[论文解读] A foundation model for atomistic materials chemistry

Ilyes Batatia, Philipp Benner+86 more|arXiv (Cornell University)|Dec 29, 2023

Machine Learning in Materials Science被引用 236

一句话总结

介绍了 MACE-MP-0，一种基础机器学习原子间势，能够开箱即用地在多样的原子尺度系统中工作，并可用有限数据进行微调以达到从头计算精度。

ABSTRACT

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early ML force fields have largely been limited by: (i) the substantial computational and human effort of developing and validating potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of 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 of problems in the physical sciences, including 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 as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users get reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step towards democratising the revolution in atomic-scale modeling that has been brought about by ML force fields.

研究动机与目标

• 开发一个用于固体、液体和气体的通用 ML 原子间势模型，能够从单一预训练模型中对多样化化学性质进行建模。
• 证明一个中等规模的公开数据集可以训练出具有广泛可迁移性的基础模型。
• 展示通过少量配置的微调即可为新系统实现从头计算级的精度。
• 说明该模型在水、催化和 MOF 方面的适用性，以及其他基准属性。

提出的方法

• 采用将原子簇展开与等变图神经网络相结合的 MACE 架构。
• 引入高体秩等变特征（4-体）以降低所需的信息传递深度。
• 使用张量分解来高效参数化高阶特征。
• 在 MPtrj 公开数据集上进行训练，并在多样化系统上评估稳定性和精度。
• 呈现多种模型变体（MACE-MP-0、MACE-MP-0b3、MACE-MP-0b3+ D3），并在基准任务上报告表现。

实验结果

研究问题

• RQ1 单个 MLIP 基础模型是否能够在整个元素周期表范围内准确描述固体、液体和气体？
• RQ2 基础模型在多大程度上可以对域外系统进行泛化，并仍然提供稳定的 MD 轨迹？
• RQ3 在有限数据下进行微调在实现特定应用的定量从头计算精度方面有多有效？

主要发现

• MACE-MP-0 在广泛的材料和分子上表现出稳定的 MD，并具有定性且有时定量的精度。
• 开箱即用在水、冰、界面和 MOF 上的表现与参考方法和实验数据相比显示出有意义的一致性。
• 通过多头回放协议用少量配置进行微调可显著降低力误差，并且能够在目标任务上达到从头计算级的精度。
• 该模型对域外催化任务具有迁移能力，并且在有针对性的微调下，在许多情况下与 DFT NEB 计算结果达到极佳一致性。
• 基础模型使对大型化学空间（例如 91 MOF-74 类似物）的高吞吐探索成为可能，具有可观的 MD 时间尺度。