Gold–Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential
dc.contributor.author | McCandler, Caitlin A. | |
dc.contributor.author | Pihlajamäki, Antti | |
dc.contributor.author | Malola, Sami | |
dc.contributor.author | Häkkinen, Hannu | |
dc.contributor.author | Persson, Kristin A. | |
dc.date.accessioned | 2024-07-31T06:09:19Z | |
dc.date.available | 2024-07-31T06:09:19Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | McCandler, C. A., Pihlajamäki, A., Malola, S., Häkkinen, H., & Persson, K. A. (2024). Gold–Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential. <i>Acs Nano</i>, <i>18</i>(20), 19014-19023. <a href="https://doi.org/10.1021/acsnano.4c03094" target="_blank">https://doi.org/10.1021/acsnano.4c03094</a> | |
dc.identifier.other | CONVID_221053207 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/96441 | |
dc.description.abstract | Monolayer protected metal clusters comprise a rich class of molecular systems and are promising candidate materials for a variety of applications. While a growing number of protected nanoclusters have been synthesized and characterized in crystalline forms, their dynamical behavior in solution, including prenucleation cluster formation, is not well understood due to limitations both in characterization and first-principles modeling techniques. Recent advancements in machine-learned interatomic potentials are rapidly enabling the study of complex interactions such as dynamical behavior and reactivity on the nanoscale. Here, we develop an Au–S–C–H atomic cluster expansion (ACE) interatomic potential for efficient and accurate molecular dynamics simulations of thiolate-protected gold nanoclusters (Aun(SCH3)m). Trained on more than 30,000 density functional theory calculations of gold nanoclusters, the interatomic potential exhibits ab initio level accuracy in energies and forces and replicates nanocluster dynamics including thermal vibration and chiral inversion. Long dynamics simulations (up to 0.1 μs time scale) reveal a mechanism explaining the thermal instability of neutral Au25(SR)18 clusters. Specifically, we observe multiple stages of isomerization of the Au25(SR)18 cluster, including a chiral isomer. Additionally, we simulate coalescence of two Au25(SR)18 clusters and observe series of clusters where the formation mechanisms are critically mediated by ligand exchange in the form of [Au–S]n rings. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | American Chemical Society (ACS) | |
dc.relation.ispartofseries | Acs Nano | |
dc.rights | CC BY 4.0 | |
dc.subject.other | Nanocluster | |
dc.subject.other | Interatomic Potential | |
dc.subject.other | Molecular Dynamics | |
dc.subject.other | Isomers | |
dc.subject.other | Coalescence | |
dc.subject.other | Gold | |
dc.title | Gold–Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential | |
dc.type | research article | |
dc.identifier.urn | URN:NBN:fi:jyu-202407315263 | |
dc.contributor.laitos | Fysiikan laitos | fi |
dc.contributor.laitos | Department of Physics | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 19014-19023 | |
dc.relation.issn | 1936-0851 | |
dc.relation.numberinseries | 20 | |
dc.relation.volume | 18 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © 2024 the Authors | |
dc.rights.accesslevel | openAccess | fi |
dc.type.publication | article | |
dc.relation.grantnumber | 351582 | |
dc.subject.yso | kulta | |
dc.subject.yso | klusterit | |
dc.subject.yso | molekyylidynamiikka | |
dc.subject.yso | isomeria | |
dc.subject.yso | nanotieteet | |
dc.subject.yso | koneoppiminen | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p19016 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p18755 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p29332 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p10129 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p6228 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
dc.rights.url | https://creativecommons.org/licenses/by/4.0/ | |
dc.relation.dataset | https://materialsproject-contribs.s3.amazonaws.com/index.html#ausch_potential/ | |
dc.relation.doi | 10.1021/acsnano.4c03094 | |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Suomen Akatemia | fi |
jyx.fundingprogram | Others, AoF | en |
jyx.fundingprogram | Muut, SA | fi |
jyx.fundinginformation | C.A.M. acknowledges the National Defense Science and Engineering Graduate (NDSEG) fellowship and the Kavli ENSI Graduate Student Fellowship for financial support. The research at UC Berkeley/LBNL used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award BES-ERCAP0024004. The research at University of Jyväskylä was supported by the Academy of Finland (grant 351582) and the Finnish national supercomputing center CSC. | |
dc.type.okm | A1 |