Beck Thomas L, Carloni Paolo, Asthagiri Dilipkumar N
National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.
INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany.
J Chem Theory Comput. 2024 Mar 12;20(5):1777-1782. doi: 10.1021/acs.jctc.3c01276. Epub 2024 Feb 21.
Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.
百亿亿次超级计算机借助人工智能/机器学习技术，开启了动态模拟的大门，能够以前所未有的长度和时间尺度对生物分子运动进行建模。这一新能力有可能彻底改变我们对基本生物过程的理解。在此，我们报告在意大利比萨举行的最近一次CECAM研讨会上讨论的一些主要进展，该研讨会主题主要聚焦于原子级模拟。首先，我们重点介绍当前大规模生物分子模拟的实例以及跨越百亿亿次门槛所带来的未来可能性。接下来，我们讨论在优化这些强大资源的使用方面需要克服的挑战。最后，我们列出几个可以用这种新计算机架构进行研究的重大挑战性问题作为结尾。
