| 1 | A density-functional-theory-based and machine-learning-accelerated hybrid method for intricate system catalysis显示文摘Being progressively applied in the design of highly active catalysts for energy devices,machine learning(ML)technology has shown attractive ability of dramatically reducing the computational cost of the traditional density functional theory(DFT)method,showing a particular advantage for the simulation of intricate system catalysis.Starting with a basic description of the whole workflow of the novel DFT-based and ML-accelerated(DFT-ML)scheme,and the common algorithms useable for machine learning,we presented in this paper our work on the development and performance test of a DFT-based ML method for catalysis program(DMCP)to implement the DFT-ML scheme.DMCP is an efficient and user-friendly program with the flexibility to accommodate the needs of performing ML calculations based on the data generated by DFT calculations or from materials database.We also employed an example of transition metal phthalocyanine double-atom catalysts as electrocatalysts for carbon reduction reaction to exhibit the general workflow of the DFT-ML hybrid scheme and our DMCP program. | Xuhao Wan Zhaofu Zhang Wei Yu Yuzheng Guo | 2021 | Materials Reports(Energy)2021，1，3： | 2 |
| 2 | Machine-learning-based interatomic potentials for advanced manufacturing显示文摘This paper summarizes the progress of machine-learning-based interatomic potentials and their applications in advanced manufacturing.Interatomic potential is essential for classical molecular dynamics.The advancements made in machine learning(ML)have enabled the development of fast interatomic potential with ab initio accuracy.The accelerated atomic simulation can greatly transform the design principle of manufacturing technology.The most widely used supervised and unsupervised ML methods are summarized and compared.Then,the emerging interatomic models based on ML are discussed:Gaussian approximation potential,spectral neighbor analysis potential,deep potential molecular dynamics,SCHNET,hierarchically interacting particle neural network,and fast learning of atomistic rare events. | Wei Yu Chaoyue Ji Xuhao Wan Zhaofu Zhang John Robertson Sheng Liu Yuzheng Guo | 2021 | International Journal of Mechanical System Dynamics2021，1，2： | 0 |
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