Journal of Chemical Physics, Vol. 161 (2024), Iss. 13

Stable Solid Molecular Hydrogen above 900 K from a Machine-Learned Potential Trained with Diffusion Quantum Monte Carlo

Physical Review Letters, Vol. 130 (2023), Iss. 7

Mitigating amorphization in superhard boron carbide by microalloying-induced stacking fault formation

An, Qi

Physical Review Materials, Vol. 5 (2021), Iss. 10

Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning

SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, (2020), P.1

Towards a new paradigm in intelligence-driven computational fluid dynamics simulations

Engineering Applications of Computational Fluid Mechanics, Vol. 18 (2024), Iss. 1

Deep learning interatomic potential for thermal and defect behaviour of aluminum nitride with quantum accuracy

Computational Materials Science, Vol. 232 (2024), Iss. P.112656

Deep potential molecular dynamics on the microstructure and thermodynamic properties of FLiNaK–ThF<sub>4</sub>

SCIENTIA SINICA Chimica, Vol. 53 (2023), Iss. 6 P.1008

Molecular dynamics simulations of lanthanum chloride by deep learning potential

Computational Materials Science, Vol. 210 (2022), Iss. P.111014

Active learning of uniformly accurate interatomic potentials for materials simulation

Physical Review Materials, Vol. 3 (2019), Iss. 2

Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential

Physical Review B, Vol. 99 (2019), Iss. 21

Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways

ACS Physical Chemistry Au, Vol. 2 (2022), Iss. 4 P.316

The Middle Science: Traversing Scale In Complex Many-Body Systems

ACS Central Science, Vol. 7 (2021), Iss. 8 P.1271

Investigations of water/oxide interfaces by molecular dynamics simulations

WIREs Computational Molecular Science, Vol. 11 (2021), Iss. 6

Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning

Nature Computational Science, Vol. 4 (2024), Iss. 3 P.210

Analysis on Microstructure–Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations

Polymers, Vol. 13 (2021), Iss. 16 P.2683

Unusual dynamics of tetrahedral liquids caused by the competition between dynamic heterogeneity and structural heterogeneity

The Journal of Chemical Physics, Vol. 161 (2024), Iss. 4

34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering

A deep learning-based energy and force prediction framework for high-throughput quantum chemistry calculations

2024

Predicting Molecular Energy Using Force-Field Optimized Geometries and Atomic Vector Representations Learned from an Improved Deep Tensor Neural Network

Journal of Chemical Theory and Computation, Vol. 15 (2019), Iss. 7 P.4113

Overview Frequency Principle/Spectral Bias in Deep Learning

Communications on Applied Mathematics and Computation, Vol. (2024), Iss.

Study of high-pressure thermophysical properties of orthocarbonate Sr3CO5 using deep learning molecular dynamics simulations

Physical Chemistry Chemical Physics, Vol. 26 (2024), Iss. 7 P.6351

Prediction on local structure and properties of LiCl-KCl-AlCl3 ternary molten salt with deep learning potential

Journal of Molecular Liquids, Vol. 375 (2023), Iss. P.120689

Development of a multi-element neural network modified lattice inversion potential and application to the Ta-He system

Computational Materials Science, Vol. 237 (2024), Iss. P.112899

Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks

The Journal of Physical Chemistry A, Vol. 126 (2022), Iss. 24 P.3926

Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based Ab Initio Molecular Dynamics. 1. Theory, Algorithm, and Performance

Journal of Chemical Theory and Computation, Vol. 16 (2020), Iss. 6 P.3757

Exploring the Effects of Ionic Defects on the Stability of CsPbI3 with a Deep Learning Potential

ChemPhysChem, Vol. 23 (2022), Iss. 7

Accurate Deep Potential model for the Al–Cu–Mg alloy in the full concentration space*

Chinese Physics B, Vol. 30 (2021), Iss. 5 P.050706

Exploring the mechanism of plastic deformation in BCC Mg-Li-Al alloys via Machine learning Molecular dynamics simulations

Computational Materials Science, Vol. 246 (2025), Iss. P.113396

Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics

Physics of Plasmas, Vol. 27 (2020), Iss. 12

Reconstruction of protein structures from single-molecule time series

The Journal of Chemical Physics, Vol. 153 (2020), Iss. 19

Fermionic neural network with effective core potential

Physical Review Research, Vol. 4 (2022), Iss. 1

Physically inspired atom-centered symmetry functions for the construction of high dimensional neural network potential energy surfaces

Computational Materials Science, Vol. 186 (2021), Iss. P.110071

A generalizable machine learning potential of Ag–Au nanoalloys and its application to surface reconstruction, segregation and diffusion

Modelling and Simulation in Materials Science and Engineering, Vol. 30 (2022), Iss. 2 P.025003

Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials

The Journal of Chemical Physics, Vol. 160 (2024), Iss. 17

Incorporating Neural Networks into the AMOEBA Polarizable Force Field

The Journal of Physical Chemistry B, Vol. 128 (2024), Iss. 10 P.2381

Machine Learning and Physics: A Survey of Integrated Models

ACM Computing Surveys, Vol. 56 (2024), Iss. 5 P.1

Coordination and Thermophysical Properties of Transition Metal Chlorocomplexes in LiCl–KCl Eutectic

The Journal of Physical Chemistry B, Vol. 125 (2021), Iss. 31 P.8876

A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten

Tungsten, Vol. 6 (2024), Iss. 2 P.304

Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems

Seko, Atsuto

Journal of Applied Physics, Vol. 133 (2023), Iss. 1

Prediction of Water Diffusion in Wide Varieties of Polymers with All-Atom Molecular Dynamics Simulations and Deep Generative Models

Journal of Chemical Information and Modeling, Vol. 63 (2023), Iss. 1 P.76

A review of advancements in coarse-grained molecular dynamics simulations

Molecular Simulation, Vol. 47 (2021), Iss. 10-11 P.786

Characterization of the Hydrogen-Bond Network in High-Pressure Water by Deep Potential Molecular Dynamics

Journal of Chemical Theory and Computation, Vol. 19 (2023), Iss. 16 P.5602

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

Chemical Science, Vol. 9 (2018), Iss. 8 P.2261

Shadow Molecular Dynamics and Atomic Cluster Expansions for Flexible Charge Models

Journal of Chemical Theory and Computation, Vol. 19 (2023), Iss. 13 P.4255

TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials

Journal of Chemical Information and Modeling, Vol. 60 (2020), Iss. 7 P.3408

Strategy to consider element distribution when constructing training datasets for developing machine learning potentials of alloys based on a Monte-Carlo-like method

Physical Review B, Vol. 106 (2022), Iss. 9

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

Journal of Chemical Theory and Computation, Vol. 16 (2020), Iss. 7 P.4192

Carrier-phonon decoupling in perovskite thermoelectrics via entropy engineering

Nature Communications, Vol. 15 (2024), Iss. 1

Advances in Molecular Dynamics Studies of Molten Salts Based on Machine Learning

Acta Chimica Sinica, Vol. 81 (2023), Iss. 11 P.1663

Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory-Based Ab Initio Molecular Dynamics II: Extensions to the Isobaric–Isoenthalpic and Isobaric–Isothermal Ensembles

Journal of Chemical Theory and Computation, Vol. 17 (2021), Iss. 12 P.7789

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

Acta Materialia, Vol. 215 (2021), Iss. P.117008

AI for organic and polymer synthesis

Science China Chemistry, Vol. 67 (2024), Iss. 8 P.2461

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning

Journal of Chemical Information and Modeling, Vol. 59 (2019), Iss. 8 P.3485

Deep-learning potential method to simulate shear viscosity of liquid aluminum at high temperature and high pressure by molecular dynamics

AIP Advances, Vol. 11 (2021), Iss. 1

Deep-learning potentials for proton transport in double-sided graphanol

Journal of Materials Research, Vol. 38 (2023), Iss. 24 P.5114

A Practical Guide to Recent Advances in Multiscale Modeling and Simulation of Biomolecules

Learning DeePMD-Kit: A Guide to Building Deep Potential Models

2023

Solving many-electron Schrödinger equation using deep neural networks

Journal of Computational Physics, Vol. 399 (2019), Iss. P.108929

Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential

Chinese Physics B, Vol. 33 (2024), Iss. 7 P.076103

A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?

The Journal of Chemical Physics, Vol. 158 (2023), Iss. 8

A high-dimensional neural network potential for molecular dynamics simulations of condensed phase nickel and phase transitions

Molecular Simulation, Vol. 49 (2023), Iss. 3 P.263

Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations

Journal of the Mechanics and Physics of Solids, Vol. 190 (2024), Iss. P.105714

Training models using forces computed by stochastic electronic structure methods

Electronic Structure, Vol. 6 (2024), Iss. 1 P.015011

Crystal Structure Prediction of Binary Alloys via Deep Potential

Frontiers in Chemistry, Vol. 8 (2020), Iss.

Subspace decomposition based DNN algorithm for elliptic type multi-scale PDEs

Journal of Computational Physics, Vol. 488 (2023), Iss. P.112242

Metadynamics for training neural network model chemistries: A competitive assessment

The Journal of Chemical Physics, Vol. 148 (2018), Iss. 24

Solving Traveltime Tomography with Deep Learning

Communications in Mathematics and Statistics, Vol. 11 (2023), Iss. 1 P.3

Machine-learning atomic simulation for heterogeneous catalysis

npj Computational Materials, Vol. 9 (2023), Iss. 1

Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations

Machine Learning: Science and Technology, Vol. 2 (2021), Iss. 3 P.031001

Reinforced dynamics for enhanced sampling in large atomic and molecular systems

The Journal of Chemical Physics, Vol. 148 (2018), Iss. 12

High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks

Machine Learning: Science and Technology, Vol. 3 (2022), Iss. 2 P.025011

Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics

Computational Materials Science, Vol. 204 (2022), Iss. P.111137

Extended Lagrangian Born–Oppenheimer molecular dynamics for orbital-free density-functional theory and polarizable charge equilibration models

Niklasson, Anders M. N.

The Journal of Chemical Physics, Vol. 154 (2021), Iss. 5

Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric KNbO3 perovskite

Physical Review B, Vol. 107 (2023), Iss. 1

Revealing the molecular structures of α-Al2O3(0001)–water interface by machine learning based computational vibrational spectroscopy

The Journal of Chemical Physics, Vol. 161 (2024), Iss. 12

Finite-temperature screw dislocation core structures and dynamics in α-titanium

npj Computational Materials, Vol. 9 (2023), Iss. 1

Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system

Journal of the Ceramic Society of Japan, Vol. 131 (2023), Iss. 10 P.762

Deep learning inter-atomic potential model for accurate irradiation damage simulations

Applied Physics Letters, Vol. 114 (2019), Iss. 24

Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks

Journal of Computational Physics, Vol. 443 (2021), Iss. P.110523

Modelling infrared spectra of the O-H stretches in liquid H 2 O based on a deep learning potential, the importance of nuclear quantum effects

Molecular Simulation, Vol. 50 (2024), Iss. 7-9 P.539

When do short-range atomistic machine-learning models fall short?

The Journal of Chemical Physics, Vol. 154 (2021), Iss. 3

Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene

npj Computational Materials, Vol. 7 (2021), Iss. 1

Accurate fundamental invariant-neural network representation of ab initio potential energy surfaces

National Science Review, Vol. 10 (2023), Iss. 12

MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data

Soft Matter, Vol. 18 (2022), Iss. 44 P.8446

Recent advances and applications of machine learning in solid-state materials science

npj Computational Materials, Vol. 5 (2019), Iss. 1

Simulations of activities, solubilities, transport properties, and nucleation rates for aqueous electrolyte solutions

Panagiotopoulos, Athanassios Z.

The Journal of Chemical Physics, Vol. 153 (2020), Iss. 1

Comparative Studies of the Structural and Transport Properties of Molten Salt FLiNaK Using the Machine-Learned Neural Network and Reparametrized Classical Forcefields

The Journal of Physical Chemistry B, Vol. 125 (2021), Iss. 37 P.10562

Neural Canonical Transformation with Symplectic Flows

Physical Review X, Vol. 10 (2020), Iss. 2

Deep neural network potential for simulating hydrogen blistering in tungsten

Physical Review Materials, Vol. 7 (2023), Iss. 9

Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy

The Journal of Physical Chemistry A, Vol. 124 (2020), Iss. 35 P.7155

Hydration MgCl2-NaCl-KCl molten salt using a novel approach for training machine learning potential

Journal of Molecular Liquids, Vol. 394 (2024), Iss. P.123533

Neural network atomistic potentials for global energy minima search in carbon clusters

Physical Chemistry Chemical Physics, Vol. 25 (2023), Iss. 32 P.21173

Machine Learning in Nano-Scale Biomedical Engineering

IEEE Transactions on Molecular, Biological and Multi-Scale Communications, Vol. 7 (2021), Iss. 1 P.10

DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models

Computer Physics Communications, Vol. 282 (2023), Iss. P.108520

Piezo- and Pyroelectricity in Zirconia: A Study with Machine-Learned Force Fields

Physical Review Applied, Vol. 18 (2022), Iss. 5

Exploring QCD matter in extreme conditions with Machine Learning

Progress in Particle and Nuclear Physics, Vol. 135 (2024), Iss. P.104084

Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids

Computer Methods in Applied Mechanics and Engineering, Vol. 388 (2022), Iss. P.114211

Efficient Molecular Dynamics Simulations of Deep Eutectic Solvents with First-Principles Accuracy Using Machine Learning Interatomic Potentials

Journal of Chemical Theory and Computation, Vol. 19 (2023), Iss. 23 P.8732

Neural network–based pore flow field prediction in porous media using super resolution

Physical Review Fluids, Vol. 7 (2022), Iss. 7

Influence of temperature on the process of hydrogen bond symmetrization in ɛ-FeOOH

Physical Review B, Vol. 108 (2023), Iss. 18

Simulating the isotropic Raman spectra of O–H stretching mode in liquid H2O based on a machine learning potential: the influence of vibrational couplings

Physical Chemistry Chemical Physics, Vol. 25 (2023), Iss. 41 P.28180

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

The Journal of Physical Chemistry B, Vol. 127 (2023), Iss. 11 P.2302

Molecular potential energy computation via graph edge aggregate attention neural network

Chinese Journal of Chemical Physics, Vol. 36 (2023), Iss. 6 P.691

Isotope effects in liquid water via deep potential molecular dynamics

Molecular Physics, Vol. 117 (2019), Iss. 22 P.3269

A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment

Nuclear Fusion, Vol. 62 (2022), Iss. 12 P.126013

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

Computer Physics Communications, Vol. 259 (2021), Iss. P.107624

A machine learning vibrational spectroscopy protocol for spectrum prediction and spectrum-based structure recognition

Fundamental Research, Vol. 1 (2021), Iss. 4 P.488

Many-body interactions and deep neural network potentials for water

The Journal of Chemical Physics, Vol. 160 (2024), Iss. 14

Machine learning-based prediction of mechanical properties of N-doped γ-graphdiyne

Science China Materials, Vol. 67 (2024), Iss. 4 P.1129

Concurrent learning scheme for crystal structure prediction

Physical Review B, Vol. 109 (2024), Iss. 9

A highly transferable and efficient machine learning interatomic potentials study of α-Fe–C binary system

Acta Materialia, Vol. 281 (2024), Iss. P.120408

The development and perspective of computational physics software in China

SCIENTIA SINICA Physica, Mechanica & Astronomica, Vol. 54 (2024), Iss. 4 P.247110

Profiling the off-center atomic displacements in CuCl at finite temperatures with a deep-learning potential

Physical Review Materials, Vol. 7 (2023), Iss. 3

Deep learning-driven molecular dynamics simulations of molten carbonates: 1. Local structure and transport properties of molten Li2CO3-Na2CO3 system

Ionics, Vol. 28 (2022), Iss. 3 P.1231

Efficiently Trained Deep Learning Potential for Graphane

The Journal of Physical Chemistry C, Vol. 125 (2021), Iss. 27 P.14874

Machine Learning for Chemistry: Basics and Applications

Engineering, Vol. 27 (2023), Iss. P.70

Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential

The Journal of Physical Chemistry C, Vol. 125 (2021), Iss. 5 P.3127

Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly

The Journal of Physical Chemistry B, Vol. 128 (2024), Iss. 15 P.3662

The Reconstruction of Pt(001) Surface and the Shell‐Like Reconstruction of the Vicinal Pt(001) Surfaces Revealed by Neural Network Potential

Small, Vol. 20 (2024), Iss. 44

Theory of moment propagation for quantum dynamics in single-particle description

The Journal of Chemical Physics, Vol. 160 (2024), Iss. 6

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences

The Journal of Chemical Physics, Vol. 151 (2019), Iss. 8

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Computer Physics Communications, Vol. 228 (2018), Iss. P.178

Determining the thermal conductivity and phonon behavior of SiC materials with quantum accuracy via deep learning interatomic potential model

Journal of Nuclear Materials, Vol. 591 (2024), Iss. P.154897

A deep potential model with long-range electrostatic interactions

The Journal of Chemical Physics, Vol. 156 (2022), Iss. 12

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

The Journal of Physical Chemistry Letters, Vol. 11 (2020), Iss. 13 P.5120

Representing globally accurate reactive potential energy surfaces with complex topography by combining Gaussian process regression and neural networks

Physical Chemistry Chemical Physics, Vol. 24 (2022), Iss. 21 P.12827

Signatures of a liquid–liquid transition in an ab initio deep neural network model for water

Proceedings of the National Academy of Sciences, Vol. 117 (2020), Iss. 42 P.26040

Deep neural network for the dielectric response of insulators

Physical Review B, Vol. 102 (2020), Iss. 4

Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations

The Journal of Chemical Physics, Vol. 161 (2024), Iss. 13

Azo(xy) vs Aniline Selectivity in Catalytic Nitroarene Reduction by Intermetallics: Experiments and Simulations

The Journal of Physical Chemistry C, Vol. 125 (2021), Iss. 44 P.24440

Embedding Quantum Statistical Excitations in a Classical Force Field

Atlas, Susan R.

The Journal of Physical Chemistry A, Vol. 125 (2021), Iss. 17 P.3760

Discovery and design of soft polymeric bio-inspired materials with multiscale simulations and artificial intelligence

Journal of Materials Chemistry B, Vol. 8 (2020), Iss. 31 P.6562

Rapid Prediction of a Liquid Structure from a Single Molecular Configuration Using Deep Learning

Journal of Chemical Information and Modeling, Vol. 63 (2023), Iss. 12 P.3742

Development and Validation of Versatile Deep Atomistic Potentials for Metal Oxides

The Journal of Physical Chemistry Letters, Vol. 14 (2023), Iss. 2 P.468

Deep learning-based neural network potential for investigating the synergistic effect of H and He in BCC-Fe

Computational Materials Science, Vol. 246 (2025), Iss. P.113434

Machine learning-assisted multi-scale modeling

Journal of Mathematical Physics, Vol. 64 (2023), Iss. 7

Edge Conductivity in PtSe2Nanostructures

Small Structures, Vol. 5 (2024), Iss. 2

Uniformly accurate machine learning-based hydrodynamic models for kinetic equations

Proceedings of the National Academy of Sciences, Vol. 116 (2019), Iss. 44 P.21983