AdsMT: Multi-modal Transformer for Predicting Global Minimum Adsorption Energy

We built three Global Minimum Adsorption Energy (GMAE) benchmark datasets named OCD-GMAE, Alloy-GMAE and FG-GMAE from OC20-Dense, Catalysis Hub, and `functional groups' (FG)-dataset datasets through strict data cleaning, and each data point represents a unique combination of catalyst surface and adsorbate. These new benchmark datasets can be beneficial for future ML study on GMAE prediction. In addition, a similar data cleaning procedure was employed on the OC20 dataset to create a new dataset named OC20-LMAE, which comprises surface/adsorbate pairings along with their local minimum adsorption energies (LMAE). The OC20-LMAE dataset contains 363,937 data points and serves as an effective resource for model pretraining.我们通过严格的数据清洗流程，从OC20-Dense、Catalysis Hub以及官能团（FG）数据集（functional groups'-dataset）中构建了三款全局最低吸附能（Global Minimum Adsorption Energy, GMAE）基准数据集，分别命名为OCD-GMAE、Alloy-GMAE与FG-GMAE。每条数据点均对应一组独一无二的催化剂表面与吸附质组合。上述新型基准数据集可为后续开展GMAE预测相关的机器学习研究提供重要支撑。此外，我们针对OC20数据集采用了同款数据清洗流程，构建了一款名为OC20-LMAE的新型数据集，该数据集收录了表面/吸附质配对及其对应的局部最低吸附能（Local Minimum Adsorption Energy, LMAE）。OC20-LMAE数据集共计包含363,937条数据点，可作为模型预训练的优质资源。