多物种组成中原子能量的高效准确机器学习插值
Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
摘要：
用于原子模拟的机器学习势（MLP）是传统经典势的一个有前途的替代方案。目前的方法依赖于局部原子环境的描述符，其尺寸随着化学物种的数量呈二次方增加。在这篇文章中，我们证明了这种缩放在实践中可以避免。我们表明，一个数学上简单，计算效率高的描述符与恒定的复杂性是足够的，以代表过渡金属氧化物的组合物和生物分子含有11个化学物种的精度约为3毫电子伏/原子。这一见解消除了MLP效用的感知界限，并为研究具有十多种化学物质的以前无法访问的材料的物理学铺平了道路。
英文摘要：
Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase quadratically with the number of chemical species. In this article, we demonstrate that such a scaling can be avoided in practice. We show that a mathematically simple and computationally efficient descriptor with constant complexity is sufficient to represent transition-metal oxide compositions and biomolecules containing 11 chemical species with a precision of around 3 meV/atom. This insight removes a perceived bound on the utility of MLPs and paves the way to investigate the physics of previously inaccessible materials with more than ten chemical species.
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