Self-Parametrizing System-Focused Atomistic Models
Self-Parametrizing System-Focused Atomistic Models
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DOI：
10.1021/acs.jctc.9b00855
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发表时间：
2020-03-01
影响因子：
5.5
通讯作者：
Reiher, Markus
中科院分区：
文献类型：
--
作者：
Brunken, Christoph;Reiher, Markus
在复杂的环境中，如蛋白质，纳米结构，或在表面上的化学反应的计算研究需要准确和有效的原子模型适用于纳米尺度。一般来说，原子实体的精确参数化将不适用于任意系统类，但需要快速、自动化、以系统为中心的参数化过程，以快速适用、可靠、灵活和可再现。在这里，我们开发和联合收割机自动参数化量子化学衍生分子力学模型与机器学习的自主不确定性量化和细化下的校正。我们的方法首先生成一个准确的，物理动机的模型，从最小能量结构和相应的海森矩阵的部分海森拟合过程的力常数。然后，该模型是生成大量配置的起点，对于这些配置，可以在运行中评估额外的偏离最小参考数据。在这些数据上训练Delta机器学习模型，以提供对能量和力的校正，包括不确定性估计。在此过程中，机器学习模型的灵活性根据可用训练数据的量进行定制。大系统的参数化是通过一种分段方法实现的。由于它们的模块化性质，所有模型构建步骤都允许以滚动方式改进模型。我们的方法也可以用于生成系统集中的静电分子力学嵌入环境中的量子力学/分子力学的混合模型在纳米尺度上的任意原子结构。
Computational studies of chemical reactions in complex environments such as proteins, nanostructures, or on surfaces require accurate and efficient atomistic models applicable to the nanometer scale. In general, an accurate parametrization of the atomistic entities will not be available for arbitrary system classes but demands a fast, automated, system-focused parametrization procedure to be quickly applicable, reliable, flexible, and reproducible. Here, we develop and combine an automatically parametrizable quantum chemically derived molecular mechanics model with machine-learned corrections under autonomous uncertainty quantification and refinement. Our approach first generates an accurate, physically motivated model from a minimum energy structure and its corresponding Hessian matrix by a partial Hessian fitting procedure of the force constants. This model is then the starting point to generate a large number of configurations for which additional off minimum reference data can be evaluated on the fly. A Delta-machine learning model is trained on these data to provide a correction to energies and forces including uncertainty estimates. During the procedure, the flexibility of the machine learning model is tailored to the amount of available training data. The parametrization of large systems is enabled by a fragmentation approach. Due to their modular nature, all model construction steps allow for model improvement in a rolling fashion. Our approach may also be employed for the generation of system-focused electrostatic molecular mechanics embedding environments in a quantum-mechanical/molecular-mechanical hybrid model for arbitrary atomistic structures at the nanoscale.