CDS&E: Two-electron Reduced Density Matrices in Quantum Chemistry and Physics

化学系的化学理论、模型和计算方法(CTMC)项目获得这一奖项，支持芝加哥大学的David A.Mazziotti教授开发处理多电子量子系统的新方法，以研究化学和材料中的问题。许多对改善社会至关重要的新分子和新材料是如此复杂，以至于很难用传统方法进行研究。Mazziotti和他的研究小组将寻求新的量子化学算法，这些算法利用电子相互作用的成对性质，结合量子计算、机器学习和优化方面的最新进展，以更高的效率预测分子和材料的能量和其他性质。这项工作具有潜在的变革性，因为它旨在使应用于更复杂的化学系统，为化学和材料的计算研究开辟新的前景。Mazziotti和他的团队还将推动教育和外联活动，包括化学量子计算和机器学习课程，基于视频会议的互动量子化学系列讲座，以及面向高中生的在线科学期刊。准确而高效地计算具有关联电子的分子和材料仍然是科学和工程中的突出问题之一。在量子计算、机器学习和最优化理论的最新发现的催化下，Mazziotti和他的研究小组旨在通过探索将这些最新进展与简化密度矩阵(RDM)理论相结合来提高对相关分子和材料的预测能力。他们将通过量子计算、机器学习和/或优化方面的新的新兴范例，为具有关联电子的一般量子系统开发RDM算法，超越最近基于近似泛函或N-可表示条件的经典算法。这些新的公式将使用量子计算、机器学习和/或优化来避免多电子波函数的显式经典构造和存储，因此，它们将为处理当前方法无法达到的强关联系统提供新的可能性。提出的工作是及时的：最近，量子计算机的量子比特数和保真度都有了显著的提高，机器学习已经成功地应用于图像和语音识别、自然语言处理、自动驾驶和化学等领域。Mazziotti和他的团队将应用现有的和建议的双电子RDM方法来预测和优化化学和物理中重要的强关联分子系统和过程。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。

With this award, the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry is supporting Professor David A. Mazziotti of the University of Chicago to develop new methods for treating many-electron quantum systems to study problems in chemistry and materials. Many new molecules and materials that are critically important to improving society are so complex that they are difficult to study with conventional methods. Mazziotti and his research group will pursue novel quantum chemistry algorithms that exploit the pairwise nature of the electronic interactions in combination with recent advances in quantum computing, machine learning, and optimization to predict the energies and other properties of molecules and materials with increased efficiency. The work has the potential to be transformative in that it aims to enable applications to more complex chemical systems, opening new vistas for computational research in chemistry and materials. Mazziotti and his group will also drive educational and outreach activities including a quantum computing and machine learning curricula for chemistry, an interactive videoconference-based lecture series on quantum chemistry, and an online science journal for high school students. Accurate and yet efficient computation of molecules and materials with correlated electrons remains one of the outstanding problems in science and engineering. Catalyzed by recent discoveries in quantum computation, machine learning, and optimization theory, Mazziotti and his research group aim to improve predictive capabilities for correlated molecules and materials by exploring the combination of these recent advances with reduced density matrix (RDM) theory. They will develop RDM algorithms for general quantum systems with correlated electrons via new, emergent paradigms in quantum computing, machine learning, and/or optimization, going beyond recent classical algorithms based on approximate functionals or N-representability conditions. These novel formulations will use quantum computing, machine learning, and/or optimization to avoid the explicit classical construction and storage of the many-electron wave function, and therefore, they will raise new possibilities for treating strongly correlated systems that are beyond the reach of current approaches. The proposed work is timely: recently, quantum computers have been developed with significant improvements in their qubit-numbers and fidelity, and machine learning has been applied successfully to applications in image and speech recognition, natural language processing, autonomous driving, and chemistry. Mazziotti and his group will apply existing and proposed two-electron RDM methods to predict and optimize strongly correlated molecular systems and processes of importance in chemistry and physics.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.