CDI-Type I: Using Machine Learning to Develop New Approaches to Semiempirical Quantum Chemistry

这项拟议的工作将量子化学与机器学习相结合，以开发预测化学体系电子结构的有效计算方法。在过去的几十年里，量子化学已经达到了一个点，可以常规地为小分子产生高精度的结果。然而，随着分子大小的增加，计算成本迅速增加，这使得对蛋白质或复杂纳米结构的计算具有挑战性。该项目利用分子相似性，即分子片段在不同环境中的行为相似，以显著降低计算成本。首先，生成在一系列环境中关于分子片段的电子结构的准确但计算昂贵的高级结果的数据库。然后，这些数据被用来开发一种机器学习算法，该算法使用关于分子片段及其环境的信息来预测片段的行为。机器学习的挑战是对新的片段和环境进行泛化，将这种泛化集成到更大的分子模拟中，最后表征性能以允许报告最终模拟结果的置信度。例如，如果学习算法的工作原理是将化学空间分解成可以用低成本近似函数很好地描述的区域，则该方法必须表征这些区域的边界并处理区域之间的转换。这一挑战将通过该项目的化学和机器学习部分的紧密结合来解决，这样关于逼近函数的形式和学习算法的设计决策将一起做出。快速和准确地产生分子系统的能量的能力将在生物和纳米技术等领域产生广泛的影响。目前对大分子系统的计算方法依赖于大大简化的能量模型，如分子力学的球状和棒状模型。虽然这样的模型对结构很有用，但功能预测往往需要化学键的断裂和形成，这需要更现实的电子结构方法。这里开发的方法旨在使大系统的现实函数预测在计算上可行。化学和机器学习的紧密结合也为研究生和本科生提供了极好的跨学科培训机会。这是一个网络驱动的发现和创新计划奖，由化学部和多学科活动办公室共同资助。

The proposed work melds quantum chemistry with machine learning to develop efficient computational methods for predicting the electronic structure of chemical systems. The past decades have brought quantum chemistry to a point where highly accurate results can be routinely generated for small molecules. However, the computational cost increases rapidly with molecular size, making calculations on proteins or complex nanostructures challenging. This project takes advantage of molecular similarity, whereby molecular fragments behave similarly in different environments, to substantially lower the computational cost. First, a database of accurate but computationally expensive high-level results on the electronic structure of a molecular fragment in a range of environments is generated. This data is then used to develop a machine learning algorithm that uses information about the molecular fragment and its environment to predict the behavior of the fragment. The challenge for machine learning is to generalize to new fragments and environments, to integrate this generalization into the larger molecular simulation, and finally to characterize the performance to allow reporting of the confidence in the eventual simulation results. For example, if the learning algorithm works by breaking chemical space into regions that can be well described with low-cost approximating functions, the approach must characterize the boundaries of these regions and handle the transitions between the regions. This challenge will be addressed by a close integration of the chemistry and machine learning portions of the project, such that design decisions regarding the form of the approximating function and learning algorithm are made together.The ability to quickly and accurately generate the energy of a molecular system would have broad impact in domains such as biology and nanotechnology. Current computational approaches to large molecular systems rely on greatly simplified models of the energy, such as the ball and stick models of molecular mechanics. While such models are useful for structure, functional predictions often require breaking and formation of chemical bonds, which requires more realistic electronic structure approaches. The approaches developed here are designed to make realistic functional predictions for large systems computationally feasible. The close integration of chemistry and machine learning also provides excellent interdisciplinary training opportunities for both graduate and undergraduate students.This is a Cyber-Enabled Discovery and Innovation Program award and is co-funded by the Division of Chemistry and the Office of Multidisciplinary Activities.