Advanced machine learning based global optimizations for Pt nanoclusters |
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| Affiliation: | 1. Department of Chemistry, Osmania University, Hyderabad, 500007, India;2. Department of Chemistry, Malla Reddy Engineering College (Autonomous), Main Campus, Hyderabad, Telangana, 500100, India;1. Selvamm Arts and Science College, Namakkal, 637 003, Tamilnadu, India;2. Adiyamaan College of Engineering, Hosur, 635 209, Tamilnadu, India;3. Department of Physics, Periyar University, Salem, 636 011, Tamilnadu, India;1. Meenakshi Ramasamy College of Arts and Science (Affliated to Bharathidasan University), Thathanur, 621804, Tiruchirapalli, TN, India;2. Vivekanandha College of Arts and Sciences for Women (Autonomous), Tiruchengode, Namakkal - 637 205, TN, India;3. Vivekanandha College of Engineering for Women (Autonomous), Tiruchengode, Namakkal - 637 205, TN, India;4. RVS Engineering College, Coimbatore, Tamil Nadu, India;5. PG Department of Physics, GTN Arts College Dindigul, 624 005, Tamil Nadu, India;6. Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha, 61413, P.O. Box 9004, Saudi Arabia;7. Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia;8. University Center for Research & Development (UCRD), Chandigarh University, NH95,Chandigarh-Ludhiana Highway, Gharuan, Mohali, Punjab, 140413, India;9. Physics Department, Faculty of Science, Zagazig University, 44519 Zagazig, Egypt;1. Department of Chemistry, Sardar Vallabhai National Institute of Technology, Surat, 395007, Gujarat, India;2. Department of Chemistry, School of Advanced Sciences, VIT-AP University, Amaravati, 522237, Andhra Pradesh, India;3. Department of Polymer Science & Technology, Sri Krishnadevaraya University, Ananthapuramu, 515003, Andhra Pradesh, India;1. University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh, 160 014, India;2. Department of Chemistry and Centre of Advanced Studies, Panjab University, Chandigarh, 160 014, India;3. Vaxine Pty Ltd, Warradale, Australia;4. College of Medicine and Public Health, Flinders University, Adelaide, Australia;5. National Interdisciplinary Centre of Vaccine, Immunotherapeutics and Antimicrobials, Panjab University, Chandigarh, 160 014, India;1. Department of Physics, University of Rajasthan, Jaipur, Rajasthan, 302004, India;2. Government Girls College, Jhunjhunu, Rajasthan, 333001, India;3. Government College, Pratapgarh, Rajasthan, 230001, India;4. Government College, Jhunjhunu, Rajasthan, 333001, India;5. Centre for Non-Conventional Energy Resources, University of Rajasthan, Jaipur, Rajasthan, 302004, India |
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| Abstract: | Pt-nanoclusters have attracted attention due to their extensive use as catalysts in various sectors and their catalytic capabilities, instigating a theoretical investigation to correlate structure and property. On the other hand, it is challenging to find stable and reliable structures to support experimental results at the nanoscale due to their fluxional nature at ambient temperature. The major objective of this work is to test the capability of stable and reliable structure findings at the nanocluster region by Gaussian Process Regression (GPR) model potentials on-the-fly within the evolutionary framework using the Bayesian optimization approach. The entire algorithm is called Global Optimizations by GPR (GO-GPR) learning. In this regard, the GO-GPR algorithm examined the potential energy surfaces of bare Ptn-nanoclusters of sizes (n = 3–6, 7, 8, 10, 13). GO-GPR identified new low-lying isomers and global minimum structures are in correlation with earlier studies. In the case of Pt13 and Pt8 nano-clusters, the global minimum structure is close to the second lowest energy structure, implying that these clusters can have fluxional nature. In fact, a few experimental studies have shown that Pt8 and Pt13 are effective in catalyzing reactions. |
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| Keywords: | Global optimizations (GO) Bayesian optimizations approach Gaussian regression process (GPR) potentials Pt-nanoclusters |
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