[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120089-en":3,"doc-seo-120089-105":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},120089,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Computational Advances in Ionic Liquid Applications for Green Chemistry - A Critical Review of Lignin Processing and Machine Learning Approaches","The valorization and dissolution of lignin using ionic liquids (ILs) is critical for sustainable biorefineries and a circular bioeconomy. This review critically evaluates computational and machine learning methods published since 2022 for IL-based lignin dissolution and valorization, spanning approaches from quantum chemistry to ML. It summarizes strengths, limitations, and recent advances in predicting and optimizing lignin–IL interactions, emphasizing challenges in modeling lignin’s complex structure, efficient IL screening, and multiscale integration to accelerate discovery of novel systems.","Lawrence Berkeley National Laboratory  \nLBL Publications  \nTitle  \nComputational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches  \nPermalink  \n[https://escholarship.org/uc/item/98n2m2m6](https://escholarship.org/uc/item/98n2m2m6)  \nJournal  \nMolecules, 29(21)  \nISSN  \n1431-5157  \nAuthors  \nTaylor, Brian R  \nKumar, Nikhil  \nMishra, Dhirendra Kumar et al.  \nPublication Date  \n2024  \nDOI  \n10.3390/molecules29215073  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n molecules   \nReview  \nComputational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches  \nBrian R. Taylor 1,2,†, Nikhil Kumar 1,2,†, Dhirendra Kumar Mishra 1,3,†, Blake A. Simmons 1,4, Hemant Choudhary 1,3, * and Kenneth L. Sale 1,2, *  \nCitation: Taylor, B.R.; Kumar, N.; Mishra, D.K.; Simmons, B.A.;  \nChoudhary, H.; Sale, K.L. Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches. Molecules 2024, 29, 5073. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)molecules29215073  \nAcademic Editors: Pradip K. Bhowmik and Biplab Banerjee  \nReceived: 1 October 2024  \nRevised: 22 October 2024  \nAccepted: 25 October 2024  \nPublished: 26 October 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Joint BioEnergy Institute, Emeryville, CA 94608, USA  \n2 Department of Biosecurity and Bioassurance, Sandia National Laboratories, Livermore, CA 94551, USA  \n3 Department of Bioresource and Environmental Security, Sandia National Laboratories, Livermore, CA 94550, USA  \n4 Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA  \n* Correspondence: hchoudhary@lbl.gov or hchoudh@sandia.gov (H.C.);  \nklsale@lbl.gov or klsale@sandia.gov (K.L.S.)† These authors contributed equally to this work.  \nAbstract: The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022 . The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.  \nKeywords: Density Functional Theory (DFT); biomass processing; lignocellulosic biorefineries; lignin depolymerization; reactive force fields (ReaxFF); solvent screening; quantum chemistry; multiscale modeling  \n1. Introduction  \nThe need for sustainable alternatives to fossil-based resources has driven significant interest in lignocellulosic biomass as a renewable feedstock for biofuels and chemicals. Lignin, which constitutes 15–35%[1] of this feedstock, offers significant potential as a source for high-value products such as biofuels [2], materials [","cbCaihZk8uxg3NG6","https://ap.wps.com/l/cbCaihZk8uxg3NG6","pdf",698835,1,17,"English","en",105,"# Introduction\n## Sustainability drivers and lignocellulosic biomass\n## Challenges in lignin valorization\n## Promise of ionic liquids for lignin dissolution\n## Computational screening opportunities","[{\"question\":\"What problem does the review focus on?\",\"answer\":\"The review focuses on computational and machine learning methods for understanding and optimizing how ionic liquids dissolve and valorize lignin for greener biorefineries.\"},{\"question\":\"Why are ionic liquids important for lignin processing?\",\"answer\":\"Ionic liquids are promising because they are thermostable, can dissolve diverse substances, have tunable cation–anion structures, and exhibit low vapor pressure, helping overcome lignin solubility and depolymerization challenges.\"},{\"question\":\"What main computational themes are highlighted?\",\"answer\":\"The review highlights modeling approaches from quantum chemistry to machine learning, addressing strengths and limitations for predicting lignin–IL interactions, efficient solvent/IL screening, and integrating ML with quantum calculations.\"}]","Computational Advances in Ionic Liquid Applications for Green Chemistry - 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