[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126474-en":3,"doc-seo-126474-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126474,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Barrier Height Prediction by Machine Learning - Correction of Semiempirical Calculations","Different machine learning models are developed to predict density functional theory-quality barrier heights from semiempirical quantum mechanical calculations. The study compares a multitask deep neural network, gradient-boosted trees via XGBoost, and Gaussian process regression, finding mean absolute errors comparable to prior approaches under similar data sizes. The proposed ML corrections support rapid screening of large reaction networks in combustion chemistry and astrochemistry, and highlight that highly impactful features are bespoke predictors for use in future Δ-ML models.","This article is licensed under CC-BY 4.0   \n[pubs.acs.org/JPCA](pubs.acs.org/JPCA)  Article   \nBarrier Height Prediction by Machine Learning Correction of Semiempirical Calculations  \nXabier García-Andrade, Pablo García Tahoces, Jeśus Pérez-Ríos, and Emilio Martínez Ńũnez *  \n Cite This: J. Phys. Chem. A 2023, 127, 2274−2283  \nRead Online  \n\n| ACCESS   | Metrics & More |  |  Article Recommendations |  | *sı Supporting Information |\n| --- | --- | --- | --- | --- | --- |\n\nABSTRACT: Different machine learning (ML) models are proposed in the present work to predict density functional theory-quality barrier heights (BHs) from semiempirical quantum mechanical (SQM) calculations. The ML models include a multitask deep neural network, gradient-boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors are similar to those of previous models considering the same number of data points. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in combustion chemistry or in astrochemistry. Finally, our results show that 70% of the features with the highest impact on  \nmodel output are bespoke predictors. This custom-made set ofpredictors could be employed by future Δ-ML models to improve the quantitative prediction of other reaction properties.  \n1. INTRODUCTION  \nTransition state theory (TST) provides a useful means to study the kinetics of elementary chemical reactions.1 Depending on the specific version, TST requires a more or less exhaustive knowledge of the potential energy surface of the system.2 In the absence of strong tunneling effects, the value of the Gibbs energy of activation ΔG‡ [Gibbs energy difference between the transition state (TS) and the reactant(s)] is sufficient to predict the rate of reaction. At 0 K, ΔG‡ is just the electronic energy difference between the TS and reactant including their zero-point vibrational energies (ZPEs), called the barrier height (BH). Although the BH does not include the thermal correction to enthalpy and the entropic contribution, sometimes it is employed as a proxy for the true Gibbs energy of activation. Nevertheless, predicting highly accurate BHs (of sub-kcal/mol accuracy) requires the use of expensive ab initio methods, such as the gold standard coupled cluster including  \nmodel was trained on a gas-phase organic chemistry (GPOC) data set of 12,000 chemical reactions involving carbon, hydrogen, nitrogen, and oxygen. The calculations were carried out at the DFT ωB97X-D3/def2-TZVP quantum chemistry level, which has been shown to predict BHs with a mean absolute error (MAE) of 3.5 kcal/mol against a CCSD(T)-F12 reference.16 An updated version of the GPOC is available, 16 with BHs calculated at the CCSD(T)-F12 level of theory; in a follow-up work our models will be improved using the newest data set. Green and co-workers recently improved their model using fewer parameters and proper data splits to estimate performance on unseen reactions.8 In addition, Habershon and co-workers employed this basis set to predict rates of chemical reactions.17 Alexandrova and co-workers have also shown that topological descriptors of the quantum mechanical charge density in the reactant state can be used to predict BHs for Diels−Alder reactions.9 Hybrid models combining traditional  \nDownloaded via UNIV DE SANTIAGO DE COMPOSTELA on April 22, 2024 at 07:49:38 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nsingle and double excitations with perturbative triple excitations [CCSD(T)].3 Fortunately, today’s state-of-the-art density functionals predict BHs that are rather close to the accurate CCSD(T),4 thus being the method of choice for modeling large systems. However, even density functional theory (DFT) becomes prohibitive for biochemical systems or  \nfor complex reaction networks","cbCaiomv6jAf27U8","https://ap.wps.com/l/cbCaiomv6jAf27U8","pdf",5081933,12,1,10,"English","en",105,"# Abstract\n## Introduction\n## Machine learning for barrier heights\n## Semiempirical methods and their limitations\n## PM7-TS and benchmarking","[{\"question\":\"Which machine learning models are proposed for barrier height prediction?\",\"answer\":\"The work proposes a multitask deep neural network, gradient-boosted trees using XGBoost, and Gaussian process regression.\"},{\"question\":\"How do the ML model errors compare to previous approaches?\",\"answer\":\"The mean absolute errors are reported to be similar to those of previous models when using the same number of data points.\"},{\"question\":\"Why are semiempirical corrections useful in reaction-network screening?\",\"answer\":\"Semiempirical quantum mechanical methods are faster than DFT, and the ML corrections enable rapid screening of large reaction networks in combustion chemistry and astrochemistry.\"}]","Barrier Height Prediction by Machine Learning - Correction of Semiempirical Calculations | PDF",1785905235,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"barrier-height-prediction-by-machine-learning-correction-of-semiempirical-calculations","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/barrier-height-prediction-by-machine-learning-correction-of-semiempirical-calculations/126474/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Which machine learning models are proposed for barrier height prediction?","Question",{"text":77,"@type":78},"The work proposes a multitask deep neural network, gradient-boosted trees using XGBoost, and Gaussian process regression.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How do the ML model errors compare to previous approaches?",{"text":82,"@type":78},"The mean absolute errors are reported to be similar to those of previous models when using the same number of data points.",{"name":84,"@type":75,"acceptedAnswer":85},"Why are semiempirical corrections useful in reaction-network screening?",{"text":86,"@type":78},"Semiempirical quantum mechanical methods are faster than DFT, and the ML corrections enable rapid screening of large reaction networks in combustion chemistry and astrochemistry.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,122,125,130,133,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":123,"slug":124},30,"research-report",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":128,"slug":129},9,"Religion & Spirituality",20,"religion-spirituality",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":128,"slug":132},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":22,"slug":135},"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]