{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-network-approach-for-predicting","title":"Neural Network Approach for Predicting Infrared Spectra from 3D Molecular Structure","arxiv_id":"2405.05737","date":"2024-05-09","proceeding":null,"authors":["Saleh Abdul Al","Abdul-Rahman Allouche"],"abstract":"Accurately predicting infrared (IR) spectra in computational chemistry using ab initio methods remains a challenge. Current approaches often rely on an empirical approach or on tedious anharmonic calculations, mainly adapted to semi-rigid molecules. This limitation motivates us to explore alternative methodologies. Previous studies explored machine-learning techniques for potential and dipolar surface generation, followed by IR spectra calculation using classical molecular dynamics. However, these methods are computationally expensive and require molecule-by-molecule processing. Our article introduces a new approach to improve IR spectra prediction accuracy within a significantly reduced computing time. We developed a machine learning (ML) model to directly predict IR spectra from three-dimensional (3D) molecular structures. The spectra predicted by our model significantly outperform those from density functional theory (DFT) calculations, even after scaling. In a test set of 200 molecules, our model achieves a Spectral Information Similarity Metric of 0.92, surpassing the value achieved by DFT scaled frequencies, which is 0.57. Additionally, our model considers anharmonic effects, offering a fast alternative to laborious anharmonic calculations. Moreover, our model can be used to predict various types of spectra (Ultraviolet or Nuclear Magnetic Resonance for example) as a function of molecular structure. All it needs is a database of 3D structures and their associated spectra.","url_abs":"https://arxiv.org/abs/2405.05737v1","url_pdf":"https://arxiv.org/pdf/2405.05737v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"neural-network-approach-for-predicting","repo_url":"https://github.com/allouchear/nnmol-ir","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}