{"paper":{"title":"Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.ET","physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"Alain C. Vaucher, David R. Glowacki, Lars A. Bratholm, Markus Reiher, Silvia Amabilino, Simon J. Bennie","submitted_at":"2019-01-16T18:09:10Z","abstract_excerpt":"Whilst the primary bottleneck to a number of computational workflows was not so long ago limited by processing power, the rise of machine learning technologies has resulted in a paradigm shift which places increasing value on issues related to data curation - i.e., data size, quality, bias, format, and coverage. Increasingly, data-related issues are equally as important as the algorithmic methods used to process and learn from the data. Here we introduce an open source GPU-accelerated neural network (NN) framework for learning reactive potential energy surfaces (PESs), and investigate the use "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.05417","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"}