用于控制和学习的可微分子模拟
Differentiable Molecular Simulations for Control and Learning
摘要：
分子动力学模拟在原子尺度上使用统计力学，以使基本机制的阐明和所需任务的物质工程成为可能。分子系统在微观尺度上的行为通常用由哈密顿量或能量函数参数化的微分方程来模拟。哈密顿量描述了系统的状态及其与环境的相互作用。为了得到预测微观模型，人们希望推断出与所观察到的宏观量一致的分子哈密顿量。从工程的角度来看，人们希望控制哈密顿算子以实现期望的模拟结果和结构，如在自组装和光学控制中，然后在实验室中实现具有期望的哈密顿算子的系统。在这两种情况下，目标都是修改哈密顿量，使模拟系统的涌现属性与给定的目标相匹配。我们演示了如何使用可微模拟来实现这一点，其中批量目标观测值和模拟结果可以相对于哈密顿算子进行分析区分，为参数化哈密顿算子推断宏观模型和开发控制协议开辟了新的途径。
英文摘要：
Molecular dynamics simulations use statistical mechanics at the atomistic scale to enable both the elucidation of fundamental mechanisms and the engineering of matter for desired tasks. The behavior of molecular systems at the microscale is typically simulated with differential equations parameterized by a Hamiltonian, or energy function. The Hamiltonian describes the state of the system and its interactions with the environment. In order to derive predictive microscopic models, one wishes to infer a molecular Hamiltonian that agrees with observed macroscopic quantities. From the perspective of engineering, one wishes to control the Hamiltonian to achieve desired simulation outcomes and structures, as in self-assembly and optical control, to then realize systems with the desired Hamiltonian in the lab. In both cases, the goal is to modify the Hamiltonian such that emergent properties of the simulated system match a given target. We demonstrate how this can be achieved using differentiable simulations where bulk target observables and simulation outcomes can be analytically differentiated with respect to Hamiltonians, opening up new routes for parameterizing Hamiltonians to infer macroscopic models and develop control protocols.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
