Machine learning approach to atomic simulations of protected gold nanoclusters
Julkaistu sarjassa
JYU dissertationsTekijät
Päivämäärä
2022Tekijänoikeudet
© The Author & University of Jyväskylä
In the nanometer lengthscale, the boundaries between physics, chemistry and biology disappear and all phenomena are reduced to the level of atomic interactions. Technological advancement has provided means to measure what happens at the atomic level but there are limitations and experiments cannot tell everything. Here computational studies provide further insight. The most accurate computational methods are based on the quantum mechanics, which explains atomic interactions at the level of electrons. However, these methods are computationally demanding, which limits their usage. One can also compromise the accuracy and use efficient force field methods. During the last two decades a third type of method, machine learning (ML), has become increasingly popular. ML methods utilize data from other computational methods or measurements to "learn" underlying trends. This way they reproduce the behavior of the high-level methods with significantly reduced computational cost. Their usage is not restricted to imitate other methods but they can also be used for data analysis. In this thesis, four studies demonstrate three different applications of ML methods in studies of gold nanoclusters protected by organic ligands. Wavelet-based image comparison method was used to analyze experimental and theoretical transmission electron microscope (TEM) images of the crystal lattice made of nanoclusters. The analysis ruled out the possible structural isomer and shed light to the cluster orientation in TEM images. So-called distance-based ML methods were utilized for dynamic simulations of the similar clusters. Based on the given configuration the ML method predicted potential energies, which were used to run Monte Carlo simulations emulating the dynamics of the clusters. After this, a new distance-based ML method was designed to estimate forces affecting to the individual atoms of the nanoclusters. Estimated force vectors enabled ML assisted structure optimization of the goldthiolate systems. The results showed the great potential of the distance-based methods on simulations of the complex nanostructures.
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Julkaisija
Jyväskylän yliopistoISBN
978-951-39-9309-2ISSN Hae Julkaisufoorumista
2489-9003Julkaisuun sisältyy osajulkaisuja
- Artikkeli I: Yao, Q., Liu, L., Malola, S., Xu, H., Wu, Z., Chen, T., Cao, Y., Matus, M. F., Pihlajamäki, A., Zang, S., Han, Y., Häkkinen, H. and Xie, J. (2022). Engineering Colloidal Crystals of Atomically Precise Gold Nanoparticles Promotedby Particle Surface Dynamics. Accepted in Nature Chemistry.
- Artikkeli II: Pihlajamäki, A., Hämäläinen, J., Linja, J., Nieminen, P., Malola, S., Kärkkäinen, T., & Häkkinen, H. (2020). Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods. Journal of Physical Chemistry A, 124(23), 4827-4836. DOI: 10.1021/acs.jpca.0c01512. JYX: jyx.jyu.fi/handle/123456789/69062
- Artikkeli III: Pihlajamäki, A., Linja, J., Hämäläinen, J., Nieminen, P., Malola, S., Kärkkäinen, T., & Häkkinen, H. (2021). Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces. In ESANN 2021 : Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Online event (Bruges, Belgium), October 06 - 08 (pp. 529-534). ESANN. DOI: 10.14428/esann/2021.es2021-34
- Artikkeli IV: Pihlajamäki, A., Malola, S., Kärkkäinen,T. and Häkkinen. H. (2022). Orientation Adaptive Minimal Learning Machine: Application to Thiolate- Protected Gold Nanoclusters and Gold-Thiolate Rings. Arxiv
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Photodynamics studies of ligand-protected gold nanoclusters by using ultrafast transient infrared spectroscopy
Mustalahti, Satu (University of Jyväskylä, 2015)Highly monodisperse samples of three ligand-protected gold nanoclusters Au102(pMBA)44, Au144(SC2H4Ph)60, and a cluster tentatively identified as Au130(pMBA)50, were characterized by UV/vis and infrared spectroscopy, ... -
Dynamic Stabilization of the Ligand-Metal Interface in Atomically Precise Gold Nanoclusters Au68 and Au144 Protected by meta-Mercaptobenzoic Acid
Tero, Tiia-Riikka; Malola, Sami; Koncz, Benedek; Pohjolainen, Emmi; Lautala, Saara; Mustalahti, Satu; Permi, Perttu; Groenhof, Gerrit; Pettersson, Mika; Häkkinen, Hannu (American Chemical Society, 2017)Ligand-stabilized, atomically precise gold nanoclusters with a metal core of a uniform size of just 1-3 nm constitute an interesting class of nanomaterials with versatile possibilities for applications due to their ... -
Study of Water-Soluble p-MBA-Protected Gold Nanoclusters and Their Superstructures
Sokołowska, Karolina (Jyväskylän yliopisto, 2019)The development of gold nanoclusters has made remarkable progress enriching the research field of nanomaterial science. The breakthroughs in synthetic chemistry enable the preparation of functional materials using gold ... -
Chiral Inversion of Thiolate-Protected Gold Nanoclusters via Core Reconstruction without Breaking an Au-S Bond
Malola, Sami; Häkkinen, Hannu (American Chemical Society, 2019)Based on density functional theory computations of the well-known chiral Au38(SR)24 nanocluster and its Pd- and Ag-doped derivatives, we propose here a mechanism for chiral inversion that does not require breaking of ... -
Real-space imaging with pattern recognition of a ligand-protected Ag374 nanocluster at sub-molecular resolution
Zhou, Qin; Kaappa, Sami; Malola, Sami; Lu, Hui; Guan, Dawei; Li, Yajuan; Wang, Haochen; Xie, Zhaoxiong; Ma, Zhibo; Häkkinen, Hannu; Zheng, Nanfeng; Yang, Xueming; Zheng, Lansun (Nature Publishing Group, 2018)High-resolution real-space imaging of nanoparticle surfaces is desirable for better understanding of surface composition and morphology, molecular interactions at the surface, and nanoparticle chemical functionality in its ...
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