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The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, has revolutionized the fields of computational chemistry and materials discovery.
Equation of State Calculations by Fast Computing Machines
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Computer ”Experiments” on Classical Fluids. I. Thermodynamical Properties of Lennard-Jones Molecules
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Exploiting the isomorphism between quantum theory and classical statistical mechanics of polyatomic fluids
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On the limited memory BFGS method for large scale optimization
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Reversible multiple time scale molecular dynamics
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Thermal Contraction and Disordering of the Al(110) Surface
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A climbing image nudged elastic band method for finding saddle points and minimum energy paths
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Escaping free-energy minima
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Structural Relaxation Made Simple
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Geometric numerical integration
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Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J. and Parrinello, M · 2007
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Accurate sampling using Langevin dynamics
Bussi, G. and Parrinello, M · 2007
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Canonical sampling through velocity rescaling
Bussi, G., Donadio, D., and Parrinello, M · 2007
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Efficient and Accurate Car-Parrinello-like Approach to Born-Oppenheimer Molecular Dynamics
Kühne, T. D., Krack, M., Mohamed, F. R., and Parrinello, M · 2007
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Stochastic thermostats: Comparison of local and global schemes
Bussi, G. and Parrinello, M · 2008
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Evaluating Derivatives
Griewank, A. and Walther, A · 2008
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Statistical Mechanics and Molecular Simulations
Tuckerman, M · 2008
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Isothermal-isobaric molecular dynamics using stochastic velocity rescaling
Bussi, G., Zykova-Timan, T., and Parrinello, M · 2009
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Langevin equation with colored noise for constant-temperature molecular dynamics simulations
Ceriotti, M., Bussi, G., and Parrinello, M · 2009
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Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., and Csányi, G · 2010
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Colored-noise thermostats à la Carte
Ceriotti, M., Bussi, G., and Parrinello, M · 2010
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High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
Artrith, N., Morawietz, T., and Behler, J · 2011
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Efficient multiple time scale molecular dynamics: Using colored noise thermostats to stabilize resonances
Morrone, J. A., Markland, T. E., Ceriotti, M., and Berne, B. J · 2011
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On representing chemical environments
Bartók, A. P., Kondor, R., and Csányi, G · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and Von Lilienfeld, O. A · 2014
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Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces
Li, Z., Kermode, J. R., and De Vita, A · 2015
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Accurate molecular dynamics and nuclear quantum effects at low cost by multiple steps in real and imaginary time: Using density functional theory to accelerate wavefunction methods
Kapil, V., VandeVondele, J., and Ceriotti, M · 2016
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Computer Simulation of Liquids , volume 1
Allen, M. P. and Tildesley, D. J · 2017
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The atomic simulation environment—a Python library for working with atoms
Unified theory of atom-centered representations and message-passing machine-learning schemes
Nigam, J., Pozdnyakov, S., Fraux, G., and Ceriotti, M · 2022
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A foundation model for atomistic materials chemistry
Batatia, I., Benner, P., Chiang, Y., Elena, A. M., Kovács, D. P., Riebesell, J., Advincula, X. R., Asta, M., Avaylon, M., Baldwin, W. J., et al · 2023
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Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling
Deng, B., Zhong, P., Jun, K., Riebesell, J., Han, K., Bartel, C. J., and Ceder, G · 2023
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A hitchhiker’s guide to geometric gnns for 3d atomic systems
Duval, A., Mathis, S. V., Joshi, C. K., Schmidt, V., Miret, S., Malliaros, F. D., Cohen, T., Lio, P., Bengio, Y., and Bronstein, M · 2023
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Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations
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Statistical Mechanics: International Series of Monographs in Natural Philosophy , volume 45
Pathria, R. K · 2017
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Towards exact molecular dynamics simulations with machine-learned force fields
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Ab initio thermodynamics of liquid and solid water
Cheng, B., Engel, E. A., Behler, J., Dellago, C., and Ceriotti, M · 2019
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Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
Wang, J., Olsson, S., Wehmeyer, C., Pérez, A., Charron, N. E., De Fabritiis, G., Noé, F., and Clementi, C · 2019
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On the role of gradients for machine learning of molecular energies and forces
Christensen, A. S. and von Lilienfeld, O. A · 2020
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Liao, Y.-L., Wood, B., Das, A., and Smidt, T · 2023
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Smooth, exact rotational symmetrization for deep learning on point clouds
Pozdnyakov, S. and Ceriotti, M · 2023
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The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts
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Probing the effects of broken symmetries in machine learning
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I-PI 3.0: A flexible and efficient framework for advanced atomistic simulations
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Orb: A fast, scalable neural network potential
Neumann, M., Gin, J., Rhodes, B., Bennett, S., Li, Z., Choubisa, H., Hussey, A., and Godwin, J · 2024
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Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations
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Physical consistency bridges heterogeneous data in molecular multi-task learning
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Improving machine-learning models in materials science through large datasets
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Protein conformation generation via force-guided SE(3) diffusion models
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Flashmd: long-stride, universal prediction of molecular dynamics
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How simple can you go? an off-the-shelf transformer approach to molecular dynamics, 2025
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Mazitov, A., Bigi, F., Kellner, M., Pegolo, P., Tisi, D., Fraux, G., Pozdnyakov, S., Loche, P., and Ceriotti, M · 2025
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B · 2041
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Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
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