SusChEM: Multiscale Interaction Potentials for Cellulose

SusChEM：纤维素的多尺度相互作用势

• 批准号: 1609650
• 负责人: Feng Wang
• 金额: $ 40.29万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609650&HistoricalAwards=false
• 关键词: SusChEM; Multiscale; Interaction; Potentials; Cellulose

NON-TECHNICAL SUMMARYThe Division of Materials Research and the Chemistry Division contribute funds to this award. This SusChEM project involves computational research on the conversion of cellulosic biomass to biofuel as a sustainable energy source and a sustainable feedstock for new materials. Biofuel derived from perennial plants, such as grass, is most desirable since these plants grow on marginal land and can be harvested repeatedly. One major roadblock for economical utilization of plant biomass is the resistance of cellulosic fibrils to pretreatment to facilitate their conversion to usable fuels. Many questions regarding the detailed structures of fibrils and their interactions with water, other chemical solvents, and enzymes are poorly understood. The team will use high quality quantum mechanical computer simulations to develop accurate computational models to describe these interactions, with the ultimate goal of improving the efficiency of biomass conversion and for applications for the discovery and modeling of cellulose-based materials.The research team will engage undergraduate and graduate students in sustainability research which aims to find solutions to enable the balance of carbon emission with carbon sequestration. Students will have the opportunity to visit Oak Ridge National Laboratory and experience research in a government laboratory. The team will develop a self-contained computational chemistry USB memory stick with packages to perform electronic structure and other modeling. The computer programs contained will include many that can be used without a detailed knowledge of molecular quantum mechanics. The team will disseminate the USB memory stick to regional colleges and help the faculty to incorporate modeling in their classrooms. The PI will also develop modeling modules and tutorials to teach concepts in physical and organic chemistry curricula. TECHNICAL SUMMARYThe Division of Materials Research and the Chemistry Division contribute funds to this award. Through this SusChEM project, the research team will develop a multiscale model for cellulose fibrils and investigate fundamental properties of fibrils and fiber bundles with application to sustainable energy and the discovery of sustainable cellulose-based materials. The research team will develop an accurate potential for cellulose by fitting to accurate electronic structure forces using the adaptive force matching method. Through an iterative procedure, adaptive force matching provides both high quality reference forces and representative training sets for fitting. This allows accurate force fields to be developed without using very complex energy expressions. As a consequence, larger structures can be modeled efficiently. Once the adaptive force matching cellulose force field is available, an accurate coarse-grained potential will be developed using the multiscale coarse-graining approach. The coarse-grained potential will allow long cellulose fibrils and fibril bundles to be modeled. The cellulose potential will be developed with only electronic structure information as input. The model will be validated to reproduce experimental properties, such as lattice constants and rotamer distributions. The validated all-atom and coarse-grained potentials will be used to address fundamental problems of cellulosic fibrils, including the number of chains in a fibril, the tendency for twisting, rotamer conformations at the interface, free energy of polymorph transformations, and the persistence length of the fibril. The accurate multiscale potentials to be developed will enable reliable modeling of hydrated cellulosic fibrils, and further research in modeling cellulosic biomass and cellulose-based materials. The cellulose force field will be a stepping stone for the developments of additional models that involve alternative solvents, such as ionic liquids, and enzymes. Successful development of an accurate cellulose potential will represent a major advance of adaptive force matching. The development of adaptive force matching into a reliable protocol for mapping an expensive electronic structure potential to a simple molecular mechanics force field will have broad impact for material research in general. This award also supports educational activities to integrate computational modeling into Chemistry education. A self-contained computational chemistry USB memory stick will be developed to facilitate computer modeling by undergraduate students and the use of computer modeling in classrooms. No installation or licensing is needed, allowing the user to focus on the problem instead of computational details. The PI will disseminate the USB memory stick to regional colleges and help the faculty incorporate modeling in their classrooms. The PI will also develop modeling modules and tutorials to teach important concepts in physical and organic chemistry. The PIs will engage undergraduate and graduate students in carbon neutral sustainability research and provide them with the opportunity to visit Oak Ridge National Laboratory and experience research in a national laboratory.

非技术总结材料研究和化学部门为该奖项贡献了资金。该SUSCHEM项目涉及有关将纤维素生物量转化为生物燃料作为可持续能源的计算研究和新材料的可持续原料。源自多年生植物的生物燃料，例如草，是最可取的，因为这些植物在边际土地上生长，并且可以反复收获。经济利用植物生物量的一个主要障碍是纤维素原纤维预处理以促进其转化为可用燃料的抗性。关于原纤维的详细结构及其与水，其他化学溶剂和酶的相互作用的许多问题知之甚少。该团队将使用高质量的量子机械计算机模拟来开发准确的计算模型来描述这些相互作用，以提高生物质转化率的效率，以及用于发现和建模的基于纤维素的材料的应用。研究团队将吸引本科生和研究生的可持续性研究，以在可持续性研究中找到解决方案，以促进与Carbone SequeStration促进碳排放量的解决方案。学生将有机会参观橡树岭国家实验室，并在政府实验室进行研究。该团队将开发一个独立的计算化学USB记忆棒，其中包含软件包，以执行电子结构和其他建模。 包含的计算机程序将包括许多可以使用的，而无需详细了解分子量子力学。该团队将将USB记忆棒传播到区域学院，并帮助教师将建模纳入教室。 PI还将开发建模模块和教程，以教授物理和有机化学课程中的概念。技术总结材料研究和化学部门为该奖项贡献了资金。通过这个Suschem项目，研究团队将开发一个用于纤维素原纤维的多尺度模型，并研究原纤维和纤维束的基本特性，并应用于可持续能源，并发现可持续的基于纤维素的材料。研究团队将使用自适应力匹配方法拟合准确的电子结构力来开发准确的纤维素潜力。通过迭代程序，自适应力匹配既可以提供高质量的参考力和代表性训练集。这允许在不使用非常复杂的能量表达式的情况下开发准确的力场。结果，可以有效地对较大的结构进行建模。一旦可以使用自适应力匹配纤维素力场，就将使用多尺度粗粒方法开发精确的粗粒电位。粗粒电势将允许对长纤维素原纤维和原纤维束进行建模。纤维素电位将仅以电子结构信息作为输入而开发。该模型将经过验证以重现实验性能，例如晶格常数和旋转分布。 经过验证的全原子和粗粒电位将用于解决纤维素原纤维的基本问题，包括原纤维中的链数，扭曲的趋势，界面处的旋转型构象，多晶型变化的自由能以及原纤维的持久性。 要开发的准确多尺度电位将使水合纤维素原纤维的可靠建模以及在建模纤维素生物量和基于纤维素的材料方面的进一步研究。纤维素力场将是用于开发涉及替代溶剂（例如离子液体和酶）的其他模型的垫脚石。准确的纤维素电位的成功开发将代表自适应力匹配的重大进步。将适应性力匹配为可靠的方案的自适应力的发展，用于将昂贵的电子结构映射到简单的分子力学力场中，将对一般的物质研究产生广泛的影响。该奖项还支持将计算建模纳入化学教育的教育活动。将开发一个独立的计算化学USB存储棒，以通过本科生促进计算机建模，并在课堂中使用计算机建模。无需安装或许可，允许用户专注于问题而不是计算细节。 PI将将USB存储棒传播到区域学院，并帮助教师将建模纳入教室。 PI还将开发建模模块和教程，以教授物理和有机化学中的重要概念。 PI将吸引本科生和研究生中性可持续性研究，并为他们提供参观橡树岭国家实验室的机会，并在国家实验室进行研究。