[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117591-en":3,"doc-seo-117591-105":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},117591,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning - Software Development for Sustainable Chemistry - Doctor of Philosophy Thesis","Sustainability is a central challenge for chemical synthesis in the 21st century, as conventional routes can depend on practices harmful to human health and the environment. The thesis examines how digitalisation enables intelligent chemist-focused tools, moving beyond paper lab notebooks toward accessible ELNs. It introduces machine learning as a means to build predictive models from high-quality data, and describes AI4Green ELN development and integration of ML with sustainability assessment to avoid duplicated data entry.","Machine Learning & Software Development for Sustainable Chemistry  \nJoseph C. Davies  \nThesis submitted to the University of Nottingham for the degree  \nof Doctor of Philosophy  \nJanuary 2025  \nAbstract  \nSustainability represents one of the most pressing challenges for chemical synthesis in the 21st century. Traditional methods often rely on non-sustainable practices, including the use of chemicals that are harmful to human health and the environment. Significant efforts have been made to improve the sustainability of chemical synthesis and finding greener synthetic routes is a common aim for researchers.  \nDigitalisation presents an opportunity to embed intelligent tools into the workflows of chemists. Many academic researchers continue to use paper lab notebooks, highlighting the need for accessible electronic laboratory notebooks (ELNs) tailored to their needs. Machine learning can be used to create predictive models from highquality data, offering a powerful approach to enhancing these tools.  \nIn this thesis, software tools for sustainable chemistry are explored, and machine learning theory and its application to chemistry is introduced and exemplified. The development of the AI4Green ELN and the integration of machine learning models with an accompanying sustainability assessment is described. Integrating software and machine learning tools for sustainable chemistry directly into the ELN can help chemists measure and improve their sustainability without requiring duplicated data entry. The ELN captures reaction data in a structured, machine-readable format, facilitating the development of additional tools and modernising research data management.  \nPublications  \n1. Joseph C. Davies, David Pattison, Jonathan D. Hirst, Machine learning for yield prediction for chemical reactions using in situ sensors, J. Mol. Graph. Model., 2023, 118, 108356  \n2. Samuel Boobier, Joseph C. Davies, Ivan N. Derbenev, Christopher M. Handley, Jonathan D. Hirst, AI4Green: an open-source ELN for green and sustainable chemistry, J. Chem. Inf. Model., 2023, 63 (10), 2895-2901  \n3. Joseph C. Davies and Jonathan D. Hirst, in Reference Module in Chemistry, Molecular Sciences and Chemical Engineering, 2024. Ed. Béla Török, ISBN 9780124095472.  \n4. Ton M. Blackshaw, Joseph C. Davies, Kristian T. Spoerer, Jonathan D. Hirst, Enhancing Monte Carlo Tree Search for Retrosynthesis, J. Chem. Inf. Model., 2025, ASAP  \nAcknowledgements  \nFirst, I would like to thank my supervisor Jonathan Hirst for his constant support and for giving me this opportunity in the first place. You have given me excellent opportunities and always trusted me and encouraged me to push myself. I appreciate you always making time to support when needed and overall being a great person to work with professionally and personally. I have learnt a lot, and I am very grateful to you for that. You have also consistently and very patiently reminded me that these data are plural.  \nI am very thankful to everyone involved with the AI4Green project, all the stakeholders and everyone who has taken time to give feedback and engage with the project, but especially the project team. Thanks to Christopher Handley, Ivan Derbenev, and Sam Boobier who were all incredibly patient and taught me a lot when I was new to computational work. Similarly, thanks to many people in the DRS, particularly Phil Van Krimpen and Andy Rae who also helped and taught me a lot. A big thanks to the current team as well: Joe Heeley, Peace Nwafor, Zak Siddiq, Maddy Parker, Ton Blackshaw, and George Marshall. I have enjoyed working with all of you. Joe Heeley, we have worked together a lot since you joined the team and have proven Joes are stronger together. Thanks to DeepMatter for their provision of data and David Pattison for his guidance and data science expertise.  \nThanks to everyone in the Hirst group and the computational chemistry department. I have enjoyed the occasional cryptic crossword, redactle and debates around","cbCaiikJjGs81nZk","https://ap.wps.com/l/cbCaiikJjGs81nZk","pdf",3831244,1,225,"English","en",105,"# Abstract\n# Publications\n# Acknowledgements\n# List of Figures\n# List of Tables\n# List of Abbreviations\n# Software Tools for Sustainable Chemistry\n## Abstract\n## Introduction\n## Green and Sustainable Chemistry\n## Green Metrics","[{\"question\":\"Why does the thesis focus on sustainability in chemical synthesis?\",\"answer\":\"Because traditional synthesis methods often rely on non-sustainable practices that can harm human health and the environment, while greener synthetic routes are a common research goal.\"},{\"question\":\"How does the thesis connect digitalisation, ELNs, and machine learning?\",\"answer\":\"It argues that digitalisation can embed intelligent tools into chemists’ workflows, using electronic laboratory notebooks and machine learning models built from structured, high-quality data.\"},{\"question\":\"What is AI4Green and what role does it play?\",\"answer\":\"AI4Green is described as an open-source electronic laboratory notebook for green and sustainable chemistry, integrating reaction data capture in a structured, machine-readable way and supporting ML-driven sustainability assessment.\"}]","Machine Learning - 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