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Related Experiment Video

Updated: May 16, 2025

Two-way Valorization of Blast Furnace Slag: Synthesis of Precipitated Calcium Carbonate and Zeolitic Heavy Metal Adsorbent

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A multi-modal transformer for predicting global minimum adsorption energy.

Junwu Chen^1,2, Xu Huang^1,3, Cheng Hua⁴

¹Laboratory of Artificial Chemical Intelligence (LIAC), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Nature Communications

|April 4, 2025

Summary

This summary is machine-generated.

Predicting catalyst performance is faster with AdsMT, a new AI model. It accurately estimates global minimum adsorption energy (GMAE) for catalyst screening, reducing computational costs.

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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Area of Science:

Materials Science
Computational Chemistry
Catalysis

Background:

Accurate prediction of global minimum adsorption energy (GMAE) is vital for catalyst screening.
Density functional theory (DFT) calculations are computationally expensive for determining GMAE due to numerous adsorption sites and configurations.

Purpose of the Study:

To develop a rapid and accurate method for predicting GMAE.
To overcome the computational limitations of traditional DFT methods for large-scale catalyst screening.

Main Methods:

Designed AdsMT, a multi-modal transformer model.
Utilized surface graphs and adsorbate feature vectors for prediction.
Employed a cross-attention mechanism to capture adsorbate-surface interactions without site-binding information.

Main Results:

Achieved low mean absolute errors of 0.09, 0.14, and 0.39 eV on three benchmark datasets.
AdsMT effectively predicts GMAE, significantly reducing computational cost.
Demonstrated interpretable potential of cross-attention scores for identifying favorable adsorption sites.

Conclusions:

AdsMT offers a computationally efficient and accurate approach for GMAE prediction.
The model facilitates large-scale catalyst screening.
Integrated uncertainty quantification enhances prediction reliability.