CAREER: Development of Adaptive and Efficient Computational Inverse Design Methods for Organic Functional Materials

职业：有机功能材料自适应高效计算逆向设计方法的开发

• 批准号: 2339804
• 负责人: Daniel Tabor
• 金额: $ 62.5万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-15 至 2028-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339804&HistoricalAwards=false
• 关键词: CAREER; Development; Adaptive; Efficient; Computational

With support from the Chemical Theory, Models and Computational Methods Program in the Division of Chemistry, Daniel Tabor of Texas A&M University is developing computational simulation and machine learning tools for accelerating the discovery and design of functional materials that interact with light. These materials, which can be built from inexpensive, earth-abundant elements, are needed in future energy storage applications and to build flexible next-generation electronic devices. However, the design of molecules that make up these devices is challenging, as searching for molecules that have all the necessary properties is like searching for a needle in a haystack. To overcome the current challenges in these searches, Daniel Tabor and his research group will integrate machine learning and artificial intelligence methods to build new searching methods that efficiently propose and test new molecules through computer simulations. The Tabor group will develop a set of interactive educational modules to deepen the connection that students have between their understanding fundamentals of light and its role in modern materials science and contemporary issues in data science. The group will develop interactive spectroscopy analysis modules for all levels of instruction, including for high school, undergraduate, and graduate students. Daniel Tabor and his research group will develop a suite of machine learning tools for accelerating the inverse design of organic functional materials, particularly for organic optoelectronic materials and metastable photoacids. The focus of the methods development efforts will be on integrating new physically informed representations for molecular materials with adaptive reinforcement learning algorithms and unsupervised learning methods to form a closed computational discovery loop. The group will build new types of representations for modularly constructed, conjugated materials, implement and test the performance of generative models coupled to reinforcement learning methods on a broad and diverse class of inverse design problems. In addition, the group will expand the utility of unsupervised learning algorithms in chemistry applications, by incorporating chemical information and learning from real-time quantum chemistry characterization of newly identified chemical modules. These simulations predict properties that can be tested in experiments, and the artificial intelligence methods will provide a set of useable rules for what kinds of molecules generally are more useful for electronic applications, empowering chemists to use them in other design applications.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.

在化学理论，模型和计算方法计划的支持下，得克萨斯州A＆M大学的丹尼尔·塔博尔（Daniel Tabor）正在开发计算模拟和机器学习工具，以加速与光相互作用的功能材料的发现和设计。这些材料可以由廉价的，丰富的元素构建，在将来的储能应用中需要，并构建灵活的下一代电子设备。但是，构成这些设备的分子的设计是具有挑战性的，因为寻找具有所有必要特性的分子就像在干草堆中寻找针头一样。为了克服这些搜索中当前的挑战，丹尼尔·塔博尔（Daniel Tabor）及其研究小组将整合机器学习和人工智能方法，以构建新的搜索方法，从而有效地通过计算机模拟提出和测试新分子。 Tabor小组将开发一组互动教育模块，以加深学生在了解光的基础知识与现代材料科学中的作用与当代在数据科学中的作用之间的联系。该小组将针对所有级别的教学级别开发交互式光谱分析模块，包括高中，本科生和研究生。 丹尼尔·塔博尔（Daniel Tabor）和他的研究小组将开发一套机器学习工具，以加速有机功能材料的逆设计，尤其是用于有机光电材料和亚稳态光acid。方法开发工作的重点将是将分子材料的新物理知情表示形式与适应性增强学习算法和无监督的学习方法整合在一起，以形成封闭的计算发现循环。该小组将为模块化构造，共轭材料建立新类型的表示形式，实施和测试生成模型的性能，并在广泛而多样化的逆设计问题上加固学习方法。 此外，该小组将通过合并化学信息和从新鉴定的化学模块的实时量子化学表征中纳入化学信息和学习，扩大化学应用中无监督学习算法的实用性。 这些模拟预测可以在实验中进行测试的属性，并且人工智能方法将为哪些分子通常对电子应用更有用，使化学家能够在其他设计应用中使用它们。该奖项反映了NSF的法定任务，反映了通过评估基金会的评估和广泛的影响力和广泛的范围。