用于原子建模的分层通用开放结构数据集
Hierarchical universal open structure dataset for atomistic modeling
AI总结：
该研究针对原子建模数据集的重叠与强相关问题，提出分层子采样协议，整合开放数据集构建MAD-COSMO，其核心集MAD-CORE可用于各类原子建模任务。
摘要：
原子尺度模拟已被机器学习模型改变，这些模型能以量子计算的精度预测原子结构的性质，成本却仅为其一小部分。得益于规模和范围不断扩大的合成数据集，这些模型的通用性大幅提升。但数据集的泛滥存在问题，因为不同数据集间常有重叠，且通过短分子动力学轨迹或几何优化得到的结构间存在强相关性。为解决这些问题，我们提出一种协议，将数据集分层划分为嵌套子集，基于反映几种流行通用机器学习原子间势潜在空间表示的“生态度量”，从最粗分辨率到最细分辨率进行子采样。随后我们用该方法整合了当前大部分可用的开放结构数据集，将其合并为MAD-COSMO，这是一个组织良好的集合，包含近17亿个结构，其中最多样化的2^24个成员构成MAD-CORE分层核心集，可轻松用于各类任务，从探索性实验到大规模基础模型训练。我们预计该集合将指导并协调未来工作，聚焦化学和结构空间中最相关和/或采样不足的区域，并将进一步扩展以纳入该社区集体工作的成果。
英文摘要：
Atomic-scale simulations have been transformed by machine-learning models that predict the properties of atomistic structures with the accuracy of quantum calculations, but at a small fraction of the cost. These models have increased dramatically in generality, thanks to the availability of synthetic datasets of ever-increasing size and scope. The proliferation of datasets, with properties often computed using different levels of electronic-structure approximation, is problematic, as there is often overlap between different datasets, and strong correlations between structures obtained with short molecular dynamics trajectories or geometry optimization. To address these problems, we introduce a protocol to stratify a dataset into a hierarchy of nested subsets, subsampling from the coarsest to the finest resolution based on an "ecumetric" built to reflect the latent space representation of several popular universal machine-learning interatomic potentials. We then use this approach to combine most of the open structural datasets currently available, consolidating them into MAD-COSMO, a well-organized collection of almost 1.7 billion structures, whose $2^{24}$ most diverse members form the MAD-CORE stratified coreset, which can be easily used for all sorts of tasks, from exploratory experiments to the training of foundation models at scale. We envision that this collection will guide and help coordinate future efforts, focusing them on the most relevant and/or undersampled regions of chemical and structural space, and that it will be further extended to incorporate the outcomes of the collective work of this community.
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