EAGER: ADAPT: AI Guided Design and Synthesis of Semiconducting Molecules

了解原子和分子如何结合形成更复杂的分子和物质是化学的基础。然而，原子排列成分子的方式比宇宙中实际的原子要多。化学家通常依靠经验、文献描述和特别标准来设计和优先考虑特定应用的分子，这通常会导致巨大的努力。此外，当考虑使用经过试验的或未经试验的反应合成新分子时，化学家们必须经常推测，依靠直觉而不是基本事实。pi将应用和扩展人工智能（AI）的几个方面，以开发一个通用平台，促进数据驱动的性能预测和半导体材料的合成，重点是蓝色发光材料。因此，该项目将加快新型有机半导体的发现，这些有机半导体可以有效地合成，并具有针对目标应用的优化特性。将来自pi研究小组的研究生组成的多元化团队纳入这项工作将扩大参与范围，并有助于在化学和材料科学的背景下创建一支了解人工智能的劳动力队伍。研究重点是利用人工智能优化蓝色发光材料的设计。为了实现这一目标，该项目将通过人工智能平台连接两条平行的实验轨道。在第一个轨道上，数据和计算模型将用于训练人工智能机器学习和分子对输入的实验设计模块。这将提供一个完全容器化的工作流程，使设计强大的蓝色发光分子成为可能。在第二个轨道中，重点将放在扩展潜在的化学空间，可以融入到第一个轨道的设计概念中。这将极大地增加可探索的半导体材料的多样性，并为如何制造具有所需性能的新的、未探索的分子框架提供路线图。该项目将结合高维稀疏回归、带有图输入的机器学习和离散优化的概念和技术。由此产生的双模式平台（性能设计/合成设计）将提供前所未有的预测水平，使材料的设计和制造成为一个更高效和自动化的过程。与此同时，新的统计机器学习和实验设计算法有望在解决涉及分子对作为输入的化学问题中出现。这个项目也为材料和计算化学的本科生和研究生的训练提供了新的机会。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding how atoms and molecules combine to form more complex molecules and materials is fundamental to chemistry. Yet, there are more ways that atoms can be arranged into molecules than there are actual atoms in the universe. Chemists typically rely on experience, literature accounts, and ad hoc criteria for designing and prioritizing molecules for specific applications, often resulting in a monumental effort. Furthermore, when considering the synthesis of new molecules using tried or untried reactions, chemists must often speculate, relying on instinct rather than ground truths. The PIs will apply and extend several aspects of artificial intelligence (AI) to develop a general platform that will facilitate data-driven property prediction and synthesis of semiconducting materials, focusing on blue light-emitting materials. As a result, this project will expedite the discovery of novel organic semiconductors that can be synthesized efficiently, with optimized properties for target applications. The inclusion of a diverse team of graduate students in this work from the PIs’ research groups will broaden participation and help create an AI-aware workforce in the context of chemistry and materials science.The research focus will be on optimizing the design of blue light-emitting materials using AI. To carry out this objective, the project will proceed with two parallel experimental tracks connected by an AI platform. In the first track, data and computational models will be used to train AI machine learning and experimental design modules for molecular-pair inputs. This will provide a workflow that is fully containerized, enabling the design of robust blue light-emitting molecules. In the second track, the focus will be on extending the potential chemical space that can be integrated into the first track design concept. This will dramatically increase the diversity of semiconducting materials that can be explored and provide a roadmap for how to make new, unexplored molecular frameworks with desired properties. The project will incorporate concepts and techniques from high-dimensional sparse regression, machine learning with graph inputs, and discrete optimization. The resulting dual mode platform (property design/synthesis design) will provide an unprecedented level of prediction, making the design and manufacturing of materials a more efficient and automated process. At the same time, novel statistical machine learning and experimental design algorithms are expected to emerge in addressing chemistry problems involving molecular pairs as inputs. This project also provides new opportunities for undergraduate and graduate student training in materials and computational chemistry.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.