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Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes.
On the determination of molecular fields. – II. from the equation of state of a gas
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Are there pathways for protein folding?
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Dynamics of folded proteins
J Andrew McCammon, Bruce R Gelin, and Martin Karplus · 1977
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Polymorphic transitions in single crystals: A new molecular dynamics method
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Polymorphic transitions in single crystals: A new molecular dynamics method
M. Parrinello and A. Rahman · 1981
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Molecular dynamics of an α \alpha -helical polypeptide: Temperature dependence and deviation from harmonic behavior
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Knowledge-based protein secondary structure assignment
Dmitrij Frishman and Patrick Argos · 1995
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Toward reliable density functional methods without adjustable parameters: The PBE0 model
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Evolutionarily conserved pathways of energetic connectivity in protein families
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Stabilization of α \alpha -helices by dipole-dipole interactions within α \alpha -helices
Changmoon Park and William A Goddard · 2000
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α \alpha - and 3 10 3_{10} -helix interconversion: A quantum-chemical study on polyalanine systems in the gas phase and in aqueous solvent
Igor A Topol, Stanley K Burt, Eugen Deretey, Ting-Hua Tang, Andras Perczel, Alexander Rashin, and Imre G Csizmadia · 2001
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Polarizable force fields
Thomas A Halgren and Wolfgang Damm · 2001
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ReaxFF: A reactive force field for hydrocarbons
Adri CT Van Duin, Siddharth Dasgupta, Francois Lorant, and William A Goddard · 2001
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Molecular dynamics simulations of biomolecules
Martin Karplus and J Andrew McCammon · 2002
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Theoretical study of anion binding to calix[4]pyrrole: The effects of solvent, fluorine substitution, cosolute, and water traces
J. Ramón Blas, Manuel Márquez, Jonathan L. Sessler, F. Javier Luque, and Modesto Orozco · 2002
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PyMOL: An open-source molecular graphics tool
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Extreme stability of an unsolvated α \alpha -helix
Motoya Kohtani, Thaddeus C Jones, Jean E Schneider, and Martin F Jarrold · 2004
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Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy
Florian Weigend and Reinhart Ahlrichs · 2005
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Molecular dynamics simulations of the complete satellite tobacco mosaic virus
Peter L Freddolino, Anton S Arkhipov, Steven B Larson, Alexander McPherson, and Klaus Schulten · 2006
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Predicting crystal structure by merging data mining with quantum mechanics
Christopher C Fischer, Kevin J Tibbetts, Dane Morgan, and Gerbrand Ceder · 2006
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Three-dimensional structure of the water-insoluble protein crambin in dodecylphosphocholine micelles and its minimal solvent-exposed surface
Hee-Chul Ahn, Nenad Juranić, Slobodan Macura, and John L Markley · 2006
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Canonical sampling through velocity rescaling
Giovanni Bussi, Davide Donadio, and Michele Parrinello · 2007
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Polarizable force fields: History, test cases, and prospects
Arieh Warshel, Mitsunori Kato, and Andrei V Pisliakov · 2007
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Force field modeling of conformational energies: Importance of multipole moments and intramolecular polarization
Thomas D Rasmussen, Pengyu Ren, Jay W Ponder, and Frank Jensen · 2007
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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Canonical sampling through velocity rescaling
Giovanni Bussi, Davide Donadio, and Michele Parrinello · 2007
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Clustering by passing messages between data points
Brendan J Frey and Delbert Dueck · 2007
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Anton, a special-purpose machine for molecular dynamics simulation
David E Shaw, Martin M Deneroff, Ron O Dror, Jeffrey S Kuskin, Richard H Larson, John K Salmon, Cliff Young, Brannon Batson, Kevin J Bowers, Jack C Chao, et al · 2008
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GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation
Berk Hess, Carsten Kutzner, David Van Der Spoel, and Erik Lindahl · 2008
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Beyond point charges: Dynamic polarization from neural net predicted multipole moments
Michael G Darley, Chris M Handley, and Paul LA Popelier · 2008
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Scaling of multimillion-atom biological molecular dynamics simulation on a petascale supercomputer
Roland Schulz, Benjamin Lindner, Loukas Petridis, and Jeremy C Smith · 2009
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QM/MM methods for biomolecular systems
Hans Martin Senn and Walter Thiel · 2009
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Role of a salt bridge in the model protein crambin explored by chemical protein synthesis: X-ray structure of a unique protein analogue, [V15A]crambin- α \alpha -carboxamide
Duhee Bang, Valentina Tereshko, Anthony A Kossiakoff, and Stephen BH Kent · 2009
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Improved side-chain torsion potentials for the Amber ff99SB protein force field
Kresten Lindorff-Larsen, Stefano Piana, Kim Palmo, Paul Maragakis, John L. Klepeis, Ron O. Dror, and David E. Shaw · 2010
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Alchemical derivatives of reaction energetics
Daniel Sheppard, Graeme Henkelman, and O Anatole von Lilienfeld · 2010
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Improved side-chain torsion potentials for the Amber ff99SB protein force field
Kresten Lindorff-Larsen, Stefano Piana, Kim Palmo, Paul Maragakis, John L. Klepeis, Ron O. Dror, and David E. Shaw · 2010
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Unraveling the stability of polypeptide helices: Critical role of van der Waals interactions
Alexandre Tkatchenko, Mariana Rossi, Volker Blum, Joel Ireta, and Matthias Scheffler · 2011
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Predicting the effects of basepair mutations in DNA-protein complexes by thermodynamic integration
Frank R. Beierlein, G. Geoff Kneale, and Timothy Clark · 2011
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Open Babel: An open chemical toolbox
Noel M O’Boyle, Michael Banck, Craig A James, Chris Morley, Tim Vandermeersch, and Geoffrey R Hutchison · 2011
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Force fields and molecular dynamics simulations
MA González · 2011
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Accurate and efficient method for many-body van der Waals interactions
Alexandre Tkatchenko, Robert A DiStasio Jr, Roberto Car, and Matthias Scheffler · 2012
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Avogadro: an advanced semantic chemical editor, visualization, and analysis platform
Marcus D Hanwell, Donald E Curtis, David C Lonie, Tim Vandermeersch, Eva Zurek, and Geoffrey R Hutchison · 2012
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Fast and accurate modeling of molecular atomization energies with machine learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O Anatole Von Lilienfeld · 2012
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Computing vibrational spectra from ab initio molecular dynamics
Reactive atomistic simulations of Diels-Alder reactions: The importance of molecular rotations
Uxía Rivero, Oliver T Unke, Markus Meuwly, and Stefan Willitsch · 2019
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Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
Huziel E Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, and Alexandre Tkatchenko · 2019
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Quantum mechanics of proteins in explicit water: The role of plasmon-like solute-solvent interactions
Martin Stöhr and Alexandre Tkatchenko · 2019
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A general and adaptive robust loss function
Jonathan T Barron · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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Martin Thomas, Martin Brehm, Reinhold Fligg, Peter Vöhringer, and Barbara Kirchner · 2013
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Effect of guanine to inosine substitution on stability of canonical DNA and RNA duplexes: Molecular dynamics thermodynamics integration study
Miroslav Krepl, Michal Otyepka, Pavel Banáš, and Jiří Šponer · 2013
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Mature HIV-1 capsid structure by cryo-electron microscopy and all-atom molecular dynamics
Gongpu Zhao, Juan R Perilla, Ernest L Yufenyuy, Xin Meng, Bo Chen, Jiying Ning, Jinwoo Ahn, Angela M Gronenborn, Klaus Schulten, Christopher Aiken, et al · 2013
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Accuracy and tractability of a Kriging model of intramolecular polarizable multipolar electrostatics and its application to histidine
Shaun M Kandathil, Timothy L Fletcher, Yongna Yuan, Joshua Knowles, and Paul LA Popelier · 2013
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Machine learning of molecular electronic properties in chemical compound space
Grégoire Montavon, Matthias Rupp, Vivekanand Gobre, Alvaro Vazquez-Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus-Robert Müller, and O Anatole Von Lilienfeld · 2013
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Assessment and validation of machine learning methods for predicting molecular atomization energies
Katja Hansen, Grégoire Montavon, Franziska Biegler, Siamac Fazli, Matthias Rupp, Matthias Scheffler, O Anatole Von Lilienfeld, Alexandre Tkatchenko, and Klaus-Robert Müller · 2013
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Commentary: The materials project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, et al · 2013
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Targeted adversarial learning optimized sampling
Jun Zhang, Yi Isaac Yang, and Frank Noé · 2019
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Exhaustive state-to-state cross sections for reactive molecular collisions from importance sampling simulation and a neural network representation
Debasish Koner, Oliver T Unke, Kyle Boe, Raymond J Bemish, and Markus Meuwly · 2019
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Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
KT Schütt, Michael Gastegger, Alexandre Tkatchenko, K-R Müller, and Reinhard J Maurer · 2019
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Molecularrnn: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 2019
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 2019
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Generating valid Euclidean distance matrices
Moritz Hoffmann and Frank Noé · 2019
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Efficient multi-objective molecular optimization in a continuous latent space
Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, and Djork-Arné Clevert · 2019
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Pushing the limit of molecular dynamics with ab initio
Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Lin Lin, Roberto Car, E Weinan, and Linfeng Zhang · 2020
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Quantum machine learning using atom-in-molecule-based fragments selected on the fly
Bing Huang and O Anatole von Lilienfeld · 2020
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Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
Daniel Wrapp, Nianshuang Wang, Kizzmekia S. Corbett, Jory A. Goldsmith, Ching-Lin Hsieh, Olubukola Abiona, Barney S. Graham, and Jason S. McLellan · 2020
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Citizen scientists create an exascale computer to combat COVID-19
Maxwell I Zimmerman, Justin R Porter, Michael D Ward, Sukrit Singh, Neha Vithani, Artur Meller, Upasana L Mallimadugula, Catherine E Kuhn, Jonathan H Borowsky, Rafal P Wiewiora, et al · 2020
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High-dimensional potential energy surfaces for molecular simulations: From empiricism to machine learning
Oliver T Unke, Debasish Koner, Sarbani Patra, Silvan Käser, and Markus Meuwly · 2020
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Machine Learning Meets Quantum Physics , volume 968
Kristof T Schütt, Stefan Chmiela, O Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, and Klaus-Robert Müller · 2020
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Equivariant flows: Exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noé · 2020
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Improved protein structure prediction using potentials from deep learning
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T Jones, David Silver, Koray Kavukcuoglu, and Demis Hassabis · 2020
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Ab initio
David Pfau, James S Spencer, Alexander GDG Matthews, and W Matthew C Foulkes · 2020
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Deep-neural-network solution of the electronic Schrödinger equation
Jan Hermann, Zeno Schätzle, and Frank Noé · 2020
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A deep neural network for molecular wave functions in quasi-atomic minimal basis representation
M Gastegger, A McSloy, M Luya, KT Schütt, and RJ Maurer · 2020
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Machine learning the ropes: Principles, applications and directions in synthetic chemistry
Felix Strieth-Kalthoff, Frederik Sandfort, Marwin HS Segler, and Frank Glorius · 2020
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Machine learning for molecular simulation
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, and Cecilia Clementi · 2020
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Exploring chemical compound space with quantum-based machine learning
O Anatole von Lilienfeld, Klaus-Robert Müller, and Alexandre Tkatchenko · 2020
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Retrospective on a decade of machine learning for chemical discovery
O Anatole von Lilienfeld and Kieron Burke · 2020
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Machine learning for chemical discovery
Alexandre Tkatchenko · 2020
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A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
Tsz Wai Ko, Jonas A Finkler, Stefan Goedecker, and Jörg Behler · 2021
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Molecular basis for higher affinity of SARS‐CoV‐2 spike RBD for human ACE2 receptor
Julián M. Delgado, Nalvi Duro, David M. Rogers, Alexandre Tkatchenko, Sagar A. Pandit, and Sameer Varma · 2021
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Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
Huziel E Sauceda, Valentin Vassilev-Galindo, Stefan Chmiela, Klaus-Robert Müller, and Alexandre Tkatchenko · 2021
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Explaining deep neural networks and beyond: A review of methods and applications
Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J Anders, and Klaus-Robert Müller · 2021
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Four generations of high-dimensional neural network potentials
Jörg Behler · 2021
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Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Andrew J Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Highly accurate protein structure prediction for the human proteome
Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J Kleywegt, Alex Bateman, Richard Evans, Alexander Pritzel, Michael Figurnov, Olaf Ronneberger, Russ Bates, Simon A A Kohl, Anna Potapenko, Andrew J Ballard, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, David Reimann, Stig Petersen, Andrew W Senior, Koary Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper, and Demis Hassabis · 2021
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Machine learning of solvent effects on molecular spectra and reactions
Michael Gastegger, Kristof T Schütt, and Klaus-Robert Müller · 2021
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Machine learning for alloys
Gus LW Hart, Tim Mueller, Cormac Toher, and Stefano Curtarolo · 2021
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Ab initio
Bing Huang and O Anatole von Lilienfeld · 2021
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Combining machine learning and computational chemistry for predictive insights into chemical systems
John A Keith, Valentin Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus-Robert Müller, and Alexandre Tkatchenko · 2021
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Unsupervised learning methods for molecular simulation data
Aldo Glielmo, Brooke E Husic, Alex Rodriguez, Cecilia Clementi, Frank Noé, and Alessandro Laio · 2021
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Machine learning for chemical reactions
Markus Meuwly · 2021
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Machine learning for electronically excited states of molecules
Julia Westermayr and Philipp Marquetand · 2021
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Recent advances in first-principles based molecular dynamics
François Mouvet, Justin Villard, Viacheslav Bolnykh, and Ursula Röthlisberger · 2022
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How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?
Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, and Johannes Margraf · 2022
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Super-resolution in molecular dynamics trajectory reconstruction with bi-directional neural networks
Ludwig Winkler, Klaus-Robert Müller, and Huziel E Sauceda · 2022
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Inverse design of 3d molecular structures with conditional generative neural networks
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