APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020;
Denver, Colorado
Session Index
Session P45: Emerging Trends in Molecular Dynamics Simulations and Machine Learning IV
Focus Session
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Sponsoring Units: DCOMP GDS DSOFT DPOLY
Chair: Maria Chan, Argonne Natl Lab
Room: 706
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P45.00001: Using Topological Constraints to Modify Polymer Materials
Invited Speaker:
Kurt Kremer
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P45.00002: Simpler is Better: How Linear Prediction Tasks Improve Transfer Learning in Chemical Autoencoders
Nick Iovanac, Brett Savoie
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P45.00003: Neural Network Based Molecular Dynamics to Study Polymers
Christopher Kuenneth, Ramamurthy Ramprasad
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P45.00004: Applications of Automatic Differentiation to Materials Design
Ella King, Carl Goodrich, Sam Schoenholz, Ekin Dogus Cubuk, Michael Phillip Brenner
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P45.00005: Trainable Molecular Dynamics Models
Carl Goodrich, Ella King, Samuel Schoenholz, Ekin Dogus Cubuk, Michael Phillip Brenner
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P45.00006: Hydrogen-Oxygen Combustion: Data-Driven Generation of Quantum-Accurate Interatomic Potentials
Allan Avila, Luke Bertels, Igor Mezic, Martin P Head-Gordon
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P45.00007: Toward optimal descriptors for accurate machine learning of flexible molecules
Valentin Vassilev Galindo, Igor Poltavskyi, Alexandre Tkatchenko
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P45.00008: Towards transferable parametrization of Density-Functional Tight-Binding with machine learning
Leonardo Medrano Sandonas, Martin Stoehr, Alexandre Tkatchenko
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P45.00009: Active learning of fast Bayesian force fields with mapped gaussian processes - application to stability of stanene
Yu Xie, Jonathan Vandermause, Lixin Sun, Andrea Cepellotti, Boris Kozinsky
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P45.00010: Nuclear quantum delocalization enhances non-covalent intramolecular interactions: A machine learning and path integral molecular dynamics study
Huziel Sauceda, Valentin Vassilev Galindo, Stefan Chmiela, Klaus-Robert Müller, Alexandre Tkatchenko
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P45.00011: Active learning identifies optimal π-conjugated peptide chemistries for optoelectronics
Kirill Shmilovich, Andrew L Ferguson
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P45.00012: A Self-consistent Artificial Neural Network Inter-atomic Potential for Li/C Systems
Yusuf Shaidu, Ruggero Lot, Franco Pellegrini, Emine Kucukbenli, Stefano de Gironcoli
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P45.00013: Active Learning Driven Machine Learning Inter-Atomic Potentials Generation: A Case Study for Hafnium dioxide
Ganesh Sivaraman, Anand Narayanan Krishnamoorthy, Matthias Baur, Christian L. Holm, Marius Stan, Gábor Csányi, Chris Benmore, Alvaro Vazquez-Mayagoitia
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