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.

在化学系化学理论、模型和计算方法项目的支持下，德克萨斯农工大学的 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且储量丰富的元素制成，是未来储能应用和构建灵活的下一代电子设备所需要的。然而，构成这些设备的分子设计具有挑战性，因为寻找具有所有必要特性的分子就像大海捞针一样。为了克服当前这些搜索中的挑战，丹尼尔·塔博尔和他的研究小组将整合机器学习和人工智能方法来构建新的搜索方法，通过计算机模拟有效地提出和测试新分子。泰博尔小组将开发一套交互式教育模块，以加深学生对光的基础知识及其在现代材料科学和数据科学中当代问题中的作用之间的联系。该小组将为各级教学开发交互式光谱分析模块，包括高中、本科生和研究生。 Daniel Tabor 和他的研究小组将开发一套机器学习工具，用于加速有机功能材料的逆向设计，特别是有机光电材料和亚稳态光酸。方法开发工作的重点是将分子材料的新物理信息表示与自适应强化学习算法和无监督学习方法相结合，以形成闭合的计算发现循环。该小组将为模块化构造的共轭材料构建新型表示，在广泛而多样的逆向设计问题上实施和测试与强化学习方法相结合的生成模型的性能。 此外，该小组将通过整合化学信息并从新识别的化学模块的实时量子化学表征中学习，扩大无监督学习算法在化学应用中的效用。 这些模拟预测了可以在实验中测试的特性，人工智能方法将为哪些类型的分子通常对电子应用更有用提供一套可用的规则，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 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。