用生成模型设计新储氢材料，发现6种未报道的候选结构。
A generative machine learning model for designing metal hydrides applied to hydrogen storage
- 结合因果发现与轻量生成模型，从无到有设计新材料。
- 在450个样本上训练，生成1000个候选，筛选出6种新结构。
- 4种经理论验证，适合加速氢能材料研发的科研人员。
高效氢存储是实现碳中和能源系统的关键，但现有材料数据库（如Materials Project）中已知的金属氢化物数量有限，制约了最优材料的发现。本文提出一种框架，将因果发现与轻量级生成机器学习模型结合，生成当前数据库中不存在的新金属氢化物候选。基于包含450个样本（270个训练、90个验证、90个测试）的数据集，模型生成1000个候选材料。经过排序与筛选，识别出6种此前未报告的化学式与晶体结构，其中4种通过密度泛函理论（DFT）模拟验证，展现出良好的实验研究前景。整体框架具备可扩展性和高效率，可有效拓展氢能存储数据集，加速新材料发现进程。
原文摘要 · Abstract (English)
Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1,000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.
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