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
  • [31] End-to-end differentiable learning of turbulence models from indirect observations
    Carlos A.Michelén Str?fer
    Heng Xiao
    Theoretical & Applied Mechanics Letters, 2021, 11 (04) : 205 - 212
  • [32] Internet end-to-end delay dynamics
    Zhu Changhua
    Journal of Systems Engineering and Electronics, 2006, (03) : 685 - 691
  • [33] End-to-end Internet packet dynamics
    Network Research Group, Lawrence Berkeley National Laboratory, University of California at Berkeley, Berkeley, CA 94720, United States
    IEEE ACM Trans Networking, 3 (277-292):
  • [34] End-to-End Semi-supervised Learning for Differentiable Particle Filters
    Wen, Hao
    Chen, Xiongjie
    Papagiannis, Georgios
    Hu, Conghui
    Li, Yunpeng
    2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021), 2021, : 5825 - 5831
  • [35] partial derivativePV: An end-to-end differentiable solar-cell simulator
    Mann, Sean
    Fadel, Eric
    Schoenholz, Samuel S.
    Cubuk, Ekin D.
    Johnson, Steven G.
    Romano, Giuseppe
    COMPUTER PHYSICS COMMUNICATIONS, 2022, 272
  • [36] Toward the end-to-end optimization of particle physics instruments with differentiable programming
    Dorigo T.
    Giammanco A.
    Vischia P.
    Aehle M.
    Bawaj M.
    Boldyrev A.
    de Castro Manzano P.
    Derkach D.
    Donini J.
    Edelen A.
    Fanzago F.
    Gauger N.R.
    Glaser C.
    Baydin A.G.
    Heinrich L.
    Keidel R.
    Kieseler J.
    Krause C.
    Lagrange M.
    Lamparth M.
    Layer L.
    Maier G.
    Nardi F.
    Pettersen H.E.S.
    Ramos A.
    Ratnikov F.
    Röhrich D.
    de Austri R.R.
    del Árbol P.M.R.
    Savchenko O.
    Simpson N.
    Strong G.C.
    Taliercio A.
    Tosi M.
    Ustyuzhanin A.
    Zaraket H.
    Reviews in Physics, 2023, 10
  • [37] Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection
    Sen, Priyanka
    Saffari, Amir
    Oliya, Armin
    2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021), 2021, : 8805 - 8812
  • [38] An End-to-End Differentiable Framework for Contact-Aware Robot Design
    Xu, Jie
    Chen, Tao
    Zlokapa, Lara
    Foshey, Michael
    Matusik, Wojciech
    Sueda, Shinjiro
    Agrawal, Pulkit
    ROBOTICS: SCIENCE AND SYSTEM XVII, 2021,
  • [39] End-to-end differentiable learning of turbulence models from indirect observations
    Strofer, Carlos A. Michelen
    Xiao, Heng
    THEORETICAL AND APPLIED MECHANICS LETTERS, 2021, 11 (04)
  • [40] Automatic differentiable nonequilibrium Green's function formalism: An end-to-end differentiable quantum transport simulator
    Zhouyin, Zhanghao
    Chen, Xiang
    Zhang, Peng
    Wang, Jun
    Wang, Lei
    PHYSICAL REVIEW B, 2023, 108 (19)
12345