Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
Open accessAbstract
We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO 2 ) dataset generated from a “melt-quench” ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO 2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies.
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The total NNP errors are similar to other state-of-the-art (rotationally invariant) MLPs for the modeling of reactive events.
“The total NNP errors are similar to other state-of-the-art (rotationally invariant) MLPs38,42,44 for the modeling of reactive events.”
From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [44]
These errors are similar to other reactive (rotationally invariant) MLPs and about the same as for previously developed silica NNPs.
“These errors are similar to other reactive (rotationally invariant) MLPs42,44,52 and about the same as for our previously developed silica NNPs38.”
From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [44]
Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824 · see citing works
From ultrasoft pseudopotentials to the projector augmented-wave method
Kresse, G., & Joubert, D. (1999) · Physical Review B · doi:10.1103/physrevb.59.1758 · Cited by 85,307 · see citing works
Kresse, G., & Furthmüller, J. (1996) · Computational Materials Science · doi:10.1016/0927-0256(96)00008-0 · Cited by 75,541 · see citing works
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135 · see citing works
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541 · see citing works
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721 · see citing works
Canonical dynamics: Equilibrium phase-space distributions
Hoover, W. G. (1985) · Physical Review A · doi:10.1103/physreva.31.1695 · Cited by 23,453 · see citing works
Polymorphic transitions in single crystals: A new molecular dynamics method
Parrinello, M., & Rahman, A. (1981) · Journal of Applied Physics · doi:10.1063/1.328693 · Cited by 19,668 · see citing works
A unified formulation of the constant temperature molecular dynamics methods
Nosé, S. (1984) · The Journal of Chemical Physics · doi:10.1063/1.447334 · Cited by 17,163 · see citing works
P ACKMOL : A package for building initial configurations for molecular dynamics simulations
Martı́nez, L., Andrade, R., Birgin, E. G., & Martínez, J. M. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21224 · Cited by 9,873 · see citing works
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & de Freitas, N. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2494218 · Cited by 6,195 · see citing works
Constant pressure molecular dynamics algorithms
Martyna, G. J., Tobias, D. J., & Klein, M. L. (1994) · The Journal of Chemical Physics · doi:10.1063/1.467468 · Cited by 5,845 · see citing works
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010 · see citing works
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Cśanyi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,144 · see citing works
hdbscan: Hierarchical density based clustering
McInnes, L., Healy, J., & Astels, S. (2017) · The Journal of Open Source Software · doi:10.21105/joss.00205 · Cited by 2,833 · see citing works
Density-Based Clustering Based on Hierarchical Density Estimates
Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-37456-2_14 · Cited by 2,680 · see citing works
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572 · see citing works
MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories
McGibbon, R. T., Beauchamp, K. A., Harrigan, M. P., Klein, C., Swails, J., Hernández, C. X., Schwantes, C. R., Wang, L., Lane, T. J., & Pande, V. S. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2015.08.015 · Cited by 2,358 · see citing works
SchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217 · see citing works
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921 · see citing works
Perspective on density functional theory
Burke, K. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4704546 · Cited by 1,758 · see citing works
Reverse Monte Carlo Simulation: A New Technique for the Determination of Disordered Structures
McGreevy, R. L., & Pusztai, L. (1988) · Molecular Simulation · doi:10.1080/08927028808080958 · Cited by 1,686 · see citing works
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
Shapeev, A. V. (2016) · Multiscale Modeling and Simulation · doi:10.1137/15m1054183 · Cited by 1,506 · see citing works
Perspective: Machine learning potentials for atomistic simulations
Behler, J. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4966192 · Cited by 1,435 · see citing works
Rapid estimation of elastic constants by molecular dynamics simulation under constant stress
Shinoda, W., Shiga, M., & Mikami, M. (2004) · Physical Review B · doi:10.1103/physrevb.69.134103 · Cited by 1,278 · see citing works
Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M., & Tucker, G. J. (2014) · Journal of Computational Physics · doi:10.1016/j.jcp.2014.12.018 · Cited by 1,122 · see citing works
Performance and Cost Assessment of Machine Learning Interatomic Potentials
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Cśanyi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., et al. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b08723 · Cited by 919 · see citing works
Machine learning based interatomic potential for amorphous carbon
Deringer, V. L., & Cśanyi, G. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.95.094203 · Cited by 665 · see citing works
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
Li, Z., Kermode, J. R., & Vita, A. D. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.096405 · Cited by 628 · see citing works
Active learning of uniformly accurate interatomic potentials for materials simulation
Zhang, L., Lin, D.-Y., Wang, H., Car, R., & Weinan, E. (2019) · Physical Review Materials · doi:10.1103/physrevmaterials.3.023804 · Cited by 605 · see citing works
Hafnia and hafnia-toughened ceramics
Wang, J., Li, H. P., & Stevens, R. (1992) · Journal of Materials Science · doi:10.1007/bf00541601 · Cited by 591 · see citing works
Accelerated Hierarchical Density Based Clustering
McInnes, L., & Healy, J. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.12 · Cited by 573 · see citing works
How van der Waals interactions determine the unique properties of water
Morawietz, T., Singraber, A., Dellago, C., & Behler, J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602375113 · Cited by 487 · see citing works
Hierarchical sampling for active learning
Dasgupta, S., & Hsu, D. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390183 · Cited by 458 · see citing works
Thermodynamic limit for synthesis of metastable inorganic materials
Aykol, M., Dwaraknath, S., Sun, W. Q., & Persson, K. A. (2018) · Science Advances · doi:10.1126/sciadv.aaq0148 · Cited by 431 · see citing works
Transport Coefficients from Dissipation in a Canonical Ensemble
Helfand, E. (1960) · Physical Review · doi:10.1103/physrev.119.1 · Cited by 413 · see citing works
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials
Gubaev, K., Podryabinkin, E. V., Hart, G. L. W., & Shapeev, A. V. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.09.031 · Cited by 354 · see citing works
Design and synthesis of the superionic conductor Na10SnP2S12
Richards, W. D., Tsujimura, T., Miara, L. J., Wang, Y. E., Kim, J. C., Ong, S. P., Uechi, I., Suzuki, N., & Ceder, G. (2016) · Nature Communications · doi:10.1038/ncomms11009 · Cited by 314 · see citing works
Hennig, C. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.04.009 · Cited by 282 · see citing works
Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics
Deringer, V. L., Bernstein, N., Bartók, A. P., Cliffe, M. J., Kerber, R. N., Marbella, L. E., Grey, C. P., Elliott, S. R., & Cśanyi, G. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00902 · Cited by 264 · see citing works
Dasgupta, S. (2010) · Theoretical Computer Science · doi:10.1016/j.tcs.2010.12.054 · Cited by 254 · see citing works
Jain, A., Hautier, G., Ong, S. P., & Persson, K. (2016) · Journal of Materials Research · doi:10.1557/jmr.2016.80 · Cited by 244 · see citing works
sGDML: Constructing accurate and data efficient molecular force fields using machine learning
Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K., & Tkatchenko, A. (2019) · Computer Physics Communications · doi:10.1016/j.cpc.2019.02.007 · Cited by 241 · see citing works
Data-Driven Learning of Total and Local Energies in Elemental Boron
Deringer, V. L., Pickard, C. J., & Cśanyi, G. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.156001 · Cited by 206 · see citing works
De novo exploration and self-guided learning of potential-energy surfaces
Bernstein, N., Cśanyi, G., & Deringer, V. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0236-6 · Cited by 198 · see citing works
Validation of molecular dynamics simulation
A, W. F. V. G., & Mark, A. E. (1998) · The Journal of Chemical Physics · doi:10.1063/1.476021 · Cited by 197 · see citing works
Schlom, D. G., Guha, S., & Datta, S. (2008) · MRS Bulletin · doi:10.1557/mrs2008.221 · Cited by 147 · see citing works
Gereben, O., & Pusztai, L. (2012) · Journal of Computational Chemistry · doi:10.1002/jcc.23058 · Cited by 132 · see citing works
Hong, Q., Ushakov, S. V., Kapush, D., Benmore, C. J., Weber, R. J., van de Walle, A., & Navrotsky, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-32848-7 · Cited by 71 · see citing works
Self-citationTemperature effects on the ionic conductivity in concentrated alkaline electrolyte solutions
Shao, Y., Hellström, M., Yllö, A., Mindemark, J., Hermansson, K., Behler, J., & Zhang, C. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp06479f · Cited by 70 · see citing works
Uebing, C., & Gomer, R. (1994) · The Journal of Chemical Physics · doi:10.1063/1.466819 · Cited by 70 · see citing works
High-k (k=30) amorphous hafnium oxide films from high rate room temperature deposition
Li, F. M., Bayer, B. C., Hofmann, S., Dutson, J., Wakeham, S. J., Thwaites, M. J., Milne, W. I., & Flewitt, A. J. (2011) · Applied Physics Letters · doi:10.1063/1.3601487 · Cited by 69 · see citing works
Molecular dynamics simulation of amorphous HfO2for resistive RAM applications
Broglia, G., Ori, G., Larcher, L., & Montorsi, M. (2014) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/0965-0393/22/6/065006 · Cited by 59 · see citing works
Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge
Melvin, R. L., Godwin, R. C., Xiao, J., Thompson, W. G., Berenhaut, K. S., & Salsbury, F. R. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00757 · Cited by 49 · see citing works
The Structure of Liquid and Amorphous Hafnia
Gallington, L. C., Ghadar, Y., Skinner, L. B., Weber, J., Ushakov, S. V., Navrotsky, A., Vázquez‐Mayagoitia, Á., Neuefeind, J. C., Stan, M., Low, J. J., et al. (2017) · Materials · doi:10.3390/ma10111290 · Cited by 44 · see citing works
Self-citationStructure, thermodynamics, and crystallization of amorphous hafnia
Luo, X., & Demkov, A. A. (2015) · Journal of Applied Physics · doi:10.1063/1.4931157 · Cited by 17 · see citing works
Understanding the Structure of Amorphous Thin Film Hafnia (Final Paper)
Miranda, A., & USDOE Office of Science (SC), O. O. W. D. F. T. &. S. (. (2015) · doi:10.2172/1213132 · Cited by 2 · see citing works
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Machine-Learning Interatomic Potentials for Materials Science
Mishin, Y. M. (2021) · Acta Materialia · doi:10.1016/j.actamat.2021.116980 · Cited by 402 · see citing works
Neural Network Potentials: A Concise Overview of Methods
Kocer, E., Ko, T. W., & Behler, J. (2022) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-082720-034254 · Cited by 315 · see citing works
Machine learning potentials for extended systems: a perspective
Behler, J., & Cśanyi, G. (2021) · The European Physical Journal B · doi:10.1140/epjb/s10051-021-00156-1 · Cited by 205 · see citing works
Wu, T., Kent, P. R. C., Gogotsi, Y. G., & Jiang, D. (2022) · Chemistry of Materials · doi:10.1021/acs.chemmater.2c00224 · Cited by 191 · see citing works
Long, Y., Zhu, X., Gao, C., Si, W., Li, J., & Peng, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-56487-5 · Cited by 169 · see citing works
Bai, Q., Liu, S., Tian, Y., Xu, T., Banegas‐Luna, A. J., Pérez‐Sánchez, H., Huang, J., Liu, H., & Yao, X. (2022) · WIREs Computational Molecular Science · doi:10.1002/wcms.1581 · Cited by 156 · see citing works
Verdi, C., Karsai, F., Liu, P., Jinnouchi, R., & Kresse, G. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00630-5 · Cited by 157 · see citing works
Vandermause, J., Xie, Y., Lim, J. S., Owen, C. J., & Kozinsky, B. (2022) · Nature Communications · doi:10.1038/s41467-022-32294-0 · Cited by 147 · see citing works
Benayad, A., Diddens, D., Heuer, A., Krishnamoorthy, A. N., Maiti, M., Cras, F. L., Legallais, M., Rahmanian, F., Shin, Y., Stein, H. S., et al. (2021) · Advanced Energy Materials · doi:10.1002/aenm.202102678 · Cited by 140 · see citing works
Self-citationMachine learning potentials for metal-organic frameworks using an incremental learning approach
Vandenhaute, S., Cools‐Ceuppens, M., DeKeyser, S., Verstraelen, T., & Speybroeck, V. V. (2023) · npj Computational Materials · doi:10.1038/s41524-023-00969-x · Cited by 138 · see citing works
Zhang, S., Makoś, M. Z., Jadrich, R. B., Kraka, E., Barros, K. M., Nebgen, B. T., Tretiak, S., Isayev, O., Lubbers, N., Messerly, R. A., et al. (2024) · Nature Chemistry · doi:10.1038/s41557-023-01427-3 · Cited by 140 · see citing works
Application of machine learning for advanced material prediction and design
Chan, C. H., Sun, M., & Huang, B. (2022) · EcoMat · doi:10.1002/eom2.12194 · Cited by 123 · see citing works
Qi, J., Ko, T. W., Wood, B. C., Pham, T. A., & Ong, S. P. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01227-4 · Cited by 120 · see citing works
Hyperactive learning for data-driven interatomic potentials
van der Oord, C., Sachs, M., Kovács, D. P., Ortner, C., & Cśanyi, G. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01104-6 · Cited by 116 · see citing works
Automated discovery of a robust interatomic potential for aluminum
Smith, J. S., Nebgen, B., Mathew, N., Chen, J., Lubbers, N., Burakovsky, L., Tretiak, S., Nam, H. A., Germann, T., Fensin, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21376-0 · Cited by 98 · see citing works
A machine-learned interatomic potential for silica and its relation to empirical models
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From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks
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Deep learning of accurate force field of ferroelectric HfO 2
Wu, J., Zhang, Y., Zhang, L., & Liu, S. (2021) · Physical review. B./Physical review. B · doi:10.1103/physrevb.103.024108 · Cited by 92 · see citing works
A transferable active-learning strategy for reactive molecular force fields
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Active learning strategies for atomic cluster expansion models
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Modelling chemical processes in explicit solvents with machine learning potentials
Zhang, H., Jurásková, V., & Duarte, F. (2024) · Nature Communications · doi:10.1038/s41467-024-50418-6 · Cited by 87 · see citing works
Zhao, J., Byggmästar, J., He, H., Nordlund, K., Djurabekova, F., & Hua, M. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01117-1 · Cited by 81 · see citing works
Schaaf, L. L., Fako, E., De, S., Schäfer, A., & Cśanyi, G. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01124-2 · Cited by 86 · see citing works
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Self-citationMagdău, I.-B., Arismendi‐Arrieta, D. J., Smith, H. E., Grey, C. P., Hermansson, K., & Cśanyi, G. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01100-w · Cited by 72 · see citing works
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Agarwal, G., Doan, H. A., Robertson, L. A., Zhang, L., & Assary, R. S. (2021) · Chemistry of Materials · doi:10.1021/acs.chemmater.1c02040 · Cited by 68 · see citing works
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Actinide Molten Salts: A Machine-Learning Potential Molecular Dynamics Study
Nguyen, M., Rousseau, R. J., Paviet, P. D., & Glezakou, V. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c11358 · Cited by 37 · see citing works
Meng, F.-S., Du, J.-P., Shinzato, S., Mori, H., Yu, P., Matsubara, K., Ishikawa, N., & Ogata, S. (2021) · Physical Review Materials · doi:10.1103/physrevmaterials.5.113606 · Cited by 37 · see citing works
Crystal structure prediction at finite temperatures
Kruglov, I. A., Yanilkin, A. V., Propad, Y., Mazitov, A., Rachitskii, P., & Оганов, А. Р. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01120-6 · Cited by 36 · see citing works
Yoo, D., Jung, J., Jeong, W., & Han, S. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00595-5 · Cited by 34 · see citing works
Shuaibi, M., Sivakumar, S., Chen, R. Q., & Ulissi, Z. W. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abcc44 · Cited by 33 · see citing works
Lin, S., Casillas‐Trujillo, L., Tasnádi, F., Hultman, L., Mayrhofer, P. H., Sangiovanni, D. G., & Koutná, N. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01252-3 · Cited by 32 · see citing works
Ding, J., Du, M., Wang, S., Zhang, L., Yue, Y., & Smedskjær, M. M. (2025) · Energy & Environmental Science · doi:10.1039/d4ee04566a · Cited by 31 · see citing works
Krishnamoorthy, A. N., Wölke, C., Diddens, D., Maiti, M., Mabrouk, Y., Yan, P., Grünebaum, M., Winter, M., Heuer, A., & Cekić-Lasković, I. (2022) · Chemistry - Methods · doi:10.1002/cmtd.202200008 · Cited by 30 · see citing works
Self-citationZhang, H., Gu, X., Fan, Z., & Bao, H. (2023) · Physical review. B./Physical review. B · doi:10.1103/physrevb.108.045422 · Cited by 29 · see citing works
Machine Learning Force Fields in Electrochemistry: From Fundamentals to Applications
Jinnouchi, R., & Minami, S. (2025) · ACS Nano · doi:10.1021/acsnano.5c05553 · Cited by 32 · see citing works
Atomistic simulations of nuclear fuel UO 2 with machine learning interatomic potentials
Dubois, E. T., Tranchida, J., Bouchet, J., & Maillet, J. (2024) · Physical Review Materials · doi:10.1103/physrevmaterials.8.025402 · Cited by 27 · see citing works
Uncertainty-aware predictions of molecular x-ray absorption spectra using neural network ensembles
Ghose, A. K., Segal, M., Meng, F., Liang, Z., Hybertsen, M. S., Qu, X., Stavitski, E., Yoo, S., Lu, D., & Carbone, M. R. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.013180 · Cited by 27 · see citing works
Speybroeck, V. V. (2023) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2022.0239 · Cited by 29 · see citing works
Mechanism of Charge Transport in Lithium Thiophosphate
Gigli, L., Tisi, D., Grasselli, F., & Ceriotti, M. (2024) · Chemistry of Materials · doi:10.1021/acs.chemmater.3c02726 · Cited by 27 · see citing works
Generating Minimal Training Sets for Machine Learned Potentials
Finkbeiner, J., Tovey, S., & Holm, C. (2024) · Physical Review Letters · doi:10.1103/physrevlett.132.167301 · Cited by 25 · see citing works
Self-citationPhase stability of Au-Li binary systems studied using neural network potential
Shimizu, K., Arguelles, E. F., Li, W., Ando, Y., Minamitani, E., & Watanabe, S. (2021) · Physical review. B./Physical review. B · doi:10.1103/physrevb.103.094112 · Cited by 25 · see citing works
Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment
Matin, S., Allen, A. E. A., Smith, J. S., Lubbers, N., Jadrich, R. B., Messerly, R. A., Nebgen, B. T., Li, Y. W., Tretiak, S., & Barros, K. M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01051 · Cited by 24 · see citing works
Thermophysical properties of Molten FLiNaK: A moment tensor potential approach
Rybin, N., Maksimov, D., Zaikov, Y. P., & Shapeev, A. V. (2024) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2024.125402 · Cited by 23 · see citing works
Guo, J., Ward, L. T., Babuji, Y., Hoyt, N. C., Williamson, M. A., Foster, I. T., Jackson, N. E., Benmore, C. J., & Sivaraman, G. (2022) · Physical review. B./Physical review. B · doi:10.1103/physrevb.106.014209 · Cited by 23 · see citing works
Self-citationMachine learning interatomic potential for silicon-nitride (Si3N4) by active learning
Milardovich, D., Wilhelmer, C., Waldhoer, D., Cvitkovich, L., Sivaraman, G., & Grasser, T. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0146753 · Cited by 23 · see citing works
Self-citationDesigning workflows for materials characterization
Kalinin, S. V., Ziatdinov, M. A., Ahmadi, M., Ghosh, A., Roccapriore, K. M., Liu, Y., & Vasudevan, R. K. (2024) · Applied Physics Reviews · doi:10.1063/5.0169961 · Cited by 24 · see citing works
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
Noordhoek, K., & Bartel, C. J. (2024) · Nanoscale · doi:10.1039/d3nr06468a · Cited by 23 · see citing works
Batch active learning for accelerating the development of interatomic potentials
Wilson, N., Willhelm, D., Qian, X., Arróyave, R., & Qian, X. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111330 · Cited by 22 · see citing works
Review: understanding the properties of amorphous materials with high-performance computing methods
Christie, J. K. (2023) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2022.0251 · Cited by 22 · see citing works
Efficient atomistic simulations of radiation damage in W and W–Mo using machine-learning potentials
Koskenniemi, M., Byggmästar, J., Nordlund, K., & Djurabekova, F. (2023) · Journal of Nuclear Materials · doi:10.1016/j.jnucmat.2023.154325 · Cited by 21 · see citing works
Wilhelmer, C., Waldhoer, D., Cvitkovich, L., Milardovich, D., Waltl, M., & Grasser, T. (2023) · Nanomaterials · doi:10.3390/nano13162286 · Cited by 22 · see citing works
Konstantinou, K., Mavračić, J., Mocanu, F. C., & Elliott, S. R. (2020) · physica status solidi (b) · doi:10.1002/pssb.202000416 · Cited by 21 · see citing works
From atomically resolved imaging to generative and causal models
Kalinin, S. V., Ghosh, A., Vasudevan, R. K., & Ziatdinov, M. A. (2022) · Nature Physics · doi:10.1038/s41567-022-01666-0 · Cited by 20 · see citing works
Polymers simulation using machine learning interatomic potentials
Long, T., Li, J., Wang, C., Wang, H., Cheng, X. L., Lu, H., Zhang, Y., & Zhou, C. (2024) · Polymer · doi:10.1016/j.polymer.2024.127416 · Cited by 20 · see citing works
Matsumura, N., Yoshimoto, Y., Yamazaki, T., Amano, T., Noda, T., Ebata, N., Kasano, T., & Sakai, Y. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01613 · Cited by 18 · see citing works
Laurens, G., Rabary, M., Lam, J., Peláez, D., & Allouche, A.-R. (2021) · Theoretical Chemistry Accounts · doi:10.1007/s00214-021-02773-6 · Cited by 18 · see citing works
Reducing the Cost of Neural Network Potential Generation for Reactive Molecular Systems
Březina, K., Beck, H., & Maršálek, O. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00391 · Cited by 20 · see citing works
Thermal transport of glasses via machine learning driven simulations
Pegolo, P., & Grasselli, F. (2024) · Frontiers in Materials · doi:10.3389/fmats.2024.1369034 · Cited by 18 · see citing works
A moment tensor potential for lattice thermal conductivity calculations of α and β phases of Ga2O3
Rybin, N., & Shapeev, A. V. (2024) · Journal of Applied Physics · doi:10.1063/5.0206215 · Cited by 18 · see citing works
Efficient learning of a one-dimensional density functional theory
Denner, M. M., Fischer, M. H., & Neupert, T. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033388 · Cited by 21 · see citing works
Guo, J., Woo, V., Andersson, D. A., Hoyt, N. C., Williamson, M. A., Foster, I. T., Benmore, C. J., Jackson, N. E., & Sivaraman, G. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0153021 · Cited by 17 · see citing works
Self-citationMolecular Dynamics Simulations in Nanoscale Heat Transfer: A Mini Review
Kim, J., Liu, Y., Luo, T., & Tian, Z. (2024) · ASME Journal of Heat and Mass Transfer · doi:10.1115/1.4067341 · Cited by 17 · see citing works
Stolte, N., Daru, J., Forbert, H., Marx, D., & Behler, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01382 · Cited by 18 · see citing works
Yi, J., Zhan, Y., Hu, Y., Zhao, S., & Li, H. (2026) · Physical Chemistry Chemical Physics · doi:10.1039/d6cp01826b · Cited by 16 · see citing works
Guo, Y.-X., Zhuang, Y., Shi, J., & Cheng, J. J. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0166858 · Cited by 16 · see citing works
Mathew, R., Mazumder, A., Kumar, P., Matula, J., Mohamed, S., Brázda, P., Hariharan, M., & Thomas, B. (2023) · Chemical Science · doi:10.1039/d3sc05514k · Cited by 16 · see citing works
Nanoscale Modeling of Surface Phenomena in Aluminum Using Machine Learning Force Fields
Chapman, J., & Ramprasad, R. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c05512 · Cited by 15 · see citing works
Delaizir, G., Alejandra, P. A., Pradel, A., Masson, O., & Bouzid, A. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp03383a · Cited by 15 · see citing works
Plutonium oxide melt structure and covalency
Wilke, S. K., Benmore, C. J., Alderman, O. L. G., Sivaraman, G., Ruehl, M., Hawthorne, K. L., Tamalonis, A., Andersson, D. A., Williamson, M. A., & Weber, R. J. (2024) · Nature Materials · doi:10.1038/s41563-024-01883-3 · Cited by 15 · see citing works
Self-citationResearch on Cu-Sn machine learning interatomic potential with active learning strategy
Liu, J., Zhang, G., Wang, J., Zhang, H., & Ye, H. (2024) · Computational Materials Science · doi:10.1016/j.commatsci.2024.113450 · Cited by 15 · see citing works
Primary radiation damage in silicon from the viewpoint of a machine learning interatomic potential
Hamedani, A., Byggmästar, J., Djurabekova, F., Alahyarizadeh, G., Ghaderi, R., Minuchehr, A., & Nordlund, K. (2021) · Physical Review Materials · doi:10.1103/physrevmaterials.5.114603 · Cited by 15 · see citing works
Zills, F., Schäfer, M. R., Segreto, N., Kästner, J., Holm, C., & Tovey, S. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c07187 · Cited by 15 · see citing works
Self-citationPhan, H. T., Tsou, P., Hsu, P., & Kuo, J. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp04411k · Cited by 13 · see citing works
Kang, K., Purcell, T. A. R., Carbogno, C., & Scheffler, M. (2025) · Physical Review Materials · doi:10.1103/physrevmaterials.9.063801 · Cited by 13 · see citing works
Structural and mechanical properties of monolayer amorphous carbon and boron nitride
Zhang, X., Zhang, Y., Wang, Y.-P., Du, S., Du, S., Zhang, Y., & Pantelides, S. T. (2024) · Physical review. B./Physical review. B · doi:10.1103/physrevb.109.174106 · Cited by 12 · see citing works
Tao, K., Wang, Z., Lao, Z., Chen, A., Han, Y., Shi, L., Zhou, G., & Li, J. (2024) · Energy storage materials · doi:10.1016/j.ensm.2024.103555 · Cited by 12 · see citing works
Sundar, A., Yu, J., Qi, L., & Cinbiz, M. N. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.07.123 · Cited by 12 · see citing works
Guo, R., Li, G., Tang, J., Wang, Y., & Song, X. (2023) · Carbon Trends · doi:10.1016/j.cartre.2023.100260 · Cited by 12 · see citing works
Sivaraman, G., Cśanyi, G., Vázquez‐Mayagoitia, Á., Foster, I. T., Wilke, S. K., Weber, R. J., & Benmore, C. J. (2022) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.91.091009 · Cited by 12 · see citing works
Self-citation
- Authors
- Sivaraman, G., Krishnamoorthy, A. N., Baur, M., Holm, C., Stan, M., Csányi, G., Benmore, C., & Vázquez-Mayagoitia, Á.
- Affiliations
- Argonne National Laboratory (US)
- University of Stuttgart (DE)
- Venue
- npj Computational Materials
- Published
- 2020-07-23
- Volume
- 6
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W2981603605
- Cited by
- 194
- References
- 57
- Citations by year
- 2026: 28 · 2025: 32 · 2024: 43 · 2023: 35 · 2022: 22 · 2021: 27 · 2020: 7
- FWCI
- 8.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Computational Materials (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (DE‐ AC02‐06CH11357, Contract No. DE-AC02-06CH11357., AC02‐06CH11357, contract DE-AC02-06CH11357, 06CH11357, No. DE-AC02-06CH11357, DE-AC02-)
- Deutsche Forschungsgemeinschaft (EXC2075390740016, DE-AC02-06CH11357, EXC 2075, 390740016)
- Office of Science (Contract No. DE-AC02-06CH11357, AC02‐06CH11357, DE-AC02, No. DE-AC02-06CH11357., 06CH11357, DE-AC02-06CH11357)
- Basic Energy Sciences (DE-AC02, DE-AC- 02-06CH11357, AC02‐06CH11357, Contract No. DE-AC02-06CH11357, 06CH11357)
- Laboratory Directed Research and Development (DE‐AC02‐06CH11357)
- Argonne National Laboratory (DE- AC02-06-CH11357, DE-AC02, AC02–06CH11357, Contract DE-AC02-06CH11357, No. DE-AC02-06CH11357, 06CH11357, Contract No. DE-AC02-06CH11357., under contract DE-AC02-06CH11357)
- 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 › Earth and Planetary Sciences › Geophysics › High-pressure geophysics and materials
- Keywords
- active learning (machine learning), Gaussian approximation potential, interatomic potentials, hafnium oxide, amorphous HfO2, ab initio molecular dynamics, Bayesian optimization, unsupervised learning, diffusion coefficient, machine learning interatomic potentials
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