Tuesday, March 6, 2018
11:15AM - 11:51AM
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F34.00001: Machine learning a dynamical phase diagram for many-body localization
Invited Speaker:
Evert Van Nieuwenburg
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Tuesday, March 6, 2018
11:51AM - 12:03PM
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F34.00002: Machine learning out-of-equilibrium phases of matter
Jordan Venderley, Vedika Khemani, Eun-Ah Kim
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Tuesday, March 6, 2018
12:03PM - 12:15PM
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F34.00003: Finite-Size Effects in Machine Learning the Kosterlitz-Thouless Transition
Anna Golubeva
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Tuesday, March 6, 2018
12:15PM - 12:27PM
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F34.00004: Machine Learning Vortices in the XY Model
Matthew Beach
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Tuesday, March 6, 2018
12:27PM - 12:39PM
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F34.00005: Machine Learning of Frustrated Classical Spin Models
Ce Wang, Hui Zhai
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Tuesday, March 6, 2018
12:39PM - 12:51PM
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F34.00006: Machine Learning the Spin-glass State
Humberto Munoz-Bauza, Firas Hamze, Helmut Katzgraber
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Tuesday, March 6, 2018
12:51PM - 1:03PM
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F34.00007: Extrapolating the properties of lattice polarons with Machine Learning
Rodrigo Alejandro Vargas-Hernández, John Sous, Mona Berciu, Roman Krems
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Tuesday, March 6, 2018
1:03PM - 1:15PM
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F34.00008: The dangers of inadvertently poisoned training sets in physics applications
Chao Fang, Helmut Katzgraber
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Tuesday, March 6, 2018
1:15PM - 1:27PM
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F34.00009: Identification of phase transtitions in molecular systems using unsupervised machine learning methods
Nicholas Walker, Ka-Ming Tam, Mark Jarrell
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Tuesday, March 6, 2018
1:27PM - 1:39PM
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F34.00010: Linear Scaling, Quantum-accurate Interatomic Potentials with SNAP; Acessing those Hard-to-reach Places in Classical Molecular Dynamics
Mitchell Wood, Aidan Thompson
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Tuesday, March 6, 2018
1:39PM - 1:51PM
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F34.00011: Learning Force Fields using Covariant Compositional Networks
Brandon Anderson, Risi Kondor, Horace Pan, Shubhendu Trivedi, Truong Son Hy
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Tuesday, March 6, 2018
1:51PM - 2:03PM
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F34.00012: Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
Stefan Chmiela, Huziel Sauceda, Klaus-Robert Müller, Alexandre Tkatchenko
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Tuesday, March 6, 2018
2:03PM - 2:15PM
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F34.00013: Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
Tristan Bereau, Robert Distasio, Alexandre Tkatchenko, O. Anatole Von Lilienfeld
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