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Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary.
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Ab Initio QM/MM Study of the Citrate Synthase Mechanism. A Low-Barrier Hydrogen Bond Is not Involved
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Atom-centered Symmetry Functions for Constructing High-dimensional Neural Network Potentials
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The GROMOS++ Software for the Analysis of Biomolecular Simulation Trajectories
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Machine Learning of Accurate Energy-conserving Molecular Force Fields
S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schütt, K.-R. Müller · 2017
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ANI-1: An Extensible Neural Network Potential with DFT Accuracy at Force Field Computational Cost
J. S. Smith, O. Isayev, A. E. Roitberg · 2017
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SchNet: A Continuous-filter Convolutional Neural Network for Modeling Quantum Interactions, 2017
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First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems
J. Behler · 2017
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Accuracy Test of the OPLS-AA Force Field for Calculating Free Energies of Mixing and Comparison with PAC-MAC
A. J. M. Sweere, J. G. E. M. Farrije · 2017
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Architecture, Implementation and Parallelization of the GROMOS Software for Biomolecular Simulation
N. Schmid, C. D. Christ, M. Christen, A. P. Eichenberger, W. F. van Gunsteren · 2012
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Interfacing the GROMOS (Bio)molecular Simulation Software to Quantum-Chemical Program Packages
K. Meier, N. Schmid, W. F. van Gunsteren · 2012
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Introduction to QM/MM simulations
G. Groenhof · 2013
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Optimization of Parameters for Semiempirical Methods VI: More Modifications to the NDDO Approximations and Re-optimization of Parameters
J. J. P. Stewart · 2013
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Permutation Invariant Polynomial Neural Network Approach to Fitting Potential Energy Surfaces
B. Jiang, H. Guoa · 2013
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Permutation Invariant Polynomial Neural Network Approach to Fitting Potential Energy Surfaces. II. Four-atom Systems
J. Li, B. Jiang, H. Guoa · 2013
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Quantum Chemistry
I. N. Levine · 2013
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Deep Sets, 2017
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. Salakhutdinov, A. Smola · 2017
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Fixed-Charge Atomistic Force Fields for Molecular Dynamics Simulations in the Condensed Phase: An Overview
S. Riniker · 2018
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New Developments in Force Fields for Biomolecular Simulations
P. S. Nerenberg, T. Head-Gordon · 2018
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Software Update: The ORCA Program System, Version 4.0
F. Neese · 2018
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wACSF – Weighted Atom-centered Symmetry Functions as Descriptors in Machine Learning Potentials
M. Gastegger, L. Schwiedrzik, M. Bittermann, F. Berzsenyi, P. Marquetanda · 2018
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Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
S. Chmiela, H. E. Sauceda, K. R. Mueller, A. Tkatchenko · 2018
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Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks
L. Shen, W. Yang · 2018
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The Potential for Machine Learning in Hybrid QM/MM Calculations
Y. Zhang, A. Khorshidi, G. Kastlunger, A. A. Peterson · 2018
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Do Better Quality Embedding Potentials Accelerate the Convergence of QM/MM Models? The Case of Solvated Acid Clusters
H. Junming, Y. Shao, J. Kato · 2018
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Electron Density Learning of Non-covalent Systems
A. Fabrizio, A. Grisafi, B. Meyer, M. Ceriotti, C. Corminboeuf · 2019
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Schrodinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Space, 2019
J. Stevenson, L. D. Jacobson, Y. Zhao, C. Wu, J. Maple, K. Leswing, E. Harder, R. Abel · 2019
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Incorporating Long-range Physics in Atomic-scale Machine Learning
A. Grisafi, M. Ceriotti · 2019
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A Critical Comparison of Neural Network Potentials for Molecular Reaction Dynamics with Exact Permutation Symmetry
J. Li, K. Song, J. Behler · 2019
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Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression
C. Panosetti, A. Engelmann, L. Nemec, K. Reuter, J. T. Margraf · 2020
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