End-to-End Differentiable Reactive Molecular Dynamics Simulations Using JAX

被引：0
|
作者：
Kaymak, Mehmet Cagri [1 ]
Schoenholz, Samuel S. [4 ]
Cubuk, Ekin D. [3 ]
O’Hearn, Kurt A. [1 ]
Merz Jr, Kenneth M. [2 ]
Aktulga, Hasan Metin [1 ]
机构：
[1] Department of Computer Science and Engineering, Michigan State University, East Lansing,MI,48824, United States
[2] Department of Chemistry, Michigan State University, East Lansing,MI,48824, United States
[3] Google Research, Mountain View,CA, United States
[4] OpenAI, San Francisco,CA, United States
来源：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2023年 / 13948 LNCS卷
关键词：
Compendex;
D O I：
38th International Conference on High Performance Computing, ISC High Performance 2023
中图分类号：
学科分类号：
摘要：
Graphics processing unit
引用
收藏
页码：202 / 219
相关论文
共 50 条
  • [1] End-to-End Differentiable Proving
    Rocktaschel, Tim
    Riedel, Sebastian
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), 2017, 30
  • [2] End-to-end differentiable construction of molecular mechanics force fields
    Wang, Yuanqing
    Fass, Josh
    Kaminow, Benjamin
    Herr, John E.
    Rufa, Dominic
    Zhang, Ivy
    Pulido, Ivan
    Henry, Mike
    Macdonald, Hannah E. Bruce
    Takaba, Kenichiro
    Chodera, John D.
    CHEMICAL SCIENCE, 2022, 13 (41) : 12016 - 12033
  • [3] End-to-End Differentiable Physics for Learning and Control
    Belbute-Peres, Filipe de A.
    Smith, Kevin A.
    Allen, Kelsey R.
    Tenenbaum, Joshua B.
    Kolter, J. Zico
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
  • [4] End-to-End Differentiable Learning of Protein Structure
    AlQuraishi, Mohammed
    CELL SYSTEMS, 2019, 8 (04) : 292 - +
  • [5] Differentiable MPC for End-to-end Planning and Control
    Amos, Brandon
    Rodriguez, Ivan Dario Jimenez
    Sacks, Jacob
    Boots, Byron
    Kolter, J. Zico
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
  • [6] End-to-End Differentiable Adversarial Imitation Learning
    Baram, Nir
    Anschel, Oron
    Caspi, Itai
    Mannor, Shie
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 70, 2017, 70
  • [7] End-to-End Differentiable GANs for Text Generation
    Kumar, Sachin
    Tsvetkov, Yulia
    NEURIPS WORKSHOPS, 2020, 2020, 137 : 118 - 128
  • [8] DiffML: End-to-end Differentiable ML Pipelines
    Hilprecht, Benjamin
    Hammacher, Christian
    Reis, Eduardo
    Abdelaal, Mohamed
    Binnig, Carsten
    PROCEEDINGS OF THE SEVENTH WORKSHOP ON DATA MANAGEMENT FOR END-TO-END MACHINE LEARNING, DEEM, 2023,
  • [9] End-to-end beam dynamics simulations of the ISF driver Linac
    Zhao, Q.
    Doleans, M.
    Grimm, T. L.
    Marti, F.
    Schriber, S. O.
    Wu, X.
    York, R. C.
    2007 IEEE PARTICLE ACCELERATOR CONFERENCE, VOLS 1-11, 2007, : 2996 - 2998
  • [10] Athena end-to-end simulations
    Wilms, Joern
    Brand, Thorsten
    Barret, Didier
    Beuchert, Tobias
    den Herder, Jan-Willem
    Kreykenbohm, Ingo
    Lotti, Simone
    Meidinger, Norbert
    Nandra, Kirpal
    Peille, Philippe
    Piro, Luigi
    Rau, Arne
    Schmid, Christian
    Smith, Randall K.
    Tenzer, Christoph
    Wille, Michael
    Willingale, Richard
    SPACE TELESCOPES AND INSTRUMENTATION 2014: ULTRAVIOLET TO GAMMA RAY, 2014, 9144
12345