[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119675-en":3,"doc-seo-119675-105":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":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},119675,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Machine Learning in Thermochemistry - Unleashing Predictive Modelling for Enhanced Understanding of Chemical Systems","Machine learning (ML) is transforming thermochemistry by enabling predictive modelling that improves the accuracy and efficiency of thermodynamic property estimation for chemical systems. By applying ML algorithms to fundamental quantities such as enthalpy, entropy, heat capacity, and free energy, researchers gain deeper insight into reaction energetics and molecular stability. The work also addresses the ethical dimensions of using ML, emphasizing openness, accountability, responsible deployment, and bias prevention to maintain scientific integrity. ML thereby supports faster discovery and strengthened understanding of chemical thermodynamics.","Machine Learning in Thermochemistry: Unleashing Predictive Modelling for Enhanced Understanding of Chemical Systems  \nHumphrey Sam Samuel, Emmanuel Edet Etim*, John Paul Shinggu. Bulus Bako Received: 23 December 2023/Accepted: 18 March 2024 /Published:29 March 2024  \nAbstract: Machine Learning (ML) has become a game-changing tool in many scientific sectors, altering research and spurring progress in a wide range of fields. The incorporation of ML approaches has created new predictive modelling opportunities in the context of thermochemistry, enabling more accurate and efficient prediction of the thermodynamic parameters of chemical systems. The article emphasizes the use of machine learning techniques in thermochemistry, highlighting the potential advantages and difficulties encountered in this quickly expanding field. The application of these algorithms helps in the prediction of fundamental thermodynamic quantities, including enthalpy, entropy, heat capacity, and free energy, allowing researchers to learn more about the energetics of chemical reactions and the stability of intricate molecular systems. The article also discusses openness, accountability, and the appropriate use of these formidable tools to ensure scientific integrity and prevent potential biases. These issues are related to the ethical problems linked with the application of ML in thermochemistry. As a result of the application of machine learning to thermochemistry research, a new era of predictive modelling has begun, offering a variety of opportunities to understand the intricate workings of chemical systems. ML provides enormous promise for expediting scientific discovery and improving our comprehension of thermodynamics in chemistry by eliminating obstacles and incorporating moral principles.  \nKeywords: Machine learning,  \nthermochemistry, artificial intelligence   \nHumphrey Sam Samuel  \nComputational Astrochemistry and BioSimulation Research Group, Federal University Wukari  \nDepartment of Chemical Sciences, Federal University Wukari, Taraba State  \nEmail: [humphreysedeke@gmail.com](humphreysedeke@gmail.com)  \n[Orcid: 0009-0001-7480-4234](Orcid: 0009-0001-7480-4234)  \nEmmanuel Edet Etim  \nComputational Astrochemistry and BioSimulation Research Group, Federal University Wukari  \nDepartment of Chemical Sciences, Federal University Wukari, Taraba State  \nEmail: [emmaetim@gmail.com](emmaetim@gmail.com)  \n[Orcid: 0000-0001-8304-9771](Orcid: 0000-0001-8304-9771)[ ](Orcid: 0000-0001-8304-9771)John Paul Shinggu  \nComputational Astrochemistry and BioSimulation Research Group, Federal University Wukari  \nDepartment of Chemical Sciences, Federal University Wukari, Taraba State  \n[Email: ](Email: Johnshinggu@gmail.com)[Johnshinggu@gmail.com](Email: Johnshinggu@gmail.com)  \n[Orcid: 0009-0005-2216-3155](Orcid: 0009-0005-2216-3155)  \nBulus Bako  \nComputational Astrochemistry and BioSimulation Research Group, Department of Chemical Sciences, Federal University Wukari, Taraba State  \n[Email: ](Email: bakobulus01@gmail.com)[bakobulus01@gmail.com](Email: bakobulus01@gmail.com)  \nOrcid: 0009-0001-3946-0712  \n1.0 Introduction  \nThe study of the heat energy changes that take place during chemical reactions and other physical processes is the focus of the discipline of physical chemistry known as thermochemistry. Chemical engineering, material science, pharmaceuticals, and environmental chemistry are some scientific and commercial fields that rely on thermochemistry to a good extent. Thermochemistry, a subfield of physical chemistry, studies the energy changes that take place during physical and chemical processes. Understanding the stability, reactivity, and behaviour of chemical systems depends on the precise prediction of thermodynamic parameters (Oliveira, et al., 2022) . The exploration of huge chemical regions was previously constrained by the time-consuming and complicated experimental procedures required to achieve these properties. With its invaluable insights into ","cbCaikHJLHD4fNOE","https://ap.wps.com/l/cbCaikHJLHD4fNOE","pdf",585436,1,26,"English","en",105,"# Introduction","[{\"question\":\"How does machine learning improve predictive modelling in thermochemistry?\",\"answer\":\"Machine learning enables data-driven predictive modelling of thermodynamic parameters, helping achieve more accurate and efficient predictions than traditional approaches.\"},{\"question\":\"Which thermodynamic quantities are targeted for prediction in the research?\",\"answer\":\"The document highlights predictions for enthalpy, entropy, heat capacity, and free energy, supporting a better understanding of chemical reaction energetics.\"},{\"question\":\"What ethical concerns are discussed regarding ML in thermochemistry?\",\"answer\":\"The text emphasizes transparency, accountability, appropriate use of ML tools, and steps to prevent potential biases to protect scientific integrity.\"}]","Machine Learning in Thermochemistry - 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does machine learning improve predictive modelling in thermochemistry?","Question",{"text":75,"@type":76},"Machine learning enables data-driven predictive modelling of thermodynamic parameters, helping achieve more accurate and efficient predictions than traditional approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which thermodynamic quantities are targeted for prediction in the research?",{"text":80,"@type":76},"The document highlights predictions for enthalpy, entropy, heat capacity, and free energy, supporting a better understanding of chemical reaction energetics.",{"name":82,"@type":73,"acceptedAnswer":83},"What ethical concerns are discussed regarding ML in thermochemistry?",{"text":84,"@type":76},"The text emphasizes transparency, accountability, appropriate use of ML tools, and steps to prevent potential biases to protect scientific 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