[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126614-en":3,"doc-seo-126614-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126614,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Accurate Energy Barriers for Catalytic Reaction Pathways - An Automatic Training Protocol for Machine Learning Force Fields","The study presents an automatic training protocol for machine learning force fields (MLFFs) to accurately determine energy barriers along catalytic reaction pathways. Validation uses CO2 hydrogenation to methanol over indium oxide, showing active learning brings MLFF barriers within 0.05 eV of density functional theory while reducing the cost of routine in-silico catalytic tasks. Results also reveal a 40% reduction in the rate-limiting step versus a prior finding, quantify finite-temperature and free-energy barrier effects, and demonstrate transferability to an experimentally relevant yet unexplored indium oxide surface.","arXiv :2301 .09931v2 [physics .chem-ph] 22 Aug 2023  \nAccurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields  \nLars L Schaaf1,* , Edvin Fako2 , Sandip De2,* , Ansgar Sch¨afer2 , and G´abor Cs´anyi 1  \n1 Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ UK  \n2 BASF SE, Carl-Bosch-Straße 38, 67056 Ludwigshafen, Germany  \n* corresponding authors: [lls34@cam. ac.uk](lls34@cam. ac.uk) (L. L. Schaaf), [sandip. de@basf. com](sandip. de@basf. com) (S. De)  \nAugust 23, 2023  \nAbstract  \nIn this study, we introduce a training protocol for developing machine learning force fields (MLFFs), capable of accurately determining energy barriers in catalytic reaction pathways. The protocol is validated on the extensively explored hydrogenation of carbon dioxide to methanol over indium oxide. With the help of active learning, the final force field obtains energy barriers within 0.05 eV of Density Functional Theory. Thanks to the computational speedup, not only do we reduce the cost of routine in-silico catalytic tasks, but also find a 40% reduction in the previously established rate-limiting step. Furthermore, we illustrate the importance of finite-temperature effects and compute free energy barriers. The transferability of the protocol is demonstrated on the experimentally relevant, yet unexplored, top-layer reduced indium oxide surface. The ability of MLFFs to enhance our understanding of extensively studied catalysts underscores the need for fast and accurate alternatives to direct ab-intio simulations.  \nKeywords: ML force fields, catalysis, energy barriers, active learning  \n1 Introduction  \nComputational modeling plays a central role in understanding heterogeneous catalysis at an atomic scale. By complementing experimental observations, simulations are used to discover detailed reaction mechanisms, rationalize catalytic trends, and guide the design of catalytic materials [1, 2] . Limited by the computational cost of ab-intio methods, however, catalytic systems are often represented by small idealized surfaces with individual molecules adsorbedat a specific active site. Under these approximations, Density Functional Theory (DFT) is extensively used to draw connections between adsorption properties and catalytic activity [3–5] . However, neglecting more complex effects, such as interactions between different adsorbates, poisoning, finite temperature, and complex surface morphology, can severely limit the relevance of computational studies to experimental observations [4, 6–10] . Furthermore, selecting viable reaction paths and specific intermediate configurations requires expert knowledge or extensive trial and error. Finding all relevant reaction mechanisms and accurately predicting reaction rates  \nwould require extensive screening [11] . For large reaction networks and complex surfaces, this goes beyond DFT capabilities.  \nAn alternative is to use empirically parameterized force fields, which are orders of magnitude faster. However, while there exist reactive force fields [12], their parameters need to be adjusted for every novel system and often deviate significantly from the true PES due to their limited expressivity [13] .  \nMachine learning force fields (MLFFs) offer a way to bridge this gap. Rather than starting a new electronic structure calculation for each step in a simulation, the MLFF predicts energy and forces for novel configurations using a model trained on a set of reference configurations. Apart from being orders of magnitude faster for small systems, local MLFFs scale linearly with system size, are reactive, and systematically improvable by augmenting the training set. Recent innovations in describing atomic environmentsand training frameworks have allowed MLFF to describe more and more complex interactions in both molecules and materials [14–20] . In the context of heterogeneous catalysis, MLFFs have evolved from  \ncapturing low dimension","cbCaikiKhu38aii4","https://ap.wps.com/l/cbCaikiKhu38aii4","pdf",3353050,4,1,16,"English","en",105,"# Abstract\n# Introduction\n## Motivation and limitations of ab-initio methods\n## Machine learning force fields as a bridge\n## Focus of this work and protocol overview","[{\"question\":\"What does the proposed training protocol for MLFFs achieve in catalytic reaction pathways?\",\"answer\":\"It enables MLFFs to accurately determine energy barriers along catalytic reaction pathways, using active learning to converge modeled surfaces toward DFT in relevant domains.\"},{\"question\":\"How is the protocol validated and what is the target accuracy?\",\"answer\":\"Validation is performed on CO2 hydrogenation to methanol over indium oxide, achieving energy barriers within 0.05 eV of density functional theory.\"},{\"question\":\"What additional effects does the study consider beyond static energy barriers?\",\"answer\":\"It emphasizes finite-temperature effects and computes free energy barriers, with results converged at reaction conditions around 500 K.\"}]","Accurate Energy Barriers for Catalytic Reaction Pathways - 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