@article{chen_multi-modal_2025,
	title = {A multi-modal transformer for predicting global minimum adsorption energy},
	volume = {16},
	issn = {2041-1723},
	doi = {10.1038/s41467-025-58499-7},
	abstract = {The fast assessment of the global minimum adsorption energy ({GMAE}) between catalyst surfaces and adsorbates is crucial for large-scale catalyst screening. However, multiple adsorption sites and numerous possible adsorption configurations for each surface/adsorbate combination make it prohibitively expensive to calculate the {GMAE} through density functional theory ({DFT}). Thus, we designed a multi-modal transformer called {AdsMT} to rapidly predict the {GMAE} based on surface graphs and adsorbate feature vectors without site-binding information. The {AdsMT} model effectively captures the intricate relationships between adsorbates and surface atoms through the cross-attention mechanism, hence avoiding the enumeration of adsorption configurations. Three diverse benchmark datasets were introduced, providing a foundation for further research on the challenging {GMAE} prediction task. Our {AdsMT} framework demonstrates excellent performance by adopting the tailored graph encoder and transfer learning, achieving mean absolute errors of 0.09, 0.14, and 0.39 {eV}, respectively. Beyond {GMAE} prediction, {AdsMT}'s cross-attention scores showcase the interpretable potential to identify the most energetically favorable adsorption sites. Additionally, uncertainty quantification was integrated into our models to enhance the trustworthiness of the predictions.},
	pages = {3232},
	number = {1},
	journaltitle = {Nature Communications},
	shortjournal = {Nat Commun},
	author = {Chen, Junwu and Huang, Xu and Hua, Cheng and He, Yulian and Schwaller, Philippe},
	date = {2025-04-04},
	pmid = {40185724},
	pmcid = {PMC11971357},
}