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Generating a data set that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine learned interatomic potentials (MLIP).
Force fields for silicas and aluminophosphates based on ab initio calculations
Beest, B. v., Kramer, G. J., Van Beest, B. W. H., Kramer, G. J., and Van Santen, R. A · 1955
Earlier work this paper cites.
Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling
Torrie, G. M. and Valleau, J. P · 1977
Earlier work this paper cites.
Computer “experiment” for nonlinear thermodynamics of Couette flow
Evans, D. J · 1983
Earlier work this paper cites.
Density-functional approximation for the correlation energy of the inhomogeneous electron gas
Perdew, J. P · 1986
Earlier work this paper cites.
First-Principles Interatomic Potential of Silica Applied to Molecular Dynamics
Tsuneyuki, S., Tsukada, M., Aoki, H., and Matsui, Y · 1988
Earlier work this paper cites.
THE weighted histogram analysis method for free‐energy calculations on biomolecules. I. The method
Kumar, S., Rosenberg, J. M., Bouzida, D., Swendsen, R. H., and Kollman, P. A · 1992
Earlier work this paper cites.
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G. and Furthmüller, J · 1996
Earlier work this paper cites.
From ultrasoft pseudopotentials to the projector augmented-wave method
Kresse, G. and Joubert, D · 1999
Earlier work this paper cites.
Reaction coordinates of biomolecular isomerization
Bolhuis, P. G., Dellago, C., and Chandler, D · 2000
Earlier work this paper cites.
Conformational dynamics of an alanine dipeptide analog: An ab initio
Wei, D., Guo, H., and Salahub, D. R · 2001
Earlier work this paper cites.
From ab initio
Boese, A. D., Chandra, A., Martin, J. M. L., and Marx, D · 2003
Earlier work this paper cites.
Differentiable Molecular Simulations for Control and Learning
Wang, W., Axelrod, S., and Gómez-Bombarelli, R · 2003
Earlier work this paper cites.
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
Hirschfeld, L., Swanson, K., Yang, K., Barzilay, R., and Coley, C. W · 2005
Earlier work this paper cites.
Structural relaxation made simple
Bitzek, E., Koskinen, P., Gähler, F., Moseler, M., and Gumbsch, P · 2006
Earlier work this paper cites.
Structure and dynamics of the homologues series of alanine peptides: A joint molecular-dynamics/{NMR} study
Graf, J., Nguyen, P. H., Stock, G., and Schwalbe, H · 2007
Earlier work this paper cites.
Statistically optimal analysis of samples from multiple equilibrium states
Shirts, M. R. and Chodera, J. D · 2008
Earlier work this paper cites.
Gaussian Mixture Models
Reynolds, D · 2009
Earlier work this paper cites.
Heating and flooding: A unified approach for rapid generation of free energy surfaces
Chen, M., Cuendet, M. A., and Tuckerman, M. E · 2012
Earlier work this paper cites.
Construction of high-dimensional neural network potentials using environment-dependent atom pairs
Jose, K. V. J., Artrith, N., and Behler, J · 2012
Earlier work this paper cites.
The Adaptive Biasing Force Method: Everything You Always Wanted To Know but Were Afraid To Ask
Comer, J., Gumbart, J. C., Hénin, J., Lelièvre, T., Pohorille, A., and Chipot, C · 2015
Earlier work this paper cites.
Gaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation
Miao, Y., Feher, V. A., and McCammon, J. A · 2015
Earlier work this paper cites.
Extended Adaptive Biasing Force Algorithm. An On-the-Fly Implementation for Accurate Free-Energy Calculations
Fu, H., Shao, X., Chipot, C., and Cai, W · 2016
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Gal, Y. and Ghahramani, Z · 2016
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OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., Wiewiora, R. P., Brooks, B. R., and Pande, V. S · 2017
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The atomic simulation environment—a Python library for working with atoms
Hjorth Larsen, A., Jørgen Mortensen, J., Blomqvist, J., Castelli, I. E., Christensen, R., Dułak, M., Friis, J., Groves, M. N., Hammer, B., Hargus, C., Hermes, E. D., Jennings, P. C., Bjerre Jensen, P., Kermode, J., Kitchin, J. R., Leonhard Kolsbjerg, E., Kubal, J., Kaasbjerg, K., Lysgaard, S., Bergmann Maronsson, J., Maxson, T., Olsen, T., Pastewka, L., Peterson, A., Rostgaard, C., Schiøtz, J., Schütt, O., Strange, M., Thygesen, K. S., Vegge, T., Vilhelmsen, L., Walter, M., Zeng, Z., and Jacobsen, K. W · 2017
Cited alongside, same era.
Overcoming Free-Energy Barriers with a Seamless Combination of a Biasing Force and a Collective Variable-Independent Boost Potential
Chen, H., Fu, H., Chipot, C., Shao, X., and Cai, W · 2021
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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., and Tkatchenko, A · 2021
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Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer
Schran, C., Brieuc, F., and Marx, D · 2021
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Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
Schwalbe-Koda, D., Tan, A. R., and Gómez-Bombarelli, R · 2021
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Molecular dynamics study on the co-doping effect of Al2O3 and fluorine to reduce Rayleigh scattering of silica glass
Urata, S., Nakamura, N., Tada, T., and Hosono, H · 2021
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Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Cited alongside, same era.
Addressing uncertainty in atomistic machine learning
Peterson, A. A., Christensen, R., and Khorshidi, A · 2017
Cited alongside, same era.
Active learning of linearly parametrized interatomic potentials
Podryabinkin, E. V. and Shapeev, A. V · 2017
Cited alongside, same era.
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., and Walsh, A · 2018
Cited alongside, same era.
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
Deringer, V. L., Caro, M. A., Csányi, G., Csányi L Deringer, G. V., Csányi, G., Deringer, V. L., and Caro, M. A · 2019
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A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Janet, J. P., Duan, C., Yang, T., Nandy, A., and Kulik, H. J · 2019
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A systematic study of minima in alanine dipeptide
Mironov, V., Alexeev, Y., Mulligan, V. K., and Fedorov, D. G · 2019
Cited alongside, same era.
Active learning accelerates ab initio molecular dynamics on reactive energy surfaces
Ang, S. J., Wang, W., Schwalbe-Koda, D., Axelrod, S., and Gómez-Bombarelli, R · 2020
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Angelopoulos, A. N. and Bates, S · 2022
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Fu, X., Wu, Z., Wang, W., Xie, T., Keten, S., Gomez-Bombarelli, R., and Jaakkola, T · 2022
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Hu, Y., Musielewicz, J., Ulissi, Z., and Medford, A. J · 2022
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Statistically optimal analysis of the extended-system adaptive biasing force (eABF) method
Hulm, A., Dietschreit, J. C. B., and Ochsenfeld, C · 2022
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Thaler, S., Doehner, G., and Zavadlav, J · 2022
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Batatia, I., Kovács, D. P., Simm, G. N. C., Ortner, C., and Csányi, G · 2023
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Characterizing Uncertainty in Machine Learning for Chemistry
Heid, E., McGill, C. J., Vermeire, F. H., and Green, W. H · 2023
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Uncertainty-driven dynamics for active learning of interatomic potentials
Kulichenko, M., Barros, K., Lubbers, N., Li, Y. W., Messerly, R., Tretiak, S., Smith, J. S., and Nebgen, B · 2023
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How to validate machine-learned interatomic potentials
Morrow, J. D., Gardner, J. L. A., and Deringer, V. L · 2023
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Data efficiency and extrapolation trends in neural network interatomic potentials, April 2023
Vita, J. A. and Schwalbe-Koda, D · 2023
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Zaverkin, V., Holzmüller, D., Christiansen, H., Errica, F., Alesiani, F., Takamoto, M., Niepert, M., and Kästner, J · 2023
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Fast Uncertainty Estimates in Deep Learning Interatomic Potentials
Zhu, A., Batzner, S., Musaelian, A., and Kozinsky, B · 2023
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Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method
Oreluk, J., Liu, Z., Hegde, A., Li, W., Packard, A., Frenklach, M., and Zubarev, D · 2045
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Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles
Tan, A. R., Urata, S., Goldman, S., Dietschreit, J. C. B., and Gómez-Bombarelli, R · 2057
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Hyperactive learning for data-driven interatomic potentials
Van Der Oord, C., Sachs, M., Kovács, D. P., Ortner, C., and Csányi, G · 2057
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