{"type":"ScholarlyArticle","title":"Electron Density-enhanced Molecular Geometry Learning","authors":[{"name":"Hongxin Xiang","slug":"hongxin-xiang","given":"Hongxin","family":"Xiang","affiliation":""},{"name":"Jun Xia","slug":"jun-xia","given":"Jun","family":"Xia","affiliation":""},{"name":"Xin Jin","slug":"xin-jin","given":"Xin","family":"Jin","affiliation":""},{"name":"Wenjie Du","slug":"wenjie-du","given":"Wenjie","family":"Du","affiliation":""},{"name":"Li Zeng","slug":"li-zeng","given":"Li","family":"Zeng","affiliation":""},{"name":"Xiangxiang Zeng","slug":"xiangxiang-zeng","given":"Xiangxiang","family":"Zeng","affiliation":""}],"abstract":"Electron density (ED), which describes the probability distribution of electrons in space, is crucial for accurately understanding the energy and force distribution in molecular force fields (MFF).\r\nExisting machine learning force fields (MLFF) focus on mining appropriate physical quantities from the atom-level conformation to enhance the molecular geometry representation while ignoring the unique information from microscopic electrons. In this work, we propose an efficient Electronic Density representation framework to enhance molecular Geometric learning (called EDG), which leverages images rendered from ED to boost molecular geometric representations in MLFF. Specifically, we construct a novel image-based ED representation, which consists of 2 million 6-view images with RGB-D channels, and design an ED representation learning model, called ImageED, to learn ED-related knowledge from these images. We further propose an efficient ED-aware teacher and introduce a cross-modal distillation strategy to transfer knowledge from the image-based teacher to the geometry-based students. Extensive experiments on QM9 and rMD17 demonstrate that EDG can be directly integrated into existing geometry-based models and significantly improves the capabilities of these models (e.g., SchNet, EGNN, SphereNet, ViSNet) for geometry representation learning in MLFF with a maximum average performance increase of 33.7%. Code and appendix are available at https://github.com/HongxinXiang/EDG","doi":"10.24963/ijcai.2025/872","url":"https://ijcai.org/proceedings/2025/872","pdf":"https://ijcai.org/proceedings/2025/0872.pdf","markdown":"https://ijcai.org/proceedings/2025/872.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/872","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":872,"pages":{"first":7840,"last":7848},"keywords":["Machine Learning: ML: Knowledge-aided learning","Multidisciplinary Topics and Applications: MTA: Bioinformatics","Machine Learning: ML: Self-supervised Learning","Machine Learning: ML: Representation learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}