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Machine learning guided discovery of superconducting calcium borocarbides

Autor: Zhang, Chao, Tang, Hui, Pan, Chen, Jiang, Hong, Sun, Huai-Jun, Ho, Kai-Ming, Wang, Cai-Zhuang

Pursuit of superconductivity in light-element systems at ambient pressure is of great experimental and theoretical interest. In this work, we combine a machine learning (ML) method with first-principles calculations to efficiently search for the ener

Externí odkaz: http://arxiv.org/abs/2303.09342

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Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation

Autor: Xiang, Yan, Tang, Yu-Hang, Gong, Zheng, Liu, Hongyi, Wu, Liang, Lin, Guang, Sun, Huai

We introduce an explorative active learning (AL) algorithm based on Gaussian process regression and marginalized graph kernel (GPR-MGK) to explore chemical space with minimum cost. Using high-throughput molecular dynamics simulation to generate data

Externí odkaz: http://arxiv.org/abs/2209.00514

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