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

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丹尼尔塔博尔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、地球上丰富的元素制成，在未来的能量存储应用中以及建造灵活的下一代电子设备中都是需要的。然而，组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大海捞针一样。为了克服目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，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。塔博尔小组将开发一套互动教育模块，以加深学生对光的理解基础及其在现代材料科学和当代数据科学问题中的作用之间的联系。该小组将开发交互式光谱分析模块，用于所有级别的教学，包括高中，本科和研究生。 丹尼尔塔博尔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将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，以形成一个封闭的计算发现循环。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和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性质，人工智能方法将提供一套可用的规则，用于确定哪种分子通常对电子应用更有用，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查进行评估来支持的搜索.

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.