[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117125-en":3,"doc-seo-117125-105":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},117125,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Simulating Chemical Reactivity using Machine Learning Potentials and Umbrella Sampling - Efficiently train and run MLPs","Dynamic simulations of reactions underpin predictions of mechanisms and chemical behavior explanation. The thesis addresses limitations of AIMD’s cost and the inadequacy of fixed force fields for bond breaking or forming by developing efficient strategies for machine learning potentials (MLPs). Using and extending umbrella sampling (US), it presents automated training and enhanced sampling workflows, demonstrates ACE MLP studies of terpene reactions and sesquiterpene bifurcation, and incorporates nuclear quantum effects via path integral molecular dynamics.","Simulating Chemical Reactivity using Machine Learning Potentials and Umbrella Sampling  \nTristan Johnston-Wood The Queen’s College University of Oxford  \nA thesis submitted for the degree of Doctor of Philosophy  \nTrinity 2023  \nAbstract  \nDynamic simulations of reactions enable prediction of mechanisms and explanation of chemical phenomena. Traditional methods of simulation include ab initio molecular dynamics (AIMD), which is generally accurate but expensive, and force field methods, which are fast but often unsuitable for exploring bond breaking or forming processes. Machine learning potentials (MLPs) have emerged as a promising tool for exploring dynamics at the accuracy of quantum mechanics and a fraction of the cost of AIMD. However, training often requires many data points and expert knowledge. In this thesis, we introduce strategies to efficiently train and use MLPs to study a range of chemical processes, utilising and extending the umbrella sampling (US) methodology. Chapter 2 presents a training strategy for the generation of MLPs along with the gap-train and mlptrain Python packages developed during this thesis, which automate training and enhanced sampling. In Chapter 3, we illustrate the use of the ACE MLP to study terpene reactions. The generated MLPs accurately predict free energy and product ratios, providing good agreement with experiment and previous computational studies. We explore a proposed bifurcation of a sesquiterpene, using MLPs to confirm its existence. Nuclear quantum effects are introduced using path integral molecular dynamics and MLPs, highlighting examples where quantum dynamics affect the free energy and product ratios in hydrogen migration reactions. Chapter 4 presents a series of metrics to quantify the convergence and overlap in US. We study the influence of overlap on free energy for analytic, MLP and protein systems, finding an overlap of 15% is sufficient to converge free energy. Finally, in Chapter 5, attempts to develop an adaptive US scheme arepresented, along with a Python package, adaptiveus, to automate the process.  \nAcknowledgements  \nOne rarely realises how integral support, discussion and collaboration with others is before embarking on their PhD. However, coming to the end, I owe a great deal to the people mentioned here. First and foremost, I would like to thank Professor Fernanda Duarte, who has provided guidance, detailed feedback and support throughout. During the first half of my DPhil, Tom Young provided support well above the call of duty, teaching me (almost) everything I know about Python and always enthusiastically engaging in scientific discussion, often ending in fiery debates but fruitful ideas. I am now confident I didn’t annoy him too much as I still enjoy a glass of wine with him talking science.  \nI would also like to thank the other members of the Duarte group, especially Alistair for interesting discussions on bonds and orbitals; Matina (µαλα´κα), Tomasz and honorary members Harry and Duthie for guidance early on and general vibes later; and Matthew (freelance broadcaster and commentator) for his distracting and brightening presence. Thank you to the members of the MLP subgroup for their ideas and feedback, including Hanwen, Veronika and Khan. Double trouble Aleksy and Martin have provided a plethora of scientific ideas and fun, as has Kate, even allowing me to feature in her mockumentary.  \nMy first year at Oxford saw many memories created and knowledge gained through TMCS, and I would like to thank my fellow classmates for their help as well as Martin Galpin for as many pints as maths lessons. I have seen many Part II students pass through the group, and I am happy to say how much I enjoyed their presence and the life they gave to the group, including Janko, James, and recently Alex, Valdas and Josh. I have also shared in a number of fruitful  \nacademic collaborations including with Volker, Adrian, G´abor, Julien and Steve.  \nOutside of the group, Lillian h","cbCaiquSonQRFzkZ","https://ap.wps.com/l/cbCaiquSonQRFzkZ","pdf",54599914,1,216,"English","en",105,"# Abstract\n# Chapter 2: MLP training strategy and automated enhanced sampling\n## gap-train and mlptrain\n# Chapter 3: ACE MLP for terpene reactions and bifurcation\n## Free energy and product ratios agreement\n## Nuclear quantum effects via path integral molecular dynamics\n# Chapter 4: Metrics for US convergence and overlap\n## Overlap threshold for free energy convergence\n# Chapter 5: Adaptive umbrella sampling scheme\n## adaptiveus Python package","[{\"question\":\"Why are machine learning potentials used instead of AIMD or force fields?\",\"answer\":\"MLPs target quantum-mechanical accuracy at a fraction of AIMD’s cost, while traditional force fields are often unsuitable for bond breaking or forming processes. This enables broader and more efficient reaction dynamics exploration.\"},{\"question\":\"How does the thesis use umbrella sampling with MLPs?\",\"answer\":\"It extends and utilises umbrella sampling (US) to improve sampling while training and applying MLPs. The work includes methods and automation to generate MLPs and obtain enhanced sampling results.\"},{\"question\":\"What does the thesis find about umbrella-sampling overlap and free energy convergence?\",\"answer\":\"It introduces metrics to quantify convergence and overlap in US and studies how overlap affects free energy across different system types. The thesis reports that an overlap of 15% is sufficient to converge free energy.\"}]","Simulating Chemical Reactivity using Machine Learning Potentials and Umbrella Sampling - Efficiently train and run MLPs | PDF",1785674006,544,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"simulating-chemical-reactivity-using-machine-learning-potentials-and-umbrella-sampling-efficiently-train-and-run-mlps","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/simulating-chemical-reactivity-using-machine-learning-potentials-and-umbrella-sampling-efficiently-train-and-run-mlps/117125/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are machine learning potentials used instead of AIMD or force fields?","Question",{"text":75,"@type":76},"MLPs target quantum-mechanical accuracy at a fraction of AIMD’s cost, while traditional force fields are often unsuitable for bond breaking or forming processes. This enables broader and more efficient reaction dynamics exploration.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis use umbrella sampling with MLPs?",{"text":80,"@type":76},"It extends and utilises umbrella sampling (US) to improve sampling while training and applying MLPs. The work includes methods and automation to generate MLPs and obtain enhanced sampling results.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the thesis find about umbrella-sampling overlap and free energy convergence?",{"text":84,"@type":76},"It introduces metrics to quantify convergence and overlap in US and studies how overlap affects free energy across different system types. 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