Gold–Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential
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. Acs Nano, 18(20), 19014-19023. https://doi.org/10.1021/acsnano.4c03094
Published in
Acs NanoAuthors
Date
2024Copyright
© 2024 the Authors
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.
...
Publisher
American Chemical Society (ACS)ISSN Search the Publication Forum
1936-0851Keywords
Dataset(s) related to the publication
https://materialsproject-contribs.s3.amazonaws.com/index.html#ausch_potential/Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/221053207
Metadata
Show full item recordCollections
Related funder(s)
Research Council of FinlandFunding program(s)
Others, AoFAdditional information about funding
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. ...License
Related items
Showing items with similar title or keywords.
-
Analyzing protein-nanocluster interactions with graph-based machine learning for molecular dynamics
Sikoniemi, Anssi (2024)In this work a custom graph convolutional network was succesfully constructed and trained to predict interaction energies in molecular dynamics simulations between Au25(SR)18 nanoclusters and BSA proteins based on their ... -
Isomer dynamics of the [Au6(NHC-S)4]2+ nanocluster
Sabooni Asre Hazer, Maryam; Malola, Sami; Häkkinen, Hannu (Royal Society of Chemistry, 2022)The use of metal nanoclusters is strongly reliant on their size and configuration; hence, studying the potential isomers of a cluster is extremely beneficial in understanding their performance. In general, the prediction ... -
GraphBNC : Machine Learning‐Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins
Pihlajamäki, Antti; Matus, María Francisca; Malola, Sami; Häkkinen, Hannu (Wiley-VCH Verlag, 2024)Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing ... -
2023 Roadmap on molecular modelling of electrochemical energy materials
Zhang, Chao; Cheng, Jun; Chen, Yiming; Chan, Maria K. Y.; Cai, Qiong; Carvalho, Rodrigo P.; Marchiori, Cleber F. N.; Brandell, Daniel; Araujo, C. Moyses; Chen, Ming; Ji, Xiangyu; Feng, Guang; Goloviznina, Kateryna; Serva, Alessandra; Salanne, Mathieu; Mandai, Toshihiko; Hosaka, Tomooki; Alhanash, Mirna; Johansson, Patrik; Qiu, Yun-Ze; Xiao, Hai; Eikerling, Michael; Jinnouchi, Ryosuke; Melander, Marko M.; Kastlunger, Georg; Bouzid, Assil; Pasquarello, Alfredo; Shin, Seung-Jae; Kim, Minho M.; Kim, Hyungjun; Schwarz, Kathleen; Sundararaman, Ravishankar (IOP Publishing, 2023)New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and ... -
What Contributes to the Measured Chiral Optical Response of the Glutathione-Protected Au25 Nanocluster?
Monti, Marta; Matus, María Francisca; Malola, Sami; Fortunelli, Alessandro; Aschi, Massimiliano; Stener, Mauro; Häkkinen, Hannu (American Chemical Society (ACS), 2023)The water-soluble glutathione-protected [Au25(GSH)18]−1 nanocluster was investigated by integrating several methodologies such as molecular dynamics simulations, essential dynamics analysis, and state-of-the-art time-dependent ...