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Li et al. J Mater Inf 2024;4:4 Journal of materials
DOI: 10.20517/jmi.2023.41 Informatics

Research Article Open Access

Local environment interaction-based machine learn-

ing framework for predicting molecular adsorption en-
ergy

Yifan Li, Yihan Wu, Yuhang Han, Qiujie Lyu, Hao Wu, Xiuying Zhang, Lei Shen

Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore.

Correspondence to: Dr. Lei Shen, Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1,
Singapore 117575, Singapore. E-mail: shenlei@nus.edu.sg

How to cite this article: Li Y, Wu Y, Han Y, Lyu Q, Wu H, Zhang X, Shen L. Local environment interaction-based machine learning
framework for predicting molecular adsorption energy. J Mater Inf 2024;4:4. http://dx.doi.org/10.20517/jmi.2023.41
Received: 30 Dec 2023 First Decision: 20 Feb 2024 Revised: 18 Mar 2024 Accepted: 27 Mar 2024 Published: 30 Mar
2024
Academic Editors: Xingjun Liu, Fengyu Li Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
Machine learning (ML) models in materials science are mainly developed for predicting global properties, such as for-
mation energy, band gap, and elastic modulus. Thus, these models usually fall short in describing local characteristics,
such as molecular adsorption on surfaces. Here, we introduce a local environment interaction-based ML framework
that contains a modified graph-based Voronoi tessellation geometrical representation, improved fingerprint feature
engineering, and traditional ML and advanced deep learning (DL) algorithms. The precise characterization can be
extracted using this framework for representing local information of adsorption of molecules on a surface. Using
both traditional ML and advanced DL algorithms, we demonstrate remarkable prediction accuracy and robustness
on 0D, two-dimensional (2D), and three-dimensional (3D) catalysts. Furthermore, it is found that the employment
of this approach reduces data requirements and augments computational speed, specifically for DL algorithms. This
work provides an effective and universal ML framework for various applications of molecular adsorption from catal-
ysis, sensors, carbon capture, and energy storage to drug delivery, signifying a novel and promising avenue in the
field of materials informatics. The implementation code in this work is available at https://github.com/mpeshel/
LEI-framework_LERN.

Keywords: Molecular adsorption, feature engineering, neural networks

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, shar-
ing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate
if changes were made.

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