{"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/a-community-powered-search-of-machine","title":"A community-powered search of machine learning strategy space to find NMR property prediction models","arxiv_id":"2008.05994","date":"2020-08-13","proceeding":null,"authors":["Lars A. Bratholm","Will Gerrard","Brandon Anderson","Shaojie Bai","Sunghwan Choi","Lam Dang","Pavel Hanchar","Addison Howard","Guillaume Huard","Sanghoon Kim","Zico Kolter","Risi Kondor","Mordechai Kornbluth","Youhan Lee","Youngsoo Lee","Jonathan P. Mailoa","Thanh Tu Nguyen","Milos Popovic","Goran Rakocevic","Walter Reade","Wonho Song","Luka Stojanovic","Erik H. Thiede","Nebojsa Tijanic","Andres Torrubia","Devin Willmott","Craig P. Butts","David R. Glowacki","Kaggle participants"],"abstract":"The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published \"in-house\" efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.","url_abs":"https://arxiv.org/abs/2008.05994v1","url_pdf":"https://arxiv.org/pdf/2008.05994v1.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":"abstracts"},"code_links":[{"paper_slug":"a-community-powered-search-of-machine","repo_url":"https://github.com/larsbratholm/champs_kaggle","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"molecular-property-prediction","task_name":"Molecular Property Prediction"},{"task_slug":"nmr-j-coupling","task_name":"NMR J-coupling"},{"task_slug":"property-prediction","task_name":"Property Prediction"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"graph-transformer","method_name":"Graph Transformer"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"lapeigen","method_name":"LapEigen"},{"method_slug":"laplacian-pe","method_name":"Laplacian PE"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/nmr-j-coupling-on-qm9","task":"NMR J-coupling","dataset":"QM9","model":"Graph Transformer","rank_in_archive_order":1,"of":2,"metrics":{"avg. log MAE":"-3.241"},"uses_additional_data":false},{"leaderboard":"/sota/nmr-j-coupling-on-qm9","task":"NMR J-coupling","dataset":"QM9","model":"Ensemble of top 400 submissions","rank_in_archive_order":2,"of":2,"metrics":{"avg. log MAE":"-3.453"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}