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（functional groups, FG）数据集构建了三个全局最低吸附能（Global Minimum Adsorption Energy, GMAE）基准数据集，分别命名为OCD-GMAE、Alloy-GMAE与FG-GMAE，每个数据点均代表催化剂表面与吸附质的唯一组合。这些全新的基准数据集可助力未来针对GMAE预测的机器学习研究。此外，我们对OC20数据集采用了相似的数据清洗流程，构建了名为OC20-LMAE的新数据集，该数据集包含表面/吸附质对及其局部最低吸附能（Local Minimum Adsorption Energy, LMAE）。OC20-LMAE数据集共包含363,937个数据点，可作为模型预训练的有效资源。