Novel Nanoporous Materials: In Silico Design

新型纳米多孔材料：计算机设计

• 批准号: 1310258
• 负责人: Coray Colina
• 金额: $ 36万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2016-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1310258&HistoricalAwards=false
• 关键词: Novel; Nanoporous; Materials; Silico; Design

Technical AbstractThis DMR project creates a computational-based design, applied to amorphous microporous materials that complement and dramatically enhance traditional experimental methods. A fundamental understanding of amorphous microporous materials is being generated that will allow new materials to be discovered for the benefit of the general scientific community. This project involves training a new generation of materials scientists who think differently about data. Open-data paradigms in which students/researchers think of their data as a public good to be eventually shared and used by others would transform the materials research enterprise, and catalyzed through this work. The proposed research focuses on investigate three new classes of nanoporous materials through the use of molecular simulations, which will direct chemical synthesis and facilitate the understanding and preparation of novel amorphous materials. Tailoring and optimization of these materials include: 1) Polymer and organic molecules of intrinsic microporosity (PIMs and OMIMs), which will greatly enhance their suitability as heterogeneous catalysts, adsorbents and gas storage materials. 2) Crosslinked polyolefin terpolymers as promising candidates for natural gas (NG) storage, and 3) Stilbene containing alternating copolymers, semi-rigid amorphous copolymers, as new polyelectrolytes and other functionalizations for optical applications. Concurrent to the above goals, is the generation of large-data sets and the extraction of critical information from that data (e.g., structure factors to understand the intrinsic correlations between structure and properties/behavior) to catalyze materials breakthroughs. This work, will generate the knowledge to develop appropriate structure-property relations for novel microporous molecular and polymeric materials, which will result in the design, synthesis and characterization of optimized materials that will possess technological relevance. Additionally, students will receive significant training through close interactions with the PI and program colleagues. Students will benefit from the interaction and immersion in a global collaborative research environment with national and international experts that will complement the intensive training in simulation that they receive at Penn State. The PI plans to empower the next generation of junior researchers by the application of open-data paradigms that include data sharing as a public good, and thus transform the materials research enterprise. This award is funded by the Division of Materials Research in the Mathematical and Physical Sciences Directorate (Computational and Data-Driven Materials Research).Non-Technical AbstractOne of the principal aims of modern science is to use computational methods to help understand and even predict the results from experimentation. Today, exciting opportunities exist for a transformation in the way materials research is conducted, including a data-driven revolution in materials discovery and design.The overarching goal of this research is to create a computational-based design, applied to amorphous microporous materials that complement and dramatically enhance traditional experimental methods. Here, the research includes the improvement of knowledge transfer and facilitation of the development and application of a vast variety of amorphous materials to industrial applications. A fundamental understanding of amorphous microporous materials will be generated that will allow new materials to be discovered for the benefit of the general community. This involves training a new generation of materials scientists who think differently about data. Open-data paradigms in which students think of their data as a public good to be eventually shared and used by others would transform the materials research enterprise, and catalyzed through this work. Additionally, undergraduates and graduate students will benefit from the interaction and immersion in a global collaborative research environment with national and international experts that will complement the intensive training in simulation that they receive at Penn State. In summary, the next generation of junior researchers will be empowered by the application of open-data paradigms that include data sharing as a public good, and thus transform the materials research enterprise.

技术摘要此DMR项目创建了一种基于计算的设计，该设计应用于无定形的微孔材料，可显着增强传统的实验方法。正在产生对无定形微孔材料的基本理解，这将使新材料被发现为普通科学界的利益。该项目涉及培训新一代的材料科学家，他们对数据有所不同。开放数据范式在学生/研究人员认为他们的数据最终被别人共享和使用的公共利益将改变材料研究企业，并通过这项工作催化。拟议的研究重点是通过使用分子模拟来研究三种新的纳米多孔材料，这将指导化学合成并促进新型无定形材料的理解和制备。这些材料的剪裁和优化包括：1）内在微孔度（PIMS和OMIMS）的聚合物和有机分子，这将大大提高其作为异质催化剂，吸附剂和气体存储材料的适合性。 2）交联聚烯聚集素培训剂作为有前途的天然气（NG）储存的有前途的候选物，3）含有交替共聚物，半刚性无定形共聚物，作为新的聚电解质和其他功能化的光学官能化。上述目标的同时是大型数据集的产生，以及从该数据中提取关键信息（例如，结构因素，了解结构与属性/行为/行为之间的固有相关性），以促进材料的突破。 这项工作将产生知识，以开发新的微孔分子和聚合物材料的适当结构 - 特性关系，这将导致具有技术相关性的优化材料的设计，合成和表征。此外，学生将通过与PI和计划同事进行密切互动来获得重大培训。学生将受益于与国家和国际专家的全球合作研究环境中的互动和沉浸，这将补充他们在宾夕法尼亚州接受的模拟中的深入培训。 PI计划通过应用开放数据范式（包括数据共享作为公共利益）的开放数据范式来增强下一代初级研究人员的能力，从而改变了材料研究企业。该奖项由数学和物理科学局（计算和数据驱动的材料研究）的材料研究部资助。现代科学主要目的的非技术摘要是使用计算方法来帮助理解甚至可以从实验中预测结果。如今，进行材料研究的方式进行转型的激动人心的机会，包括材料发现和设计中的数据驱动革命。这项研究的总体目标是创建基于计算的设计，应用于无定形的微孔材料，以补充并大大增强传统实验方法。在这里，研究包括改善知识转移以及促进各种无定形材料在工业应用中的开发和应用。将产生对无定形微孔材料的基本理解，该理解将使新材料被发现是为了一般社区的利益。这涉及培训新一代材料科学家对数据的思考。开放式范式将学生视为最终被别人共享和使用的公共利益，将改变材料研究企业，并通过这项工作催化。此外，本科生和研究生将受益于与国家和国际专家的全球合作研究环境中的互动和沉浸，这将补充他们在宾夕法尼亚州立大学接受的模拟中的深入培训。总而言之，下一代初级研究人员将通过将数据共享作为公共利益的开放数据范式的应用来赋予能力，从而改变了材料研究企业。