标题： 一种用于原子材料化学的基础模型 显示英文标题

标题： A foundation model for atomistic materials chemistry

摘要： 机器学习力场的出现彻底改变了材料的原子建模，使能在前所未有的时间和长度尺度上进行类似从头算质量的模拟。然而，它们目前受到以下限制：(i) 对于每个感兴趣的特定系统，必须投入大量的计算和人力来开发和验证势能；(ii) 缺乏从一个化学体系到下一个体系的普遍可转移性。在这里，我们利用最先进的MACE架构引入了一个单一的通用机器学习模型，该模型基于一个包含15万种无机晶体的公共数据库进行训练，能够对分子和材料运行稳定的分子动力学模拟。我们在物理科学的各种问题上展示了MACE-MP-0模型的力量——以及它的定性和有时定量的准确性——这些问题包括固体、液体、气体的性质、化学反应、界面甚至一个小蛋白的动力学。该模型可以开箱即用，并且可以用作任何感兴趣的原子系统的起点或“基础模型”，因此朝着通过降低进入门槛来民主化机器学习力场的革命迈出了一步。 显示英文摘要

摘要： Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on molecules and materials. We demonstrate the power of the MACE-MP-0 model - and its qualitative and at times quantitative accuracy - on a diverse set problems in the physical sciences, including the properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box and as a starting or "foundation model" for any atomistic system of interest and is thus a step towards democratising the revolution of ML force fields by lowering the barriers to entry.