We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects. We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parameterization. Since Multi-layer perceptrons (i.e., feed-forwar...
In general, traditional methods based on experiments and molecular dynamics (MD) simulations are always expensive and time consuming. In this work, a machine learning (ML) framework to predict the methane adsorption behavior in shale nanopores is constructed from the microscopic and kinetic theory ...
In this study, a simulation framework has been developed to explore efficiencies of different machine learning methods (i.e. support vector machines, artificial neural networks and random forest) in streamflow simulation for four rivers in the United States. In this simulation framework, we analyzed...
A machine learning algorithm may be used to generate, based on the set of training parameters, a coarse scale approximation of a phase permeability of the coarse grid cell. The hydrocarbon reservoir can be simulated using the coarse scale approximation of the effective phase permeability generated ...
It breaks down the design geometries of manufactured objects into discrete, micro-level structures that have their own mathematical values. This can be applied to workflows or processes in automotive, consumer products, industrial, aerospace, marine, and much more. Manufacturing methods such as injecti...
Machine-learning potentials (MLPs) based on artificial neural networks29,30,31 or kernel-based methods32,33,34 have been suggested to be a powerful method to address the limited accuracy and transferability of classic force fields and maintain a DFT-level accuracy. This is well demonstrated in ...
Heterogeneous catalysis is at the heart of chemistry. New theoretical methods based on machine learning (ML) techniques that emerged in recent years provide a new avenue to disclose the structures and reaction in complex catalytic systems. Here we review
It breaks down the design geometries of manufactured objects into discrete, micro-level structures that have their own mathematical values. This can be applied to workflows or processes in automotive, consumer products, industrial, aerospace, marine, and much more. Manufacturing methods such as injecti...
Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field. 07 文章标题:用于电动汽车的新型优化U型轻质液冷电池热管理系统的设计:一种机器学习方法 期刊名称:...
constructed by using machine learning methods, including principal component analysis and kernel density estimation. This work also presents the result of the simulation of controlling a renewable energy-powered semi-closed greenhouse growing tomatoes located in Ithaca, New York. Compared to other model ...