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Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations.
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Tamara Husch, Jiace Sun, Lixue Cheng, Sebastian J. R. Lee, Thomas F. Miller: Improved accuracy and transferability of molecular-orbital-based machine learning: organics, transition-metal complexes, non-covalent interactions, and transition states . arXiv 2010.03626, 2020
2020
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2020
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2020
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Ralf Drautz: Atomic cluster expansion of scalar, vectorial, and tensorial properties including magnetism and charge transfer . Physical Review B 102(2): 024104 , 2020
2020
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Jigyasa Nigam, Sergey Pozdnyakov, Michele Ceriotti: Recursive evaluation and iterative contraction of n n -body equivariant features . Journal of Chemical Physics 153(12): 121101 , 2020
2020
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Viktor Zaverkin, Johannes Kästner: Gaussian moments as physically inspired molecular descriptors for accurate and scalable machine learning potentials . Journal of Chemical Theory and Computation 16(8): 5410 , 2020
2020
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Sergey N. Pozdnyakov, Michael J. Willatt, Albert P. Bartók, Christoph Ortner, Gábor Csányi, Michele Ceriotti: Incompleteness of atomic structure representations . Physical Review Letters 125(16): 166001 , 2020
2020
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Ryosuke Jinnouchi, Ferenc Karsai, Carla Verdi, Ryoji Asahi, Georg Kresse: Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials . Journal of Chemical Physics 152(23) , 2020
2020
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Shreyas J. Honrao, Stephen R. Xie, Richard G. Hennig: Augmenting machine learning of energy landscapes with local structural information . Journal of Applied Physics 128(8): 085101 , 2020
2020
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Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker, Jörg Behler: A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer . arXiv 2009.06484, 2020
2020
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Behnam Parsaeifard, Jonas A. Finkler, Stefan Goedecker: Detecting non-local effects in the electronic structure of a simple covalent system with machine learning methods . arXiv 2008.11277, 2020
2020
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Julia Westermayr, Philipp Marquetand: Machine learning and excited-state molecular dynamics . Machine Learning: Science and Technology 1(4): 043001 , 2020
2020
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Julia Westermayr, Philipp Marquetand: Deep learning for UV absorption spectra with SchNarc: first steps toward transferability in chemical compound space . Journal of Chemical Physics 153(15): 154112 , 2020
2020
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Julia Westermayr, Michael Gastegger, Philipp Marquetand: Combining SchNet and SHARC: the SchNarc machine learning approach for excited-state dynamics . Journal of Physical Chemistry Letters 11(10): 3828 , 2020
2020
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Julia Westermayr, Felix A. Faber, Anders S. Christensen, O. Anatole von Lilienfeld, Philipp Marquetand: Neural networks and kernel ridge regression for excited states dynamics of CH 2 NH + 2 {}_{2}^{+} : from single-state to multi-state representations and multi-property machine learning models . Machine Learning: Science and Technology 1(2): 025009 , 2020
2020
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2020
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Zachary del Rosario, Matthias Rupp, Yoolhee Kim, Erin Antono, Julia Ling: Assessing the frontier: active learning, model accuracy, and multi-objective candidate discovery and optimization . Journal of Chemical Physics 153(2): 024112 , 2020
2020
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Christopher Sutton, Mario Boley, Luca M. Ghiringhelli, Matthias Rupp, Jilles Vreeken, Matthias Scheffler: Identifying domains of applicability of machine learning models for materials science . Nature Communications 11: 4428 , 2020
2020
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Lauri Himanen, Marc O.J. Jäger, Eiaki V. Morooka, Filippo Federici Canova, Yashasvi S. Ranawat, David Z. Gao, Patrick Rinke, Adam S. Foster: DScribe: library of descriptors for machine learning in materials science . Computer Physics Communications 247: 106949 , 2020
2020
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Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong: Performance and cost assessment of machine learning interatomic potentials . Journal of Physical Chemistry A 124(4): 731 , 2020
2020
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Berk Onat, Christoph Ortner, James R. Kermode: Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials . Journal of Chemical Physics 153(14): 144106 , 2020
2020
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Behnam Parsaeifard, Deb Sankar De, Anders S. Christensen, Felix A. Faber, Emir Kocer, Sandip De, Jörg Behler, Anatole von Lilienfeld, Stefan Goedecker: An assessment of the structural resolution of various fingerprints commonly used in machine learning . Machine Learning: Science and Technology in press , 2020
2020
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Silvan Käser, Debasish Koner, Anders S. Christensen, O. Anatole von Lilienfeld, Markus Meuwly: ML models of vibrating H 2 CO: Comparing reproducing kernels, FCHL and PhysNet . Journal of Physical Chemistry A 124(42): 8853 , 2020
2020
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Alexander Goscinski, Guillaume Fraux, Michele Ceriotti: The role of feature space in atomistic learning
2020
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Hyunwook Jung, Sina Stocker, Christian Kunkel, Harald Oberhofer, Byungchan Han, Karsten Reuter, Johannes T. Margraf: Size-extensive molecular machine learning with global representations . ChemSystemsChem 2(4): e1900052 , 2020
2020
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Jigyasa Nigam, Sergey Pozdnyakov, Michele Ceriotti: Recursive evaluation and iterative contraction of n n -body equivariant features . Journal of Chemical Physics 153(12): 121101 , 2020
2020
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2020
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Aldo Glielmo, Claudio Zeni, Ádám Fekete, Alessandro De Vita: Building nonparametric n n -body force fields using Gaussian process regression . In Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller (editors), Machine Learning Meets Quantum Physics
2020
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Ryosuke Jinnouchi, Ferenc Karsai, Carla Verdi, Ryoji Asahi, Georg Kresse: Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials . Journal of Chemical Physics 152(23) , 2020
2020
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Anders S. Christensen, Lars A. Bratholm, Felix A. Faber, O. Anatole von Lilienfeld: FCHL revisited: faster and more accurate quantum machine learning . Journal of Chemical Physics 152(4): 044107 , 2020
2020
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Félix Musil, Max Veit, Alexander Goscinski, Guillaume Fraux, Michael J. Willatt, Markus Stricker, Till Junge, Michele Ceriotti: Efficient implementation of atom-density representations . arXiv 2101.08814, 2021
2021
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