Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Pei-Lin Kang

Showing results (1-10 of 9) with videos related to

Pageof 1
Sort By:
Iscience|January 25, 2021
Reaction prediction via atomistic simulation: from quantum mechanics to machine learningPei-Lin Kang, Zhi-Pan Liu
Accounts of Chemical Research|September 17, 2020
Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface ExplorationPei-Lin Kang, Cheng Shang, Zhi-Pan Liu
Journal of the American Chemical Society|December 5, 2019
Glucose to 5-Hydroxymethylfurfural: Origin of Site-Selectivity Resolved by Machine Learning Based Reaction SamplingPei-Lin Kang, Cheng Shang, Zhi-Pan Liu
Chemical Science|August 3, 2022
Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivityPei-Lin Kang, Yun-Fei Shi, Cheng Shang, et al.
Journal of the American Chemical Society|July 18, 2022
Methanol Synthesis from CO<sub>2</sub>/CO Mixture on Cu-Zn Catalysts from Microkinetics-Guided Machine Learning Pathway SearchYun-Fei Shi, Pei-Lin Kang, Cheng Shang, et al.
Chemical Science|January 11, 2019
Atomic structure of boron resolved using machine learning and global samplingSi-Da Huang, Cheng Shang, Pei-Lin Kang, et al.
Journal of Chemical Theory and Computation|October 19, 2023
Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy SurfacePei-Lin Kang, Zheng-Xin Yang, Cheng Shang, et al.
Journal of Chemical Theory and Computation|July 22, 2024
Many-Body Function Corrected Neural Network with Atomic Attention (MBNN-att) for Molecular Property PredictionZheng-Xin Yang, Xin-Tian Xie, Pei-Lin Kang, et al.
Precision Chemistry|December 30, 2024
LASP to the Future of Atomic Simulation: Intelligence and AutomationXin-Tian Xie, Zheng-Xin Yang, Dongxiao Chen, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Iscience|January 25, 2021
Reaction prediction via atomistic simulation: from quantum mechanics to machine learningPei-Lin Kang, Zhi-Pan Liu
Accounts of Chemical Research|September 17, 2020
Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface ExplorationPei-Lin Kang, Cheng Shang, Zhi-Pan Liu
Journal of the American Chemical Society|December 5, 2019
Glucose to 5-Hydroxymethylfurfural: Origin of Site-Selectivity Resolved by Machine Learning Based Reaction SamplingPei-Lin Kang, Cheng Shang, Zhi-Pan Liu
Chemical Science|August 3, 2022
Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivityPei-Lin Kang, Yun-Fei Shi, Cheng Shang, et al.
Journal of the American Chemical Society|July 18, 2022
Methanol Synthesis from CO<sub>2</sub>/CO Mixture on Cu-Zn Catalysts from Microkinetics-Guided Machine Learning Pathway SearchYun-Fei Shi, Pei-Lin Kang, Cheng Shang, et al.
Chemical Science|January 11, 2019
Atomic structure of boron resolved using machine learning and global samplingSi-Da Huang, Cheng Shang, Pei-Lin Kang, et al.
Journal of Chemical Theory and Computation|October 19, 2023
Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy SurfacePei-Lin Kang, Zheng-Xin Yang, Cheng Shang, et al.
Journal of Chemical Theory and Computation|July 22, 2024
Many-Body Function Corrected Neural Network with Atomic Attention (MBNN-att) for Molecular Property PredictionZheng-Xin Yang, Xin-Tian Xie, Pei-Lin Kang, et al.
Precision Chemistry|December 30, 2024
LASP to the Future of Atomic Simulation: Intelligence and AutomationXin-Tian Xie, Zheng-Xin Yang, Dongxiao Chen, et al.
Pageof 1