Jinnouchi Ryosuke, Miwa Kazutoshi, Karsai Ferenc, Kresse Georg, Asahi Ryoji
Toyota Central R&D Laboratories., Inc., Aichi 480-1192, Japan.
VASP Software GmbH, Sensengasse 8/16, 1090 Vienna, Austria.
J Phys Chem Lett. 2020 Sep 3;11(17):6946-6955. doi: 10.1021/acs.jpclett.0c01061. Epub 2020 Aug 12.
The on-the-fly generation of machine-learning force fields by active-learning schemes attracts a great deal of attention in the community of atomistic simulations. The algorithms allow the machine to self-learn an interatomic potential and construct machine-learned models on the fly during simulations. State-of-the-art query strategies allow the machine to judge whether new structures are out of the training data set or not. Only when the machine judges the necessity of updating the data set with the new structures are first-principles calculations carried out. Otherwise, the yet available machine-learned model is used to update the atomic positions. In this manner, most of the first-principles calculations are bypassed during training, and overall, simulations are accelerated by several orders of magnitude while retaining almost first-principles accuracy. In this Perspective, after describing essential components of the active-learning algorithms, we demonstrate the power of the schemes by presenting recent applications.
通过主动学习方案动态生成机器学习力场在原子模拟领域引起了广泛关注。这些算法使机器能够自我学习原子间势，并在模拟过程中即时构建机器学习模型。先进的查询策略使机器能够判断新结构是否超出训练数据集。只有当机器判断需要用新结构更新数据集时，才会进行第一性原理计算。否则，使用现有的机器学习模型来更新原子位置。通过这种方式，在训练过程中大部分第一性原理计算被绕过，总体而言，模拟速度加快了几个数量级，同时几乎保留了第一性原理的精度。在这篇展望文章中，在描述了主动学习算法的基本组成部分之后，我们通过展示近期的应用来证明这些方案的强大之处。
