Machine learning molecular simulations with physical constraints

具有物理约束的机器学习分子模拟

• 批准号: 2355973
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2355973
• 关键词: Machine; learning; molecular; simulations; physical

Molecular simulations have become a powerful approach, highly complementary to experiments, to interrogate the structure and dynamics of macromolecular and colloidal systems. The resulting datasets are however typically highly dimensional, complex and noisy, making the extraction of useful information difficult. Artificial intelligence techniques such as deep learning are remarkable in their capacity of extracting and presenting useful information even in situations where humans would struggle. This project aims at the development of artificial intelligence-based methods for the analysis of molecular simulations and generation of models that are faithful to available experimental data, and consistent with physical laws. We will investigate the usage of generative models to enhance the sampling of molecular conformational space. These models will be exploited to discover new states and identify the specific transition path between two known states. Potential applications of the developed computational tools range from protein folding and colloidal self-assembly to formulation science.

分子模拟已经成为一种强有力的方法，高度补充实验，询问大分子和胶体系统的结构和动力学。然而，由此产生的数据集通常是高维的，复杂的和嘈杂的，使得提取有用的信息变得困难。深度学习等人工智能技术在提取和呈现有用信息的能力方面非常出色，即使在人类难以理解的情况下。该项目旨在开发基于人工智能的方法，用于分析分子模拟和生成忠实于现有实验数据并符合物理定律的模型。我们将研究生成模型的使用，以提高分子构象空间的采样。这些模型将被用来发现新的状态，并确定两个已知状态之间的特定过渡路径。所开发的计算工具的潜在应用范围从蛋白质折叠和胶体自组装到配方科学。