[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121488-en":3,"doc-seo-121488-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121488,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Automatic Process Exploration through Machine Learning Assisted Transition State Searches","An efficient automatic process explorer (APE) framework reduces reliance on human intuition when identifying relevant elementary processes in kinetic Monte Carlo (kMC) simulations with fixed process lists. A fuzzy machine-learning classifier minimizes redundancy by steering transition-state searches toward previously unexplored local atomic environments. Applied to island diffusion on a Pd(100) surface, APE uncovers many overlooked low-barrier collective processes, substantially increasing kMC-predicted island diffusivity. The workflow iteratively explores local environments via dimer transition-state searches and curates duplicate-free process catalogs.","Automatic Process Exploration through Machine Learning Assisted Transition State Searches  \nKing Chun Lai, Patricia Poths, Sebastian Matera, Christoph Scheurer, and Karsten Reuter  \nFritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany  \n (Received 12 July 2024; accepted 6 February 2025; published 4 March 2025)  \nWe present an efficient automatic process explorer (APE) framework to overcome the reliance on human intuition to empirically establish relevant elementary processes of a given system, e.g., in prevalent kinetic Monte Carlo (kMC) simulations based on fixed process lists. Use of a fuzzy machine learning classification algorithm minimizes redundancy in the transition-state searches by driving them toward hitherto unexplored local atomic environments. APE application to island diffusion at a Pd(100) surface immediately reveals a large number of, up to now, disregarded low-barrier collective processes that lead to a significant increase in the kMC-determined island diffusivity as compared to classic surface hopping and exchange diffusion mechanisms.  \nDOI: 10.1103/PhysRevLett.134.096201  \nIn materials and surface science, the prevalence ofactivated atomic-scale processes with barriers exceeding several kB T makes rare-event type dynamics more the norm than an exception. In corresponding systems, the dynamics is characterized by long residence times in basins of the potential energy surface (PES), and infrequent crossings of PES transition states (TSs) into other basins. Longer-term time evolution is then suitably described by a Markovian master equation, which coarse grains the continuous dynamics into discrete Markov jumps between the respective system states. The short-time dynamics within each basin is appropriately condensed into rate constants kij for individual jumps from state i to state j. Rate theories provide the link between the PES and the kij , e.g., within harmonic transition state theory (hTST) [1,2], kij ¼ νij expð−ΔEaij =kB TÞ with νij a vibrational-frequency dependent prefactor and ΔEaij the activation barrier connected with the TS.  \nAmong various approaches to approximately solve the master equation for practical systems [3–5], the kinetic Monte Carlo (kMC) approach enjoys increasing popularity as a general and versatile methodology [6,7] . The kMC approach generates an ensemble of state-to-state trajectories, whose average yields the correct time evolution. For nontrivial systems, this implies excessive queries of the possible jumps out of states i visited along a trajectory. In particular, if the PES is evaluated by first-principles theories like density-functional theory (DFT), the dominant  \nPublished by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Open access publication funded by the Max Planck Society.  \nsolution to keep the computational costs ofthe concomitant TS searches tractable is to determine the involved states i as well as all jumps also known as the elementary processes connecting them before the actual kMC simulation. In corresponding microkinetic models [8,9], the activation barriers ΔEaij are typically identified using two-sided TS searches to have the resulting list of kij accessible in lookup tables during the kMC simulation itself. Such firstprinciples kMC simulations [10,11] have been successfully employed for a wide range of applications [12–17] . Such works typically also map the system onto a lattice model, to further reduce the number of nonequivalent elementary processes as well as to exploit efficient local updating and other numerical accelerators in high-end lattice kMC codes [10,11] .  \nWhile highly efficient, the downside of this look-up table approach to kMC is the necessity to a priori establish the microkinetic model. This is typical","cbCaia76Cs9cBbUM","https://ap.wps.com/l/cbCaia76Cs9cBbUM","pdf",3805232,1,6,"English","en",105,"# Automatic Process Exploration through Machine Learning Assisted Transition State Searches\n## Background: Rare-event dynamics and master-equation kMC\n## Conventional approaches and their limitations\n## APE workflow and DECAF-driven local environment exploration\n## Application: Pd(100) island diffusion and impact on diffusivity","[{\"question\":\"What problem does the Automatic Process Explorer (APE) address in kMC simulations?\",\"answer\":\"APE addresses the need to manually identify relevant elementary processes and transition states, which can bias results and miss crucial events when using fixed process lists in kMC.\"},{\"question\":\"How does APE reduce redundant transition-state searches?\",\"answer\":\"APE uses a fuzzy machine-learning classification algorithm to guide transition-state searches toward hitherto unexplored local atomic environments, minimizing repeated exploration of already known environments.\"},{\"question\":\"What key outcome does APE achieve for island diffusion on Pd(100)?\",\"answer\":\"APE reveals a large set of previously disregarded low-barrier collective processes, leading to a significant increase in kMC-determined island diffusivity compared with classic surface hopping and exchange diffusion mechanisms.\"}]","Automatic Process Exploration through Machine Learning Assisted Transition State Searches | 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problem does the Automatic Process Explorer (APE) address in kMC simulations?","Question",{"text":75,"@type":76},"APE addresses the need to manually identify relevant elementary processes and transition states, which can bias results and miss crucial events when using fixed process lists in kMC.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does APE reduce redundant transition-state searches?",{"text":80,"@type":76},"APE uses a fuzzy machine-learning classification algorithm to guide transition-state searches toward hitherto unexplored local atomic environments, minimizing repeated exploration of already known environments.",{"name":82,"@type":73,"acceptedAnswer":83},"What key outcome does APE achieve for island diffusion on Pd(100)?",{"text":84,"@type":76},"APE reveals a large set of previously disregarded low-barrier collective processes, leading to a significant increase in kMC-determined island diffusivity compared with classic surface hopping and exchange diffusion 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