AI 中文总结
研究非晶态材料对通用原子间势的挑战，引入基于精选数据集的基准框架，评估主流模型可转移性，识别局限并探究微调策略，为非晶功能材料领域应用及相关模型设计提供指导。
AI 中文摘要
预训练或‘基础’机器学习原子间势（MLIPs）如今广泛用于材料建模。早期预训练模型和基准主要聚焦有序晶体结构，其对非晶态固体的可转移性不明。本文基于对该领域当前主流模型的系统评估，表明非晶态是未来通用MLIPs的核心挑战。引入基于精选非晶系统参考数据集的基准框架及结构与属性验证。研究识别出当前许多预训练模型可转移性的局限并探究针对无序相的微调策略，结果有助于MLIPs在非晶功能材料快速发展领域的未来应用，为设计下一代训练数据集和可转移原子模型提供指导。
英文摘要
Pre-trained or 'foundational' machine-learned interatomic potentials (MLIPs) are now widely used in materials modelling. However, early pre-trained models and benchmarks have largely focused on ordered, crystalline structures, and their transferability to non-crystalline solids remains unclear. Here, we show that the amorphous state is indeed a central challenge for future universal MLIPs, based on a systematic evaluation of current mainstream models in this domain. We introduce a benchmarking framework built on a curated reference dataset of canonical amorphous systems, as well as validation for structures and properties. Our study identifies limitations in the transferability of many current pre-trained models and investigates fine-tuning strategies tailored to disordered phases. Together, our results can facilitate future applications of MLIPs in the fast-growing field of amorphous functional materials, and they provide guidance for designing next-generation training datasets and transferable atomistic models.
