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

合作研究：D3SC：CDS

• 批准号: 2055669
• 负责人: Kim Lewis
• 金额: $ 19.71万
• 依托单位: Howard 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: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2055669&HistoricalAwards=false
• 关键词: Collaborative; Research; D3SC; CDS&E; Predictive

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.

通过化学理论，化学理论，模型和计算方法计划的奖项，支持使用人工智能来预测适合分子材料的卟啉分子的化学理论，安德烈·克莱伯恩（Andre Clayborne）（乔治·梅森大学）和金·刘易斯（Kim Lewis）（霍华德大学）。 卟啉分子具有用于分子材料和量子信息技术的电气和光学特性。但是，传统模拟的耗时实验和挑战将研究人员和行业限制在基于卟啉的材料的广泛使用中。协作团队将结合尖端电导实验和理论计算技术，以及机器学习和认知计算。多学科研究项目还纳入了具有工业合作伙伴表演的研究生的夏季工业沉浸体验，以为学术界以外的职业做准备。 该协作研究项目旨在加速使用基于数据以图基于图的神经网络和认知计算的基于数据输入的卟啉分子，它们的响应特性（即电导，UV-VIS光谱等）和分子材料的发现。卟啉是量子信息技术和光电设备中基于分子的设备中有希望的组件。安德烈·克莱伯恩（Andre Clayborne）和金·刘易斯（Kim Lewis）将通过人工智能技术从量子力学计算，分子动力学模拟，扫描隧道显微镜分子断裂连接以及导电原子力显微镜中整合数据。 高度整合的工作将构建一个具有实验和理论值的卟啉和金属孢子蛋白的综合数据库，包括响应函数，例如电导曲线，并将开发一种认知计算方案，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Web的应用程序编程界面，以与工业合作伙伴（Performigence）一起使用Porphyrin Project数据库。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的审查标准通过评估来通过评估来支持的。