Collaborative Research: On-The-Fly free Energy Parameterization in Molecular Aimulations

技术总结该奖项支持计算和理论研究及教育，旨在开发一种从分子动力学模拟中提取自由能的新方法。该方法基于温度-加速度，在运行的分子动力学模拟中，对所需的一组集体变量中的自由能梯度进行采样，同时驱动这些集体变量越过自由能垒，否则将使传统的分子动力学无效。这些梯度被用来作为优化过程的输入，以最小化与分析自由能梯度相关的误差。研究的主要目的是：(1)将该方法的性能与计算粗粒度势的其他方法进行比较，例如迭代Boltzmann反转和力匹配；(2)将该方法发展为一种工具，用于推导多尺度聚合物分子模拟的平均力势；以及(3)将该方法发展为大规模蛋白质构象采样的统计评估的手段。该方法以一种通用的方式制定，因此有望广泛地适用于几乎任何期望的集体变量。这个项目将致力于开发这种方法，特别是在涉及为多尺度模拟推导有效的粗粒度势和研究大生物分子的构象统计的应用中。在这个项目中开发的所有模拟代码将免费提供给社区。该奖项还支持对未被充分代表的学生进行最先进的计算和数值方法的培训和指导。非技术性总结该奖项支持计算和理论研究和教育，旨在开发一种从计算机模拟中提取信息的新方法。无论是在设计新药或新材料，还是简单地试图了解自然是如何工作的，计算机模拟对于理解物质在分子水平上的行为都很重要。将分子水平的理解带入日常感知领域需要统计方法，这些方法并不总是直截了当的，而且通常计算起来相当昂贵。该奖项支持开发一种新的统计方法，用于从分子水平的模拟中提取信息。这种方法可能比现有的方法更有效。这项研究将开发和应用这种方法来测试与生物和材料应用相关的系统。该项目可以显著降低从分子水平的模拟中获得高质量信息的成本，这些模拟是在化学、物理和生物学的交叉领域进行的。在这个项目中开发的所有模拟代码将免费提供给社区。该奖项还支持对未被充分代表的学生进行最先进的计算和数值方法的培训和指导。

TECHNICAL SUMMARYThis award supports computational and theoretical research and education with an aim to develop a new method for extracting free energies from molecular dynamics simulations. The method is based on temperature-acceleration, wherein free energy gradients in some desired set of collective variables are sampled in a running molecular dynamics simulation while simultaneously driving those collective variables over free energy barriers that would otherwise render traditional molecular dynamics ineffective. These gradients are used as inputs into an optimization procedure that minimizes the error associated with gradients of an analytical free energy.The major thrusts of the research are: (1) benchmark the performance of the method against alternative approaches for computing coarse grained potentials, such as iterative Boltzmann inversion and force-matching, (2) developing the method as a tool for deriving potentials of mean force for multiscale polymer molecular simulations, and (3) developing the method as a means of statistical evaluation of large-scale protein conformational sampling.The method is formulated in a general way and is therefore expected to be applicable broadly to almost any desired collective variables. This project will be devoted to developing this method particularly for applications involving deriving effective coarse-grained potentials for multiscale simulations and to the study of conformational statistics of large biomolecules. All simulation codes developed in this project will be made freely available to the community.This award also supports training and mentoring of underrepresented students in state of the art computational and numerical methods.NON-TECHNICAL SUMMARYThis award supports computational and theoretical research and education with an aim to develop a new method of extracting information from computer simulations. Whether in designing new drugs or new materials, or simply trying to understand how nature works, computer simulations are important in understanding how matter behaves at the molecular level. Bringing molecular-level understanding into the realm of everyday perception requires statistical approaches that are not always straightforward and often computationally quite expensive. This award supports developing a new statistical method for extracting information from molecular-level simulations. The method is potentially much more efficient than existing approaches. The research will develop and apply this method to test systems relevant for biological and materials applications. The project could significantly reduce the cost associated with obtaining high-quality information from simulations at the molecular level that at the intersections of chemistry, physics, and biology. All simulation codes developed in this project will be made freely available to the community.This award also supports training and mentoring of underrepresented students in state of the art computational and numerical methods.