Machine-learned potentials for next-generation matter simulations-Reference-Cited by-同舟云学术

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Machine-learned potentials for next-generation matter simulations

Published:2021-05-27 Issue:6 Volume:20 Page:750-761
ISSN:1476-1122
Container-title:Nature Materials
language:en
Short-container-title:Nat. Mater.

Author:

Friederich Pascal^ORCID,Häse Florian^ORCID,Proppe Jonny^ORCID,Aspuru-Guzik Alán^ORCID

Funder

EC | Horizon 2020 Framework Programme

Herchel Smith Graduate Fellowship Jacques-Emile Dubois Student Dissertation Fellowship

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Canadian Institute for Advanced Research (CIFAR) Canada 150 Research Chair Program Dr. Anders G. Frøseth

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science,General Chemistry

Link

http://www.nature.com/articles/s41563-020-0777-6.pdf

Reference154 articles.

1. Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).

2. Tabor, D. P. et al. Accelerating the discovery of materials for clean energy in the era of smart automation. Nat. Rev. Mater. 3, 5–20 (2018).

3. Popova, M., Isayev, O. & Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 4, eaap7885 (2018).

4. Car, R. & Parrinello, M. Unified approach for molecular dynamics and density-functional theory. Phys. Rev. Lett. 55, 2471–2474 (1985).

5. Payne, M. C., Teter, M. P., Allan, D. C., Arias, T. A. & Joannopoulos, J. D. Iterative minimization techniques for ab initio total-energy calculations: molecular dynamics and conjugate gradients. Rev. Mod. Phys. 64, 1045–1097 (1992).

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