{"paper":{"title":"FES-FM: Free Energy Surface Sampling via Reduced Flow Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Training a flow matching model directly in collective variable space allows efficient sampling of free energy surfaces.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Tiejun Li, Zichen Liu","submitted_at":"2026-05-01T01:35:48Z","abstract_excerpt":"Sampling the distribution of collective variables (CVs) and estimating the associated free energy surface are crucial problems in statistical physics, as they underpin a better understanding of chemical reactions and conformational transitions. Traditional methods usually rely on simulations in high-dimensional configuration space and project the resulting configurations onto the CV space. To improve sampling speed, we propose FES-FM, a reduced flow matching (FM) method for free energy surface (FES) sampling. We train a dynamical transport map in the CV space, thereby enabling direct sampling "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Comparative experiments demonstrate that our approach drastically reduces computational costs while delivering superior accuracy per unit sampling time.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a dynamical transport map trained only in collective variable space, even with the Hessian prior, fully captures the free energy distribution without missing important high-dimensional correlations or rare events.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"FES-FM applies reduced flow matching with a Hessian-derived prior to directly sample free energy surfaces in collective variable space, claiming lower computational cost and higher accuracy per unit time than standard methods.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Training a flow matching model directly in collective variable space allows efficient sampling of free energy surfaces.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"79d67fcf49de1d77bad0257b3a3e3cfb91a8030d02fcfb7d7a6f072e41c77823"},"source":{"id":"2605.00337","kind":"arxiv","version":2},"verdict":{"id":"b0281679-26d7-4ea4-ae07-83faefed3c0e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-09T20:23:53.937208Z","strongest_claim":"Comparative experiments demonstrate that our approach drastically reduces computational costs while delivering superior accuracy per unit sampling time.","one_line_summary":"FES-FM applies reduced flow matching with a Hessian-derived prior to directly sample free energy surfaces in collective variable space, claiming lower computational cost and higher accuracy per unit time than standard methods.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a dynamical transport map trained only in collective variable space, even with the Hessian prior, fully captures the free energy distribution without missing important high-dimensional correlations or rare events.","pith_extraction_headline":"Training a flow matching model directly in collective variable space allows efficient sampling of free energy surfaces."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.00337/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T20:34:12.295910Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T18:15:54.509647Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"94ed9aba5a8dbafe7cb461ccc4514751c51a9a6c7a0e7cec5f72a22f93dad817"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}