{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T23:51:28Z","timestamp":1788911488229,"version":"build-2803163510"},"reference-count":61,"publisher":"Wiley","issue":"15","license":[{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Comput Chem"],"published-print":{"date-parts":[[2024,6,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Machine learning (ML) force fields are revolutionizing molecular dynamics (MD) simulations as they bypass the computational cost associated with ab initio methods but do not sacrifice accuracy in the process. In this work, the GPyTorch library is used to create Gaussian process regression (GPR) models that are interfaced with the next\u2010generation ML force field FFLUX. These models predict atomic properties of different molecular configurations that appear in a progressing MD simulation. An improved kernel function is utilized to correctly capture the periodicity of the input descriptors. The first FFLUX molecular simulations of ammonia, methanol, and malondialdehyde with the updated kernel are performed. Geometry optimizations with the GPR models result in highly accurate final structures with a maximum root\u2010mean\u2010squared deviation of 0.064\u2009\u00c5 and sub\u2010kJ\u2009mol\n                    <jats:sup>\u22121<\/jats:sup>\n                    total energy predictions. Additionally, the models are tested in 298\u2009K MD simulations with FFLUX to benchmark for robustness. The resulting energy and force predictions throughout the simulation are in excellent agreement with ab initio data for ammonia and methanol but decrease in quality for malondialdehyde due to the increased system complexity. GPR model improvements are discussed, which will ensure the future scalability to larger systems.\n                  <\/jats:p>","DOI":"10.1002\/jcc.27323","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T06:33:31Z","timestamp":1707719611000},"page":"1235-1246","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["<scp>FFLUX<\/scp>\n                    molecular simulations driven by atomic\n                    <scp>Gaussian<\/scp>\n                    process regression models"],"prefix":"10.1002","volume":"45","author":[{"given":"Yulian T.","family":"Manchev","sequence":"first","affiliation":[{"name":"Department of Chemistry The University of Manchester  Manchester Great Britain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul L. 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