基于在各种化学片段上训练的机器学习量子力学力场的生物分子动力学。
Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments.
作者信息
Unke Oliver T, Stöhr Martin, Ganscha Stefan, Unterthiner Thomas, Maennel Hartmut, Kashubin Sergii, Ahlin Daniel, Gastegger Michael, Medrano Sandonas Leonardo, Berryman Joshua T, Tkatchenko Alexandre, Müller Klaus-Robert
机构信息
Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland.
Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany.
出版信息
Sci Adv. 2024 Apr 5;10(14):eadn4397. doi: 10.1126/sciadv.adn4397.
Abstract
The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.
摘要
GEMS方法能够以从头算精度对大型异构系统进行分子动力学模拟。
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