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

EAGER：ADAPT：人工智能引导半导体分子的设计和合成

• 批准号: 2141384
• 负责人: Eric Kolaczyk
• 金额: $ 30万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2141384&HistoricalAwards=false
• 关键词: EAGER; ADAPT; AI; Guided; Design

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.

了解原子和分子如何结合形成更复杂的分子和材料是化学基础的。然而，与宇宙中的实际原子相比，原子可以排列到分子中的方法更多。化学家通常依靠经验，文献叙述和临时标准来设计和优先针对特定应用的分子，这通常会导致巨大的努力。此外，当考虑使用未经尝试的反应或未经尝试的反应考虑新分子时，化学家必须经常推测，依靠本能而不是地面真理。 PI将应用并扩展人工智能（AI）的几个方面，以开发一个通用平台，该平台将促进数据驱动的财产预测和半导体材料的合成，重点关注蓝色发光材料。结果，该项目将加快发现可以有效合成的新型有机半导体的发现，并具有针对目标应用的优化特性。 PIS研究小组将包括一支研究生的潜水员团队在这项工作中，将扩大参与，并帮助创建AI A-Aware Aways在化学和材料科学背景下的劳动力。研究重点将是使用AI优化蓝色照明材料的设计。为了实现这一目标，该项目将继续通过AI平台连接的两个平行实验轨道。在第一个轨道中，数据和计算模型将用于训练AI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的工作流程，从而实现强大的蓝色发光分子的设计。在第二轨中，重点将放在扩展可以集成到第一个轨道设计概念中的潜在化学空间。这将大大增加可以探索的半导体材料的多样性，并为如何制造具有所需特性的新的，意外的分子框架提供路线图。该项目将结合高维稀疏回归，图形输入的机器学习以及离散优化的概念和技术。最终的双模式平台（属性设计/合成设计）将提供前所未有的预测水平，从而使材料的设计和制造变得更有效，更自动化。同时，新型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方面出现。该项目还为本科和研究生材料和计算化学培训提供了新的机会。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，被认为是通过评估而被视为珍贵的。