构建3.3万种能源材料数据库，用AI加速新材料发现
Energy-GNoME: A Living Database of Selected Materials for Energy Applications
- 基于结构与成分特征，用机器学习筛选候选材料
- 覆盖33,000种潜在能源材料，预测zT、带隙等关键性能
- 适合实验和计算化学家快速定位高潜力新材料
人工智能正推动能源材料的发现。基于GNoME协议识别出超过38万种新稳定晶体，从中筛选出超过3.3万种具能源应用潜力的材料，构建Energy-GNoME数据库。利用机器学习与深度学习工具，通过特征空间缓解跨领域数据偏差，识别热电材料、新型电池正极及钙钛矿的候选物。结合结构与成分特征的分类器确定适用范围，提升回归模型精度。这些回归模型用于预测关键性能参数，如热电优值（zT）、带隙（Eg）和正极电压（ΔV_c）。该方法大幅缩小候选范围，为实验与计算化学研究提供高效指引，加速电力生成、存储与转换材料的发现。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we identify over 33,000 materials with potential as energy materials forming the Energy-GNoME database. Leveraging Machine Learning (ML) and Deep Learning (DL) tools, our protocol mitigates cross-domain data bias using feature spaces to identify potential candidates for thermoelectric materials, novel battery cathodes, and novel perovskites. Classifiers with both structural and compositional features identify domains of applicability, where we expect enhanced accuracy of the regressors. Such regressors are trained to predict key materials properties like, thermoelectric figure of merit (zT), band gap (Eg), and cathode voltage ($ΔV_c$). This method significantly narrows the pool of potential candidates, serving as an efficient guide for experimental and computational chemistry investigations and accelerating the discovery of materials suited for electricity generation, energy storage and conversion.
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