{"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/transfer-learning-for-atomistic-simulations","title":"Transfer learning for atomistic simulations using GNNs and kernel mean embeddings","arxiv_id":"2306.01589","date":"2023-06-02","proceeding":"NeurIPS 2023 11","authors":["John Falk","Luigi Bonati","Pietro Novelli","Michele Parrinello","Massimiliano Pontil"],"abstract":"Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, accurate models require large training datasets, while generating reference calculations is computationally demanding. To bypass this difficulty, we propose a transfer learning algorithm that leverages the ability of graph neural networks (GNNs) to represent chemical environments together with kernel mean embeddings. We extract a feature map from GNNs pre-trained on the OC20 dataset and use it to learn the potential energy surface from system-specific datasets of catalytic processes. Our method is further enhanced by incorporating into the kernel the chemical species information, resulting in improved performance and interpretability. We test our approach on a series of realistic datasets of increasing complexity, showing excellent generalization and transferability performance, and improving on methods that rely on GNNs or ridge regression alone, as well as similar fine-tuning approaches.","url_abs":"https://arxiv.org/abs/2306.01589v5","url_pdf":"https://arxiv.org/pdf/2306.01589v5.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":"transfer-learning-for-atomistic-simulations","repo_url":"https://github.com/isakfalk/atomistic_transfer_mekrr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2306.01589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.01589"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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":"deterministic:regex_extraction","url":"https://github.com/IsakFalk/atomistic_transfer_mekrr","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/isakfalk/atomistic_transfer_mekrr","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":1,"unverified":9},"by_repo_kind":{"official":{"samples":10,"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":0,"samples":[{"code_sha256_prefix":"538da70b4de12205","entry":"KernelMeanEmbeddingRidgeRegression","repo":"isakfalk/atomistic_transfer_mekrr","repo_kind":"official","path":"ocpmodels/transfer_learning/models/distribution_regression.py","file_url":"https://github.com/isakfalk/atomistic_transfer_mekrr/blob/HEAD/ocpmodels/transfer_learning/models/distribution_regression.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"538da70b4de12205"}},{"code_sha256_prefix":"3344cafe530a018d","entry":"Kernel","repo":"isakfalk/atomistic_transfer_mekrr","repo_kind":"official","path":"ocpmodels/transfer_learning/models/distribution_regression.py","file_url":"https://github.com/isakfalk/atomistic_transfer_mekrr/blob/HEAD/ocpmodels/transfer_learning/models/distribution_regression.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3344cafe530a018d"}},{"code_sha256_prefix":"4706d81afc298fee","entry":"MeanEmbeddingKernel","repo":"isakfalk/atomistic_transfer_mekrr","repo_kind":"official","path":"ocpmodels/transfer_learning/models/distribution_regression.py","file_url":"https://github.com/isakfalk/atomistic_transfer_mekrr/blob/HEAD/ocpmodels/transfer_learning/models/distribution_regression.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4706d81afc298fee"}},{"code_sha256_prefix":"357cb629c800ace4","entry":"aggregate_metric","repo":"IsakFalk/atomistic_transfer_mekrr","repo_kind":"official","path":"ocpmodels/transfer_learning/common/utils.py","file_url":"https://github.com/IsakFalk/atomistic_transfer_mekrr/blob/HEAD/ocpmodels/transfer_learning/common/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"357cb629c800ace4"}},{"code_sha256_prefix":"5b4761e4a55124d9","entry":"energy_mae","repo":"IsakFalk/atomistic_transfer_mekrr","repo_kind":"official","path":"ocpmodels/transfer_learning/modules/evaluator.py","file_url":"https://github.com/IsakFalk/atomistic_transfer_mekrr/blob/HEAD/ocpmodels/transfer_learning/modules/evaluator.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5b4761e4a55124d9"}},{"code_sha256_prefix":"f184773d827cdf85","entry":"forces_mae","repo":"IsakFalk/atomistic_transfer_mekrr","repo_kind":"official","path":"ocpmodels/transfer_learning/modules/evaluator.py","file_url":"https://github.com/IsakFalk/atomistic_transfer_mekrr/blob/HEAD/ocpmodels/transfer_learning/modules/evaluator.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f184773d827cdf85"}},{"code_sha256_prefix":"e715396821430ff8","entry":"forces_rmse","repo":"IsakFalk/atomistic_transfer_mekrr","repo_kind":"official","path":"ocpmodels/transfer_learning/modules/evaluator.py","file_url":"https://github.com/IsakFalk/atomistic_transfer_mekrr/blob/HEAD/ocpmodels/transfer_learning/modules/evaluator.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e715396821430ff8"}},{"code_sha256_prefix":"3267a3f35af300d7","entry":"get_config","repo":"IsakFalk/atomistic_transfer_mekrr","repo_kind":"official","path":"ocpmodels/transfer_learning/common/utils.py","file_url":"https://github.com/IsakFalk/atomistic_transfer_mekrr/blob/HEAD/ocpmodels/transfer_learning/common/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3267a3f35af300d7"}},{"code_sha256_prefix":"6d77dd5f3c3a99b9","entry":"median_heuristic","repo":"IsakFalk/atomistic_transfer_mekrr","repo_kind":"official","path":"ocpmodels/transfer_learning/models/distribution_regression.py","file_url":"https://github.com/IsakFalk/atomistic_transfer_mekrr/blob/HEAD/ocpmodels/transfer_learning/models/distribution_regression.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6d77dd5f3c3a99b9"}},{"code_sha256_prefix":"d5f02955c7ab1571","entry":"torch_detach_maybe","repo":"IsakFalk/atomistic_transfer_mekrr","repo_kind":"official","path":"ocpmodels/transfer_learning/common/utils.py","file_url":"https://github.com/IsakFalk/atomistic_transfer_mekrr/blob/HEAD/ocpmodels/transfer_learning/common/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d5f02955c7ab1571"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}