CAREER: CAS-Climate: Data-driven Coupled-Cluster for Biomimetic CO2 Capture

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。在化学系化学理论、模型和计算方法计划的支持下，田纳西大学诺克斯维尔分校的康斯坦丁诺斯·沃吉阿齐斯正在开发数据驱动的计算方法，用于仿生捕获二氧化碳。大气中的二氧化碳(CO2)超载会产生一个重要的温室气体(GHG)层，这是美国和全球气候变化的主要贡献者。气候变化对人类健康和安全、生活质量和经济增长构成越来越大的挑战。直接空气捕集器(DAC)是指直接从空气中捕获二氧化碳的技术。DAC试剂设计的一种方法依赖于能够产生良好的二氧化碳结合的化学成分。计算研究可以检测不同的化学环境，并提出新的亲二氧化碳基团。Vogiatzis博士和他的研究小组将开发新的混合量子化学/机器学习模型，以探索基于酶如何选择性捕获二氧化碳的新型DAC方法。Vogiatzis博士还将开发一门面向高年级本科生或研究生的新课程，旨在将数据科学与化学联系起来，并为本科生和研究生提供重要技能。这门课程的目的是接触到来自弱势群体的学生，并提供对化学的刺激观点，同时培训学生在化学中更广泛地使用数据科学。本项目的主要目标是开发利用数据科学最新进展的计算方法，以扩大准确量子化学方法的适用性。Vogiatzis博士的方法基于以低计算成本获得的分子波函数的循环，以帮助训练机器学习模型，该模型将提供快速可靠的复杂分子系统的能量和几何结构，而不会损失精度。微扰三重耦合团簇方法(CCSD(T))是一种平衡精度和效率的波函数方法。Vogiatzis博士和他的研究小组将开发可转移的机器学习模型，通过利用Hartree-Fock(HF)和二阶微扰理论等低成本方法的数据学习高精度的CCSD(T)波函数。这种数据驱动的耦合团簇(DDCC)方案是基于电子关联的，这是一种在所有分子物种中具有局部短程特性的性质，与它们的大小无关。DDCC模型可以有效地编码电子关联的局部性质，经过彻底的测试和基准测试，可以用于检测二氧化碳-寡肽系统以进行仿生二氧化碳捕获。此外，这里在将量子化学方法与机器学习相结合方面取得的进展预计将适用于许多其他化学挑战。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).WIth support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Konstantinos Vogiatzis of the University of Tennessee-Knoxville is developing data-driven computational methodologies for the biomimetic capture of carbon dioxide. Carbon dioxide (CO2) overload in the atmosphere generates a significant greenhouse gas (GHG) layer, a major contributor to climate change in the United States and around the globe. Climate change presents a growing challenge to human health and safety, quality of life, and economic growth. Direct air capture (DAC) refers to technologies that capture CO2 directly from the air. One approach to DAC agent design relies upon chemical compositions that lead to favorable CO2 binding. Computational studies can examine different chemical environments and suggest new CO2-philic groups. Dr. Vogiatzis and his research group will develop new hybrid quantum chemical/machine learning models for the exploration of novel DAC approaches that are based on how enzymes can selectively capture CO2. Dr. Vogiatzis will also develop a new course offered at the upper undergraduate or early graduate level that aims to bridge data science with chemistry and provide important skills to undergraduate and graduate students. This course aims to reach students from underserved groups and provide a stimulating view of chemistry while training students in more expansive use of data science in chemistry.The primary objective of this project is to develop computational methodologies that capitalize on recent progress in data science for expanding the applicability of accurate quantum chemistry methods. Dr. Vogiatzis’ approach is based on the recycling of molecular wave functions obtained at low computational cost to help train machine-learning models which will provide fast and reliable energies and geometries of complex molecular systems without loss of accuracy. Coupled-cluster singles-and-doubles with perturbative triples (CCSD(T)) is a wave function method that balances accuracy with efficiency. Dr. Vogiatzis and his research group will develop transferable machine learning models that learn highly accurate CCSD(T) wave functions by utilizing data from low-cost methods such as Hartree-Fock (HF) and second-order perturbation theory. This data-driven coupled-cluster (DDCC) scheme is based on electron correlation, a property that has a local, short-range character across all molecular species, independent of their size. DDCC models can effectively encode the local nature of electron correlation and, after thorough testing and benchmarking, can be used for the examination of CO2-oligopeptide systems for biomimetic CO2 capture. Furthermore, the advances made here in combining quantum chemical methods with machine-learning are expected to be applicable to a significant variety of other chemical challenges.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.