{"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/artificial-intelligence-for-science-in","title":"Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems","arxiv_id":"2307.08423","date":"2023-07-17","proceeding":null,"authors":["Xuan Zhang","Limei Wang","Jacob Helwig","Youzhi Luo","Cong Fu","Yaochen Xie","Meng Liu","Yuchao Lin","Zhao Xu","Keqiang Yan","Keir Adams","Maurice Weiler","Xiner Li","Tianfan Fu","Yucheng Wang","Alex Strasser","Haiyang Yu","Yuqing Xie","Xiang Fu","Shenglong Xu","Yi Liu","Yuanqi Du","Alexandra Saxton","Hongyi Ling","Hannah Lawrence","Hannes Stärk","Shurui Gui","Carl Edwards","Nicholas Gao","Adriana Ladera","Tailin Wu","Elyssa F. Hofgard","Aria Mansouri Tehrani","Rui Wang","Ameya Daigavane","Montgomery Bohde","Jerry Kurtin","Qian Huang","Tuong Phung","Minkai Xu","Chaitanya K. Joshi","Simon V. Mathis","Kamyar Azizzadenesheli","Ada Fang","Alán Aspuru-Guzik","Erik Bekkers","Michael Bronstein","Marinka Zitnik","Anima Anandkumar","Stefano Ermon","Pietro Liò","Rose Yu","Stephan Günnemann","Jure Leskovec","Heng Ji","Jimeng Sun","Regina Barzilay","Tommi Jaakkola","Connor W. Coley","Xiaoning Qian","Xiaofeng Qian","Tess Smidt","Shuiwang Ji"],"abstract":"Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science.","url_abs":"https://arxiv.org/abs/2307.08423v5","url_pdf":"https://arxiv.org/pdf/2307.08423v5.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":"artificial-intelligence-for-science-in","repo_url":"https://github.com/divelab/AIRS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"out-of-distribution-generalization","task_name":"Out-of-Distribution Generalization"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2307.08423","atlas_url":"https://app.syntology.ai/?focus=2307.08423","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08423"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/divelab/AIRS","reach":null}],"summary":{"ran_draft_wrong":1,"ran_fixture":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"741eb38fcf150c88","entry":"fftconv_ref","repo":"divelab/AIRS","repo_kind":"official","path":"OpenBio/ATGC_Gen/src/models/sequence/hyena.py","file_url":"https://github.com/divelab/AIRS/blob/HEAD/OpenBio/ATGC_Gen/src/models/sequence/hyena.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"741eb38fcf150c88"}},{"code_sha256_prefix":"5dd28d20c60f0c19","entry":"mul_sum","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"5dd28d20c60f0c19"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}