Collaborative Research: D3SC: CDS&E: Predictive Discovery of Porphyrin Molecules and their Response Properties using Smart Objects-Enabled Machine Learning

凭借化学系化学理论、模型和计算方法项目的这一奖项，安德烈·克莱伯恩（乔治梅森大学）和金·刘易斯（霍华德大学）得到了支持，他们使用人工智能来预测适用于分子材料的卟啉分子。 卟啉分子具有电学和光学性质，可用于分子材料和量子信息技术。然而，耗时的实验和传统模拟的挑战限制了研究人员和工业对卟啉基材料的广泛使用。该合作团队将联合收割机结合尖端的电导实验和理论计算技术，沿着机器学习和认知计算。该多学科研究项目还为研究生提供了暑期工业沉浸式体验，并与工业合作伙伴Performigence合作，为学术界以外的职业生涯做准备。 该合作研究项目旨在加速卟啉分子的发现，其响应特性（即，电导、紫外-可见光谱等），以及使用以数据为中心的图形神经网络和认知计算的分子材料。卟啉化合物是量子信息技术和光电子器件中的分子基器件的重要组成部分。安德烈·克莱伯恩和金·刘易斯将把量子力学计算、分子动力学模拟、扫描隧道显微镜分子断裂结和导电原子力显微镜的数据与人工智能技术相结合。 高度综合的工作将构建一个全面的数据库，卟啉和金属卟啉的实验和理论值，包括响应函数，如电导曲线，并将开发一个认知计算协议，用于预测卟啉分子及其响应特性。此外，研究团队将开发一个基于网络的应用程序编程接口，与工业合作伙伴Performigence一起使用卟啉项目数据库。该奖项反映了NSF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。

With this award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Andre Clayborne (George Mason University) and Kim Lewis (Howard University) are supported to use artificial intelligence to predict porphyrin molecules suitable for molecular materials. Porphyrin molecules have electrical and optical properties that are tailorable for molecular materials and quantum information technologies. However, time-consuming experiments and challenges with traditional simulations limit researchers and industries from wide-spread use of porphyrin-based materials. The collaborative team will combine cutting-edge conductance experiments and theoretical calculation techniques along with machine learning and cognitive computing. The multidisciplinary research project also incorporates summer industrial immersion experiences for graduate students with Performigence, an industrial partner, to prepare for careers beyond academia. This collaborative research project aims to accelerate the discovery of porphyrin molecules, their response properties (i.e., conductance, UV-vis spectra, etc.), and molecular materials using data-centered graph-based neural networks and cognitive computing. Porphyrins are promising components in molecular-based devices in quantum information technologies and opto-electronic devices. Andre Clayborne and Kim Lewis will integrate data from quantum-mechanical computations, molecular dynamics simulations, scanning tunneling microscope molecular break junctions, and conductive atomic force microscopy with artificial intelligence techniques. The highly integrative work will construct a comprehensive database for porphyrins and metal-porphyrins with experimental and theoretical values including response functions, such as conductance curves, and will develop a cognitive computing protocol for predicting porphyrin molecules and their response properties. In addition, the research team will develop a web-based application programming interface to use the Porphyrin Project database with an industrial partner, Performigence.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.