Deep learning and self-consistent field theory: A path towards accelerating polymer phase discovery
Deep learning and self-consistent field theory: A path towards accelerating polymer phase discovery
复制标题
DOI：
10.1016/j.jcp.2021.110519
复制
发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Yao Xuan;K. Delaney;Héctor D. Ceniceros;G. Fredrickson
中科院分区：
文献类型：
--
作者：
Yao Xuan;K. Delaney;Héctor D. Ceniceros;G. Fredrickson
提出了一种新的框架，该框架利用从具有深度学习的自洽场理论（SCFT）模拟获得的数据来加速对嵌段共聚物的参数空间的探索。深度神经网络在Sobolev空间中进行调整和训练，以更好地捕捉SCFT近似的鞍点性质。所提出的方法包括两个主要问题：1）作为平均单体密度场和相关物理参数的函数的有效哈密顿量的近似的学习和2）给定聚合物参数的鞍密度场的预测。还有一个额外的挑战：有效哈密顿量必须在移位（以及2D和3D中的旋转）下保持不变。引入数据增强方法和适当的正则化以有效地实现所述不变性。在这第一项研究中，重点是一维（在物理空间）系统，以允许一个彻底的探索和发展所提出的方法。
A new framework that leverages data obtained from self-consistent field theory (SCFT) simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. Deep neural networks are adapted and trained in Sobolev space to better capture the saddle point nature of the SCFT approximation. The proposed approach consists of two main problems: 1) the learning of an approximation to the effective Hamiltonian as a function of the average monomer density fields and the relevant physical parameters and 2) the prediction of saddle density fields given the polymer parameters. There is an additional challenge: the effective Hamiltonian has to be invariant under shifts (and rotations in 2D and 3D). A data-enhancing approach and an appropriate regularization are introduced to effectively achieve said invariance. In this first study, the focus is on one-dimensional (in physical space) systems to allow for a thorough exploration and development of the proposed methodology.