Machine Learning Tools for Biofuel Creation and Purification using Ionic Fluids

迈阿密大学的Orlando阿切韦多得到了化学系化学理论、模型和计算方法项目的支持，以开发有助于设计生物质衍生燃料的人工智能工具。生物质作为可再生能源具有巨大潜力。例如，据估计，到2030年，20%的运输燃料和25%的化学品将由生物质生产。尽管年产量约为1700亿公吨，但所生产的生物质中不超过5%用于各种目的。从植物材料生产的增值化学品和高质量燃料产品的潜在市场是巨大的。 然而，目前钻探和提炼矿物柴油比种植，提取和纯化生物柴油更具成本效益。被称为离子液体（IL）和低共熔溶剂（DES）的环境友好溶剂有可能作为创造经济可行的生物燃料的解决方案的重要组成部分。阿切韦多博士将开发和应用基于人工智能的软件（即，机器学习）来设计新的离子液体和降解环境系统，有效地将生物质衍生的构建模块转化为液体燃料，并从柴油和生物柴油燃料中提取杂质。该项目将通过强调使用具有成本效益和可回收的离子流体，帮助加速从化石燃料向生物质衍生能源的过渡。阿切韦多博士开发的计算工具将免费提供给科学界。此外，多个社区外展活动将通过位于Ft的科学博物馆的演示，突出人工智能在化学中的应用。劳德代尔和迈阿密，佛罗里达，研究网络社交聚会，并为K-12年级的插图科学诗歌比赛。在第一个主要的研究重点，阿切韦多博士将研究高度通用的生物质衍生的5-羟甲基糠醛（HMF）化合物转化为（a）5，5 '-二使用N-杂环卡宾（NHC）催化的（羟甲基）糠偶姻（DHMF），和（B）2，5-双（羟甲基）呋喃（BHMF）通过Cannizzaro反应。这两种方法都是高效，温和，环保的。为了预测这些HMF转化后溶剂衍生的速率和选择性增强，这里将开发一种新的混合机器学习，量子力学和分子力学（ML/QM/MM）方法，该方法提供了从头计算QM计算的准确性，并大大提高了计算速度。在第二个主要研究方向中，阿切韦多博士将探索使用离子液体和DES从柴油中提取（a）二苯并噻吩和其他芳香族硫化物，以及（B）从生物柴油中提取甘油，生物柴油是植物甘油三酯油转酯反应生成生物燃料过程中产生的废物。离子液体和DES都具有非常适合从运输燃料中提取杂质的特性：不溶于油，无毒，环境友好。为了研究这些燃料为基础的杂质和离子液体/DES之间的有利的分子间相互作用的起源，我们的替代方法将训练人工神经网络（ANN），使用代表性的从头算分子动力学数据和耦合新优化的ANN电位蒙特卡洛模拟。基于机器学习开发的方法将广泛适用于化学研究的其他领域，作为该项目一部分生成的所有软件/产品将随时提供给科学界。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Orlando Acevedo of the University of Miami is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop artificial intelligence tools that will aid in the design of biomass-derived fuels. Biomass has significant potential as a renewable source of energy. For example, it has been estimated that by 2030, 20% of transportation fuel and 25% of chemicals will be produced from biomass. Despite an annual production of around 170 billion metric tons, no more than 5% of this produced biomass is used for diverse purposes. The potential market for value-added chemicals and high-quality fuel products produced from plant materials is immense. However, at present it is more cost-effective to drill and refine mineral diesel oil than grow, extract, and purify biodiesel. Environmentally friendly solvents known as ionic liquids (ILs) and deep eutectic solvents (DESs) have the potential to serve as a significant part of the solution in the creation of economically viable biofuels. Dr. Acevedo will develop and apply artificial intelligence-based software (i.e., machine learning) to design new ILs and DESs that efficiently convert biomass-derived building blocks into liquid fuel and extract impurities from diesel and biodiesel fuels. This project will help accelerate the transition from fossil fuels to biomass-derived energy by emphasizing the use of cost-effective and recyclable ionic fluids. The computational tools developed by Dr. Acevedo will be freely available to the scientific community. In addition, multiple community outreach events will highlight the use of artificial intelligence in chemistry through demonstrations at science museums located in Ft. Lauderdale and Miami, Florida, research networking social gatherings, and an illustrated science poem contest for K-12 grades.In the first major research thrust, Dr. Acevedo will study the conversion of the highly versatile biomass-derived 5-hydroxymethylfurfural (HMF) compound into (a) 5,5’-di(hydroxymethyl)furoin (DHMF) using N-heterocyclic carbene (NHC) catalysis, and (b) 2,5-bis(hydroxymethyl)furan (BHMF) via a Cannizzaro reaction. Both methods are efficient, mild, and eco-friendly. To predict solvent-derived rate and selectivity enhancements upon these HMF transformations, a new mixed machine learning, quantum mechanics, and molecular mechanics (ML/QM/MM) methodology will be developed here that provides the accuracy of ab initio QM calculations with dramatically increased calculation speeds. In the second major research thrust, Dr. Acevedo will explore the use of ILs and DESs to extract (a) dibenzothiophene and other aromatic sulfur-compounds from diesel, and (b) glycerol from biodiesel, a waste product developed during the transesterification of plant triglyceride oil to biofuel. Both ILs and DESs have properties ideally suited for impurity extraction from transportation fuel: insoluble in oil, non-toxic, and environmentally benign. To study the origin of the favorable intermolecular interactions between these fuel-based impurities and ILs/DESs, our alternative method will train artificial neural networks (ANNs) using representative ab initio molecular dynamics data and couple the newly optimized ANN potentials to Monte Carlo simulations. Machine learning based methodology developed will be broadly applicable to other areas of chemical research and all software/products generated as part of this project will be readily available to the scientific community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.