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.

非技术摘要材料研究部门和化学部门为该奖项提供资金。该SusChEM项目涉及将纤维素生物质转化为生物燃料作为可持续能源和新材料可持续原料的计算研究。从多年生植物如草中提取的生物燃料是最理想的，因为这些植物生长在边缘土地上，可以反复收获。经济利用植物生物质的一个主要障碍是纤维素原纤维对促进其转化为可用燃料的预处理的抗性。关于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都知之甚少。该团队将使用高质量的量子力学计算机模拟来开发精确的计算模型来描述这些相互作用，其最终目标是提高生物质转化的效率，并用于纤维素的发现和建模的应用，研究团队将邀请本科生和研究生参与可持续发展研究，旨在找到解决方案，以实现碳排放与碳平衡。隔离学生将有机会参观橡树岭国家实验室，并在政府实验室体验研究。该团队将开发一个独立的计算化学USB记忆棒，其中包含用于执行电子结构和其他建模的软件包。 所包含的计算机程序将包括许多可以在没有分子量子力学详细知识的情况下使用的程序。该团队将把USB记忆棒传播到地区大学，并帮助教师将建模纳入他们的课堂。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。材料研究部和化学部为该奖项提供资金。通过这个SusChEM项目，研究小组将开发一个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，并将其应用于可持续能源和可持续纤维素基材料的发现。研究小组将通过使用自适应力匹配方法拟合精确的电子结构力来开发纤维素的精确势能。通过迭代过程，自适应力匹配提供了高质量的参考力和代表性的训练集用于拟合。这允许在不使用非常复杂的能量表达式的情况下开发精确的力场。因此，可以有效地对较大的结构进行建模。一旦自适应力匹配纤维素力场是可用的，一个准确的粗粒度的潜力将使用多尺度粗粒度的方法。粗粒度的潜力将允许长纤维素原纤维和原纤维束被建模。纤维素的潜力将开发与电子结构信息作为输入。该模型将被验证，重现实验性质，如晶格常数和旋转异构体分布。 经验证的全原子和粗粒度的潜力将被用来解决纤维素原纤维的基本问题，包括在原纤维中的链的数量，扭转的趋势，旋转异构体在界面处的构象，多晶型物转化的自由能，和原纤维的持久性长度。 准确的多尺度潜力开发将使可靠的建模水合纤维素原纤维，并在建模纤维素生物质和纤维素基材料的进一步研究。纤维素力场将是开发其他模型的垫脚石，这些模型涉及替代溶剂，如离子液体和酶。精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展。自适应力匹配的发展成为一个可靠的协议，映射一个昂贵的电子结构的潜力，一个简单的分子力学力场将有广泛的影响，一般的材料研究。该奖项还支持将计算建模融入化学教育的教育活动。一个独立的计算化学USB记忆棒将被开发，以方便计算机建模的本科生和使用计算机建模在教室里。无需安装或许可，允许用户专注于问题而不是计算细节。PI将把USB记忆棒分发给地区大学，并帮助教师在课堂上进行建模。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。该项目将吸引本科生和研究生参与碳中和可持续性研究，并为他们提供参观橡树岭国家实验室和体验国家实验室研究的机会。