[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119740-en":3,"doc-seo-119740-105":30,"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":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},119740,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Predicting ruthenium catalysed hydrogenation of esters using machine learning","Catalytic hydrogenation of esters enables sustainable production of fine chemicals and pharmaceutical drugs, yet catalyst efficiency and cost remain major barriers to commercialization. This study applies multiple machine-learning architectures—neural networks, Gaussian processes, decision trees, random forests, k-nearest neighbors, and linear regression—to predict hydrogenation yields. Optimized models achieve reasonable prediction errors, including an RMSE of 11.76% from Gaussian processes on unseen data, and indicate that certain chemical descriptors, such as electronic parameters, can improve accuracy. The work also supports prediction of catalysts and reaction conditions like temperature and pressure, validated through hydrogenation experiments to address low dataset yields.","Digital  \nDiscovery  \nPAPER  \nCite this: DOI: 10 .1039/d3dd00029j  \nReceived 4th March 2023  \nAccepted 24th April 2023  \nDOI: 10.1039/d3dd00029j[rsc.li/digitaldiscovery](rsc.li/digitaldiscovery)  \nPredicting ruthenium catalysed hydrogenation of esters using machine learning†  \nChallenger Mishra,*a Niklas von Wolff,  *b Abhinav Tripathi,c Claire N. Brodie,  c Neil D. Lawrence,a Aditya Ravuri,a ´Eric Brmond, d Annika Preissc  \nand Amit Kumar  *c  \nCatalytic hydrogenation of esters is a sustainable approach for the production of fine chemicals, and pharmaceutical drugs. However, the efficiency and cost of catalysts are often bottlenecks in the commercialization of such technologies. The conventional approach to catalyst discovery is based on empiricism, which makes the discovery process time-consuming and expensive. There is an urgent need to develop effective approaches to discover efficient catalysts for hydrogenation reactions. In this work, we explore the approach of machine learning to predict outcomes of catalytic hydrogenation of esters using various ML architectures – NN, GP, decision tree, random forest, KNN, and linear regression. Our optimized models can predict the reaction yields with reasonable error for example, a root mean square error (RMSE) of 11.76% using GP on unseen data and suggest that the use of certain chemical descriptors (e.g. electronic parameters) selectively can result in a more accurate model. Furthermore, studies have also been carried out for the prediction of catalysts and reaction conditions such as temperature and pressure as well as their validation by performing hydrogenation reactions to improve the poor yields described in the dataset.  \n1 Introduction  \nThe catalytic hydrogenation of esters to alcohols is an atomeconomic and sustainable approach in organic synthesis with signi􀀁cant applications in the production of various 􀀁ne chemicals such as detergents, cosmetics, 􀀃avors, fragrances, and pharmaceutical drugs.1 The concept has also been expanded to the hydrogenation of polyesters to enable a circular economy.2 In the past, several homogeneous and heterogeneous catalysts have been developed, among which well-de􀀁ned ruthenium complexes represent the state-of-the-art catalysts for the hydrogenation of esters to alcohols.1,3 However, most of such catalysts exhibit low TONs (e.g. \u003C200), and operate under harsh conditions (e.g. temperature > 100 °C, and pressure > 20 bars) making this approach expensive and incompatible for molecules containing other sensitive or reducible functional groups. Thus, the true utilization of hydrogenation methodology relies on 􀀁nding an optimum catalyst that can  \naDepartment of Computer Science and Technology, University of Cambridge,  \nCambridge, CB30FD, UK. E-mail: [cm2099@cam.ac.uk](cm2099@cam.ac.uk)  \nbLaboratoire d'Electrochimie Molculaire, Universit Paris Cit, CNRS, F-75006 Paris, France. E-mail: niklas.von-wolff@[u-paris.fr](u-paris.fr)  \ncSchool of Chemistry, University of St. Andrews, St. Andrews, KY169ST, UK. E-mail: [ak336@st-andrews.ac.uk](ak336@st-andrews.ac.uk)  \ndITODYS, Universit Paris Cit, CNRS, F-75006 Paris, France  \n† Electronic supplementary information (ESI) available. See DOI:  \n[https://doi.org/10.1039/d3dd00029j](https://doi.org/10.1039/d3dd00029j)  \nhydrogenate an ester with high activity and selectivity under mild conditions (e.g., room temperature, and ambient pressure) . Our current conventional approach to catalysis development fails to achieve this due to a number of limitations such as (a) empirical screening of several parameters such as solvent, temperature, pressure, time, additive, etc. can only be limited toa certain extent,(b) syntheses of well-de􀀁ned ruthenium catalysts o􀀁en involve complex multi-step processes limiting the scope of complexes that can be studied,(c) lack of mechanistic understanding of new complexes limits its application in catalysis, and (d) intrinsic limitation of the human brain to 􀀁nda pattern in large data col","cbCaidpLvFzQKI7A","https://ap.wps.com/l/cbCaidpLvFzQKI7A","pdf",1074097,1,9,"English","en",105,"# Introduction","[{\"question\":\"What problem does the document address in ester hydrogenation catalyst discovery?\",\"answer\":\"It addresses bottlenecks in efficiency and cost of catalysts and the time-consuming, expensive nature of conventional empirical catalyst discovery for hydrogenation reactions.\"},{\"question\":\"Which machine-learning models are used to predict reaction outcomes?\",\"answer\":\"The work explores neural networks, Gaussian processes, decision trees, random forests, KNN, and linear regression to predict hydrogenation yields.\"},{\"question\":\"What factors improved prediction accuracy in the optimized models?\",\"answer\":\"Using certain chemical descriptors—particularly electronic parameters—was shown to selectively improve model accuracy.\"}]","Predicting ruthenium catalysed hydrogenation of esters using machine learning | 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problem does the document address in ester hydrogenation catalyst discovery?","Question",{"text":76,"@type":77},"It addresses bottlenecks in efficiency and cost of catalysts and the time-consuming, expensive nature of conventional empirical catalyst discovery for hydrogenation reactions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine-learning models are used to predict reaction outcomes?",{"text":81,"@type":77},"The work explores neural networks, Gaussian processes, decision trees, random forests, KNN, and linear regression to predict hydrogenation yields.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors improved prediction accuracy in the optimized models?",{"text":85,"@type":77},"Using certain chemical descriptors—particularly electronic parameters—was shown to selectively improve model 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