Electronic Structure Models Using Coarse-Grained Representations

伊利诺伊大学厄巴纳-香槟分校的Nicholas Jackson教授获得了化学部化学理论、模型和计算方法(CTMC)计划颁发的奖项，该奖项旨在开发计算机模拟方法，以支持复杂分子和材料系统的发现和发展。目标研究领域包括设计用于低成本电池、太阳能电池和有机微电子的新材料。这项理论和计算研究将通过提高我们对如何设计更好地收集、储存和控制能量的系统的基本理解来推动科学的发展。虽然较小系统的建模在最近几年取得了显著的进展，但由于模拟方法缓慢且计算代价高昂，表征、理解和控制大量原子和分子的复杂行为受到阻碍。尼古拉斯·杰克逊和他的团队将开发新的计算方法，这些方法将极大地提高对这种具有重要技术意义的系统进行计算机模拟的速度，并降低成本。这项工作将培养精通化学和先进计算机模拟方法的新一代科学家，包括数据科学和机器学习。它还将为一门新课程奠定基础，该课程将向化学领域的科学家介绍机器学习和数据科学方法。尼古拉斯·杰克逊教授和他的团队将建立一种新的范式，利用作用于粗粒度分子表示的电子预测模型进行可伸缩的量子化学预测。他们将通过针对底层量子力学数据集的系统参数化来开发方法，类似于现有的分子动力学粗粒度建模方法阵列，这些方法针对底层的全原子力场进行参数化。具体地说，新方法将处理(1)电子预测模型，通过将全原子量子化学预测重新规范化为粗粒度表示，以及(2)识别电子活性集体变量的降维技术，以用于映射算符识别和模型哈密顿设计。这些新的发展预计将对凝聚态电子预测产生类似的影响，就像分子软件对分子科学中的单分子预测产生的影响一样，改变化学应用中凝聚态材料系统的设计。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。

Professor Nicholas Jackson of the University of Illinois, Urbana-Champaign, is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry for research to develop computer simulation methods to support the discovery and development of complex molecular and materials systems. Targeted research areas include the design of new materials for use in low-cost batteries, solar cells, and organic microelectronics. This theoretical and computational research will advance science by improving our fundamental understanding of how to design systems that better harvest, store, and control energy. While modeling of smaller systems has significantly progressed in recent years, characterizing, understanding, and controlling the complex behavior of large collections of atoms and molecules is hindered by slow and computationally costly simulation methods. Nicholas Jackson and his group will develop new computational methods that will dramatically increase the speed, and decrease the cost, of performing computer simulations of such technologically important systems. This work will train a new generation of scientists fluent in both chemistry and advanced computer simulation methods, including data science and machine learning. It will also set the foundation for a new curriculum that will introduce machine learning and data science methods to Chemistry domain scientists.Professor Nicholas Jackson and his group will establish a new paradigm for scalable quantum chemical predictions utilizing electronic prediction models that act on coarse-grained molecular representations. They will develop methods via systematic parameterization against underlying quantum mechanical datasets, in analogy with the existing array of coarse-grained modeling methods of molecular dynamics that are parameterized against underlying all-atom force-fields. Specifically, the new methods will deal with (1) electronic prediction models by renormalizing all-atom quantum chemistry predictions to coarse-grained representations and (2) dimensionality reduction techniques to identify electronically active collective variables for mapping operator identification and model Hamiltonian design. The new developments are expected to have a similar impact on condensed phase electronic predictions as molecular software has had on single molecule predictions in the molecular sciences, transforming the design of condensed phase materials systems across chemical applications.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.