Cited by 781Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesAdvanced Physical and Chemical Molecular Interactions

The DOI matches its Crossref record; 151 of the 151 references it cites were found in a registry or an index.

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

Behler, Jörg·2011·Physical Chemistry Chemical Physics

DOI 10.1039/c1cp21668f

Open this paper in CoChat — free account

Abstract

The accuracy of the results obtained in molecular dynamics or Monte Carlo simulations crucially depends on a reliable description of the atomic interactions. A large variety of efficient potentials has been proposed in the literature, but often the optimum functional form is difficult to find and strongly depends on the particular system. In recent years, artificial neural networks (NN) have become a promising new method to construct potentials for a wide range of systems. They offer a number of advantages: they are very general and applicable to systems as different as small molecules, semiconductors and metals; they are numerically very accurate and fast to evaluate; and they can be constructed using any electronic structure method. Significant progress has been made in recent years and a number of successful applications demonstrate the capabilities of neural network potentials. In this Perspective, the current status of NN potentials is reviewed, and their advantages and limitations are discussed.

Integrity signals

Registry

Crossref · 10.1039/c1cp21668f

Cites

151 of 151 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

1 retracted

Theoretical investigation of the dissociation dynamics of vibrationally excited vinyl bromide on an ab initio potential-energy surface obtained using modified novelty sampling and feed-forward neural networks
Doughan, D. I., Raff, L. M., Rockley, M. G., Hagan, M., Agrawal, P. M., & Komanduri, R. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2162170 · Cited by 22
Retracted
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
CHARMM: A program for macromolecular energy, minimization, and dynamics calculations
Brooks, B., Bruccoleri, R., Olafson, B. D., States, D., Swaminathan, S., & Karplus, M. (1983) · Journal of Computational Chemistry · doi:10.1002/jcc.540040211 · Cited by 13,736
A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules
Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz, K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T., Caldwell, J. W., & Kollman, P. A. (1995) · Journal of the American Chemical Society · doi:10.1021/ja00124a002 · Cited by 13,111
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Unified Approach for Molecular Dynamics and Density-Functional Theory
Car, R., & Parrinello, M. (1985) · Physical Review Letters · doi:10.1103/physrevlett.55.2471 · Cited by 10,889

Flagged first, then by citations

Show all 151 references

Research works printed but not linked: 2

Thomsen, 1989, J. Magn. Reson., 84, 212
Fahlman, 1990, Adv. Neural Inf. Process. Syst., 2, 524

Sorted by citations

Items 201–300 of 781

Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy
Lin, Q., Zhang, Y., Zhao, B., & Jiang, B. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0004944 · Cited by 57
Deep Learning for Optoelectronic Properties of Organic Semiconductors
Lu, C., Liu, Q., Sun, Q., Hsieh, C., Zhang, S., Shi, L., & Lee, C. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c00329 · Cited by 61
Atomistic modeling of nanoscale plasticity in high-entropy alloys
Aitken, Z. H., Sorkin, V., & Zhang, Y. (2019) · Journal of materials research/Pratt's guide to venture capital sources · doi:10.1557/jmr.2019.50 · Cited by 57
Training data selection for accuracy and transferability of interatomic potentials
Zapiain, D. M. D. O., Wood, M. A., Lubbers, N., Pereyra, C. Z., Thompson, A. P., & Pérez, D. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00872-x · Cited by 68
“FFNNs have been successfully used to develop IAPs 14,15,57,58 .”
Communication: A six-dimensional state-to-state quantum dynamics study of the H + CH4 → H2 + CH3 reaction (J = 0)
Liu, S., Chen, J., Zhang, Z., & Zhang, D. H. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4774116 · Cited by 56
Construction of reactive potential energy surfaces with Gaussian process regression: active data selection
Guan, Y., Yang, S., & Zhang, D. H. (2017) · Molecular Physics · doi:10.1080/00268976.2017.1407460 · Cited by 56
Machine learning for potential energy surfaces: An extensive database and assessment of methods
Schmitz, G. A., Godtliebsen, I. H., & Christiansen, O. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5100141 · Cited by 58
Intelligent-ReaxFF: Evaluating the reactive force field parameters with machine learning
Guo, F., Wen, Y., Feng, S., Li, X., Li, H., Cui, S., Zhang, Z., Hu, H., Zhang, G., & Cheng, X. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2019.109393 · Cited by 56
MAISE: Construction of neural network interatomic models and evolutionary structure optimization
Hajinazar, S., Thorn, A., Sandoval, E. D., Kharabadze, S., & Kolmogorov, A. N. (2020) · Computer Physics Communications · doi:10.1016/j.cpc.2020.107679 · Cited by 59
Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance
Lü, D., Behler, J., & Li, J. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c04182 · Cited by 56
Self-citation
Liquid-Liquid Critical Point in Phosphorus
Yang, M., Karmakar, T., & Parrinello, M. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.080603 · Cited by 57
Discovering crystals using shape matching and machine learning
Phillips, C. L., & Voth, G. A. (2013) · Soft Matter · doi:10.1039/c3sm51449h · Cited by 55
Rigorous use of symmetry within the construction of multidimensional potential energy surfaces
Ziegler, B., & Rauhut, G. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5047912 · Cited by 55
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
Sauceda, H. E., Gastegger, M., Chmiela, S., Müller, K., & Tkatchenko, A. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0023005 · Cited by 50
Prediction of Intramolecular Reorganization Energy Using Machine Learning
Atahan-Evrenk, Ş., & Atalay, F. B. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b02733 · Cited by 54
Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections
Guan, Y., Guo, H., & Yarkony, D. R. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5099106 · Cited by 53
Learning molecular potentials with neural networks
Gökcan, H., & Isayev, O. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1564 · Cited by 51
“113 One of the initial attempts has originally been motivated to deal with polarization term for FF, and NN has been used to construct environment-dependent multipoles.”
Method
A new potential energy surface for the ground electronic state of the LiH 2 system, and dynamics studies on the H( 2 S) + LiH(X 1 Σ + ) → Li( 2 S) + H 2 (X 1 Σ g + ) reaction
Yuan, J., He, D., & Chen, M. (2015) · Physical Chemistry Chemical Physics · doi:10.1039/c4cp05352d · Cited by 53
Neural Network Based Prediction of Conformational Free Energies - A New Route toward Coarse-Grained Simulation Models
Lemke, T., & Peter, C. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00864 · Cited by 51
Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2 A′ states of LiFH
Guan, Y., Zhang, D. H., Guo, H., & Yarkony, D. R. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp06598e · Cited by 53
When not to use machine learning: A perspective on potential and limitations
Carbone, M. R. (2022) · MRS Bulletin · doi:10.1557/s43577-022-00417-z · Cited by 58
Applications and Advances in Machine Learning Force Fields
Wu, S., Yang, X., Zhao, X., Li, Z., Lü, M., Xie, X., & Yan, J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00889 · Cited by 52
Global Potential Energy Surface for the H+CH4↔H2+CH3 Reaction using Neural Networks
Xu, X., Chen, J., & Zhang, D. H. (2014) · Chinese Journal of Chemical Physics · doi:10.1063/1674-0068/27/04/373-379 · Cited by 52
Improving accuracy of interatomic potentials: more physics or more data? A case study of silica
Novikov, I. S., & Shapeev, A. V. (2018) · Materials Today Communications · doi:10.1016/j.mtcomm.2018.11.008 · Cited by 53
“Another research direction intended to increase the accuracy of the interatomic potentials is the so-called machine-learning interatomic potentials [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41].”
Background
Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks
Sun, G., & Sautet, P. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00465 · Cited by 54
Atomic permutationally invariant polynomials for fitting molecular force fields
Allen, A. E. A., Dusson, G., Ortner, C., & Cśanyi, G. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abd51e · Cited by 55
Development of a general-purpose machine-learning interatomic potential for aluminum by the physically informed neural network method
Pun, G. P. P., Yamakov, V., Hickman, J., Glaessgen, E. H., & Mishin, Y. (2020) · Physical Review Materials · doi:10.1103/physrevmaterials.4.113807 · Cited by 51
“[27] J.”
Method
Ab initio molecular dynamics calculations on scattering of hyperthermal H atoms from Cu(111) and Au(111)
Kroes, G., Pavanello, M., Blanco-Rey, M., Alducin, M., & Auerbach, D. J. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4891483 · Cited by 51
Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers
Lee, C., Lu, C., Yu, Y., Sun, Q., Hsieh, C., Zhang, S., Liu, Q., & Shi, L. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0037863 · Cited by 49
A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry
Li, J., Song, K., & Behler, J. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp06919k · Cited by 51
Self-citation
Chemical Imaging of Surfaces with Sum Frequency Generation Vibrational Spectroscopy
Shah, S. A. R., & Baldelli, S. (2020) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00057 · Cited by 49
Permutation-Invariant-Polynomial Neural-Network-Based Δ-Machine Learning Approach: A Case for the HO 2 Self-Reaction and Its Dynamics Study
Liu, Y., & Li, J. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c01064 · Cited by 49
Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks
de Hijes, P. M., Christoph Dellago, A., Jinnouchi, R., Schmiedmayer, B., & Kresse, G. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0197105 · Cited by 48
An implementation of the Levenberg–Marquardt algorithm for simultaneous-energy-gradient fitting using two-layer feed-forward neural networks
Nguyen-Truong, H. T., & Le, H. M. (2015) · Chemical Physics Letters · doi:10.1016/j.cplett.2015.04.019 · Cited by 48
Ab Initio Vibrational Spectroscopy ofcis- andtrans-Formic Acid from a Global Potential Energy Surface
Tew, D. P., & Mizukami, W. (2016) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.6b09952 · Cited by 49
Molybdenum carbide nanoparticle: Understanding the surface properties and reaction mechanism for energy production towards a sustainable future
Alaba, P. A., Abbas, A., Huang, J., & Daud, W. M. A. W. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.106 · Cited by 48
Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation
Schütt, O., & VandeVondele, J. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00378 · Cited by 47
Automated calculation of thermal rate coefficients using ring polymer molecular dynamics and machine-learning interatomic potentials with active learning
Novikov, I. S., Suleimanov, Y. V., & Shapeev, A. V. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp06037a · Cited by 49
Approximate high mode coupling potentials using Gaussian process regression and adaptive density guided sampling
Schmitz, G. A., Artiukhin, D. G., & Christiansen, O. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5092228 · Cited by 47
Self-Diffusion of Surface Defects at Copper–Water Interfaces
Natarajan, S. K., & Behler, J. (2017) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.6b12657 · Cited by 47
Self-citation
DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules
Fulford, M., Salvalaglio, M., & Molteni, C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00005 · Cited by 46
A novel approach to describe chemical environments in high-dimensional neural network potentials
Kocer, E., Mason, J. K., & Ertürk, H. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5086167 · Cited by 50
“We further investigated the performance of NNPs using our descriptors at a variety of sampling temperatures and NN architectures, with the results reported in Table I implying that a minimum of 15 to 21 descriptors were required.”
BackgroundMethodInfluential
Full and fragmented permutationally invariant polynomial potential energy surfaces for trans and cis N -methyl acetamide and isomerization saddle points
Nandi, A., Qu, C., & Bowman, J. M. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5119348 · Cited by 44
Force Field Parametrization of Metal Ions from Statistical Learning Techniques
Fracchia, F., Frate, G. D., Mancini, G., Rocchia, W., & Barone, V. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00779 · Cited by 46
Topology-Based Machine Learning Strategy for Cluster Structure Prediction
Chen, X., Chen, D., Weng, M., Jiang, Y., Wei, G., & Pan, F. M. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c00974 · Cited by 46
“Gradient boosting regression (GBR) 64 and neural network (NN) 65 have been the methods of choice for modeling atomic interactions.”
Method
Reactive molecular dynamics: From small molecules to proteins
Meuwly, M. (2018) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1386 · Cited by 46
Thermal decomposition of rice husk: a comprehensive artificial intelligence predictive model
Alaba, P. A., Popoola, S. I., Abnisal, F., Lee, C. S., Ohunakin, O. S., Adetiba, E., Akanle, M. B., Patah, M. F. A., Atayero, A. A. A., & Daud, W. M. A. W. (2019) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-019-08915-0 · Cited by 45
Interpolation and Extrapolation of Global Potential Energy Surfaces for Polyatomic Systems by Gaussian Processes with Composite Kernels
Dai, J. F., & Krems, R. V. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00700 · Cited by 44
“98, 146401 (2007) [11] J.”
BackgroundMethod
Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules
Vassilev-Galindo, V., Fonseca, G., Poltavsky, I., & Tkatchenko, A. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0038516 · Cited by 44
High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium
Schran, C., Uhl, F., Behler, J., & Marx, D. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4996819 · Cited by 44
Self-citation
Molecular Dynamics Simulation of Zinc Ion in Water with an ab Initio Based Neural Network Potential
Xu, M., Zhu, T., & Zhang, J. Z. H. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b04087 · Cited by 45
Efficient Generation of Permutationally Invariant Potential Energy Surfaces for Large Molecules
Conte, R., Qu, C., Houston, P. L., & Bowman, J. M. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00001 · Cited by 43
Machine Learning Estimates of Natural Product Conformational Energies
Rupp, M., Bauer, M. R., Wilcken, R., Lange, A., Reutlinger, M., Boeckler, F. M., & Schneider, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003400 · Cited by 44
General Many-Body Framework for Data-Driven Potentials with Arbitrary Quantum Mechanical Accuracy: Water as a Case Study
Lambros, E., Dasgupta, S., Palos, E., Swee, S., Hu, J., Paesani, F., & Lab, P. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00541 · Cited by 42
Atomic-level mechanisms of short-circuit diffusion in materials
Chesser, I., Koju, R. K., & Mishin, Y. M. (2024) · International Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) · doi:10.1515/ijmr-2023-0202 · Cited by 43
An overview about neural networks potentials in molecular dynamics simulation
Martin‐Barrios, R., Conyedo, E. N., Zhang, X., Chen, Y., & González, J. G. (2024) · International Journal of Quantum Chemistry · doi:10.1002/qua.27389 · Cited by 46
Dynamic Workflows for Routine Materials Discovery in Surface Science
Tran, K., Palizhati, A., Back, S., & Ulissi, Z. W. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00386 · Cited by 43
Properties of α-Brass Nanoparticles. 1. Neural Network Potential Energy Surface
Weinreich, J., Römer, A., Paleico, M. L., & Behler, J. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c00559 · Cited by 43
Self-citation
Closing the gap between theory and experiment for lithium manganese oxide spinels using a high-dimensional neural network potential
Eckhoff, M., Schönewald, F., Risch, M., Volkert, C. A., Blöchl, P. E., & Behler, J. (2020) · Physical review. B./Physical review. B · doi:10.1103/physrevb.102.174102 · Cited by 41
“Machine learning has been demonstrated to be a promising tool to develop highly accurate and efficient atomistic potentials [27–30].”
BackgroundSelf-citation
Improving sample and feature selection with principal covariates regression
Cersonsky, R. K., Helfrecht, B. A., Engel, E. A., Kliavinek, S., & Ceriotti, M. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abfe7c · Cited by 46
“Behler and Parrinello introduced a now commonplace NN to fit interatomic potentials [33, 52, 58] based on the decomposition of atomic configurations and total energies into local, atom-centred environments and associated energy contributions.”
Background
Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
Sauceda, H. E., Vassilev-Galindo, V., Chmiela, S., Müller, K., & Tkatchenko, A. (2021) · RePEc: Research Papers in Economics · Cited by 45
Machine-learned interatomic potentials: Recent developments and prospective applications
Eyert, V., Wormald, J. L., Curtin, W. A., & Wimmer, E. (2023) · Journal of materials research/Pratt's guide to venture capital sources · doi:10.1557/s43578-023-01239-8 · Cited by 44
Connectivity optimized nested line graph networks for crystal structures
Ruff, R., Reiser, P., Stühmer, J., & Friederich, P. (2024) · Digital Discovery · doi:10.1039/d4dd00018h · Cited by 42
Low-cost machine learning prediction of excited state properties of iridium-centered phosphors
Terrones, G., Duan, C., Nandy, A., & Kulik, H. J. (2023) · Chemical Science · doi:10.1039/d2sc06150c · Cited by 41
Research Update: Computational materials discovery in soft matter
Bereau, T., Andrienko, D., & Kremer, K. (2016) · APL Materials · doi:10.1063/1.4943287 · Cited by 43
Dynamics of transient species via anion photodetachment
Continetti, R. E., & Guo, H. (2017) · Chemical Society Reviews · doi:10.1039/c7cs00684e · Cited by 41
Crystallization of amorphous GeTe simulated by neural network potential addressing medium-range order
Lee, D., Lee, K., Yoo, D., Jeong, W., & Han, S. (2020) · Computational Materials Science · doi:10.1016/j.commatsci.2020.109725 · Cited by 41
Permutationally invariant polynomial potential energy surfaces for tropolone and H and D atom tunneling dynamics
Houston, P. L., Conte, R., Qu, C., & Bowman, J. M. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0011973 · Cited by 39
Machine-learning potentials for crystal defects
Freitas, R. M., & Cao, Y. (2022) · MRS Communications · doi:10.1557/s43579-022-00221-5 · Cited by 41
Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization
Zhang, Y., Lin, Q., & Jiang, B. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1645 · Cited by 41
“A particularly successful family of methods is based on an atomistic neural network (AtNN) framework pioneered by Behler and Parrinello, 48 which enables a unified representation for small and large systems across the phases and makes significant progress in representing molecular properties.”
BackgroundInfluential
Proton Conducting Neuromorphic Materials and Devices
Yuan, Y., Patel, R. K., Banik, S., Reta, T. B., Bisht, R. S., Fong, D. D., Sankaranarayanan, S. K. R. S., & Ramanathan, S. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00071 · Cited by 45
A nine-dimensional ab initio global potential energy surface for the H2O+ + H2 → H3O+ + H reaction
Li, A., & Guo, H. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4881943 · Cited by 40
A study of adatom ripening on an Al (1 1 1) surface with machine learning force fields
Botu, V., Chapman, J., & Ramprasad, R. (2017) · Computational Materials Science · doi:10.1016/j.commatsci.2016.12.007 · Cited by 39
Construction of diabatic energy surfaces for LiFH with artificial neural networks
Guan, Y., Fu, B., & Zhang, D. H. (2017) · The Journal of Chemical Physics · doi:10.1063/1.5007031 · Cited by 39
Temperature dependence of the vibrational spectrum of porphycene: a qualitative failure of classical-nuclei molecular dynamics
Litman, Y. E., Behler, J., & Rossi, M. (2019) · Faraday Discussions · doi:10.1039/c9fd00056a · Cited by 40
Self-citation
Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol
Rossi, K., Jurásková, V., Wischert, R., Garel, L., Corminbœuf, C., & Ceriotti, M. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00362 · Cited by 39
The role of feature space in atomistic learning
Goscinski, A., Fraux, G., Imbalzano, G., & Ceriotti, M. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abdaf7 · Cited by 35
Active learning for robust, high-complexity reactive atomistic simulations
Lindsey, R., Fried, L. E., Goldman, N., & Bastea, S. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0021965 · Cited by 39
Computation of the S1 ← S0 Vibronic Absorption Spectrum of Formaldehyde by Variational Gaussian Wavepacket and Semiclassical IVR Methods
Bonfanti, M., Petersen, J., Eisenbrandt, P., Burghardt, I., & Pollak, E. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00355 · Cited by 37
Supervised machine learning of ultracold atoms with speckle disorder
Pilati, S., & Pieri, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-42125-w · Cited by 39
Data‐Driven Approaches Toward Smarter Additive Manufacturing
Tian, C., Li, T., Bustillos, J., Bhattacharya, S., Turnham, T., Yeo, J., & Moridi, A. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100014 · Cited by 38
Modified Feed-Forward Neural Network Structures and Combined-Function-Derivative Approximations Incorporating Exchange Symmetry for Potential Energy Surface Fitting
Nguyen-Truong, H. T., & Le, H. M. (2012) · The Journal of Physical Chemistry A · doi:10.1021/jp3020386 · Cited by 38
Improved atomistic Monte Carlo models based onab-initio-trained neural networks: Application to FeCu and FeCr alloys
Castin, N., Messina, L., Domain, C., Pasianot, R. C., & Olsson, P. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.95.214117 · Cited by 37
Comparison of different machine learning models for the prediction of forces in copper and silicon dioxide
Li, W., & Ando, Y. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp04508a · Cited by 38
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
Zaverkin, V., Holzmüller, D., Steinwart, I., & Kästner, J. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00527 · Cited by 39
Constructing Diabatic Potential Energy Matrices with Neural Networks Based on Adiabatic Energies and Physical Considerations: Toward Quantum Dynamic Accuracy
Li, C., Hou, S., & Xie, C. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01074 · Cited by 36
Recent advances and applications of machine learning in electrocatalysis
Hu, Y., Chen, J., Wei, Z. X., He, Q., & Zhao, Y. (2023) · Journal of Materials Informatics · doi:10.20517/jmi.2023.23 · Cited by 38
Capturing short-range order in high-entropy alloys with machine learning potentials
Cao, Y., Sheriff, K., & Freitas, R. M. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01722-2 · Cited by 41
Thermal transport across copper–water interfaces according to deep potential molecular dynamics
Li, Z., Tan, X., Fu, Z., Liu, L., & Yang, J. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp05530a · Cited by 35
Introduction to machine learning potentials for atomistic simulations
Thiemann, F. L., O’Neill, N., Kapil, V., Michaelides, A., & Schran, C. (2024) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ad9657 · Cited by 41
“This includes multiple excellent reviews on the HD-NNP [34–37] and GAP [38, 39] formalism, as well as other broader reviews [5, 11, 40– 42].”
Two-component, ab initio potential energy surface for CO2—H2O, extension to the hydrate clathrate, CO2@(H2O)20, and VSCF/VCI vibrational analyses of both
Wang, Q., & Bowman, J. M. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4994543 · Cited by 35
Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimer
Sugisawa, H., Ida, T., & Krems, R. V. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0023492 · Cited by 39
“98, 146401 (2007) [33] J.”
Influential
Data-Driven Many-Body Potential Energy Functions for Generic Molecules: Linear Alkanes as a Proof-of-Concept Application
Bull-Vulpe, E. F., Riera, M., Bore, S. L., & Paesani, F. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00645 · Cited by 34
Material discovery and modeling acceleration via machine learning
Zuccarini, C., Ramachandran, K., & Jayaseelan, D. D. (2024) · APL Materials · doi:10.1063/5.0230677 · Cited by 41
Solubility curves and nucleation rates from molecular dynamics for polymorph prediction – moving beyond lattice energy minimization
Parks, C. D., Koswara, A., DeVilbiss, F. T., Tung, H., Nere, N. K., Bordawekar, S. V., Nagy, Z. K., & Ramkrishna, D. (2017) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp07181c · Cited by 34
Diabatic potential energy surfaces of MgH 2 + and dynamic studies for the Mg + (3p) + H 2 → MgH + + H reaction
Yuan, J., He, D., Wang, S., Chen, M., & Han, K. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c7cp08679b · Cited by 34
Converged quantum simulations of reactive solutes in superfluid helium: The Bochum perspective
Brieuc, F., Schran, C., Uhl, F., Forbert, H., & Marx, D. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0008309 · Cited by 36
Full-dimensional global potential energy surfaces describing abstraction and exchange for the H + H2S reaction
Lü, D., & Li, J. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4954765 · Cited by 33
Vibrational energy transfer near a dissociative adsorption transition state: State-to-state study of HCl collisions at Au(111)
Geweke, J., Shirhatti, P. R., Rahinov, I., Bartels, C., & Wodtke, A. M. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4959968 · Cited by 34
Potential energy surface interpolation with neural networks for instanton rate calculations
Cooper, A. M., Hallmen, P. P., & Kästner, J. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5015950 · Cited by 34

Authors: Behler, J.
Affiliations: Ruhr University Bochum (DE)
Corresponding author: Behler, Jörg
ORCID: Behler, Jörg
Venue: Physical Chemistry Chemical Physics
Publisher: Royal Society of Chemistry (RSC)
Published: 2011
Volume: 13
Issue: 40
Pages: 17930
ISSN: 1463-9076 (ISSN-L)
1463-9084
DOI: 10.1039/c1cp21668f
OpenAlex: W2058370262
Cited by: 781
References: 151
Citations by year: 2026: 27 · 2025: 58 · 2024: 54 · 2023: 71 · 2022: 68 · 2021: 79 · 2020: 89 · 2019: 66 · 2018: 58 · 2017: 88 · 2016: 33 · 2015: 28 · 2014: 27 · 2013: 23 · 2012: 11
FWCI: 0.93
Type: journal article
Language: en
Open access: closed
Locations: Physical Chemistry Chemical Physics (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
Keywords: neural network potentials, interatomic potentials, potential energy surface, molecular dynamics, Monte Carlo simulation, interatomic interactions, electronic structure methods, large-scale simulation, machine learning for materials
MeSH terms: Computer Simulation, Metals, Models, Molecular, Neural Networks, Computer, Small Molecule Libraries

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks