{"type":"ScholarlyArticle","title":"Environment-Aware Multiscale Geometric Interaction for Equivariant Molecular Spectral Prediction","authors":[{"name":"Haoran Li","slug":"haoran-li","given":"Haoran","family":"Li","affiliation":""},{"name":"Weiran Cui","slug":"weiran-cui","given":"Weiran","family":"Cui","affiliation":""},{"name":"Minghui Li","slug":"minghui-li","given":"Minghui","family":"Li","affiliation":""}],"abstract":"Predicting molecular spectra requires modeling 3D conformation and solvent modulation. However, E(3)-equivariant networks based on local message passing exhibit limited sensitivity to long-range geometric dependencies, affecting the discrimination of globally distinct conformers. We introduce the Multiscale Geometric Interaction Layer (MGIL), which integrates global context by augmenting local features with centroid-referenced anchors, geometric moments, and virtual nodes. This design explicitly encodes global anisotropy while maintaining equivariance. Furthermore, we propose a Solvent Field Modulator (SFM) to encode solvent topology for conditional feature adaptation. Experiments demonstrate that MGIL enhances the capture of global structural variations, yielding consistent performance gains across spectral prediction benchmarks while maintaining linear computational efficiency.","doi":"10.24963/ijcai.2026/501","url":"https://ijcai.org/proceedings/2026/501","pdf":"https://ijcai.org/proceedings/2026/0501.pdf","markdown":"https://ijcai.org/proceedings/2026/501.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/501","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":501,"pages":{"first":4501,"last":4508},"keywords":["Machine Learning: Geometric learning","Machine Learning: Deep learning architectures","Machine Learning: Representation learning","Machine Learning: Sequence and graph learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}