Search Results - "Kang, Pei-Lin" :: K.UTB vyhledávací portál

by Shi, Yun-Fei, Yang, Zheng-Xin, Ma, Sicong, Kang, Pei-Lin, Shang, Cheng, Hu, P., Liu, Zhi-Pan
Published in Engineering (Beijing, China) (01.08.2023)

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Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface

by Kang, Pei-Lin, Yang, Zheng-Xin, Shang, Cheng, Liu, Zhi-Pan
Published in Journal of chemical theory and computation (14.11.2023)

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Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity

by Kang, Pei-Lin, Shi, Yun-Fei, Shang, Cheng, Liu, Zhi-Pan
Published in Chemical science (Cambridge) (13.07.2022)

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The Role of Undercoordinated Sites on Zinc Electrodes for CO2 Reduction to CO

by Kang, Mavis Pei Lin, Kolb, Manuel J., Calle‐Vallejo, Federico, Yeo, Boon Siang
Published in Advanced functional materials (01.06.2022)

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Methanol Synthesis from CO2/CO Mixture on Cu–Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search

by Shi, Yun-Fei, Kang, Pei-Lin, Shang, Cheng, Liu, Zhi-Pan
Published in Journal of the American Chemical Society (27.07.2022)

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Active Site of Catalytic Ethene Epoxidation: Machine-Learning Global Pathway Sampling Rules Out the Metal Sites

by Chen, Dongxiao, Kang, Pei-Lin, Liu, Zhi-Pan
Published in ACS catalysis (02.07.2021)

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Methanol Synthesis from CO 2 /CO Mixture on Cu–Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search

by Shi, Yun-Fei, Kang, Pei-Lin, Shang, Cheng, Liu, Zhi-Pan
Published in Journal of the American Chemical Society (27.07.2022)

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Many-Body Function Corrected Neural Network with Atomic Attention (MBNN-att) for Molecular Property Prediction

by Yang, Zheng-Xin, Xie, Xin-Tian, Kang, Pei-Lin, Wang, Zhen-Xiong, Shang, Cheng, Liu, Zhi-Pan
Published in Journal of chemical theory and computation (13.08.2024)

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Atomic structure of boron resolved using machine learning and global samplingElectronic supplementary information (ESI) available: SSW-NN methodology, including the HDNN architecture, the iterative dataset generation, statistical assessment of different structural descriptors, general guidelines for setting up the structural descriptors and network size; DFT calculation details; pair distribution function of α-B; parameters for the structural descriptors used in set-1 and final NN potential; pho

by Huang, Si-Da, Shang, Cheng, Kang, Pei-Lin, Liu, Zhi-Pan
Year of Publication 28.11.2018

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Atomic structure of boron resolved using machine learning and global sampling† †Electronic supplementary information (ESI) available: SSW-NN methodology, including the HDNN architecture, the iterative dataset generation, statistical assessment of different structural descriptors, general guidelines for setting up the structural descriptors and network size; DFT calculation details; pair distribution function of α-B; parameters for the structural descriptors used in set-1 and final NN potential;

by Huang, Si-Da, Shang, Cheng, Kang, Pei-Lin, Liu, Zhi-Pan
Published in Chemical science (Cambridge) (11.09.2018)

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LASP to the Future of Atomic Simulation: Intelligence and Automation

by Xie, Xin-Tian, Yang, Zheng-Xin, Chen, Dongxiao, Shi, Yun-Fei, Kang, Pei-Lin, Ma, Sicong, Li, Ye-Fei, Shang, Cheng, Liu, Zhi-Pan
Published in Precision Chemistry (14.09.2024)

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