.png){kind=icon}
返回
Training machine learning potentials for reactive systems: A Colab tutorial on basic models
.png){kind=icon}
PRE
AI
DOI：10.1002/jcc.27269.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Muller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction. A self-guided Colab tutorial about machine learning potential for reactive systems are presented in this work. The tutorial begins with the introduction of feedforward neural network and kernel-based models by fitting the two-dimensional Muller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions and embedding neural networks. Lastly, these features will be used to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.image
Keyword:
Gaussian process regression
machine learning potential
neural network
tutorial
期刊
IF:
4.8
论文数:
7.1K
被引数:
6.1W
机构
引用论文
Job tenure and quality of work life of people with psychiatric disabilities working in social enterprises在社会企业工作的精神病患者的工作任期和工作生活质量
PREAI
分享收藏分享收藏E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentialsE(3)-等变图神经网络，用于数据高效和精确的原子间势.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Machine learning enables long time scale molecular photodynamics simulationsChemical ScienceOAAI
Chemical ScienceIF7.4
OAAI分享收藏A simplified charge projection scheme for long-range electrostatics in ab initio QM/MM calculationsOAAI
OAAI分享收藏
查看更多内容