Choosing the right molecular machine learning potential von Pinheiro, Max, Ge, Fuchun, Ferré, Nicolas, Dral, Pavlo O, Barbatti, Mario

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Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights von Dral, Pavlo O.

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Interpretable Machine Learning of Two‐Photon Absorption von Su, Yuming, Dai, Yiheng, Zeng, Yifan, Wei, Caiyun, Chen, Yangtao, Ge, Fuchun, Zheng, Peikun, Zhou, Da, Dral, Pavlo O., Wang, Cheng

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Single-cell fucosylation breakdown: Switching fucose to europium von Liu, Zhen, Liang, Yong, Zhou, Yang, Ge, Fuchun, Yan, Xiaowen, Yang, Limin, Wang, Qiuquan

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Identification and antioxidant activity of bovine bone collagen-derived novel peptides prepared by recombinant collagenase from Bacillus cereus von Song, Yihang, Fu, Yousi, Huang, Shiyang, Liao, Langxing, Wu, Qian, Wang, Yali, Ge, Fuchun, Fang, Baishan

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Inhibitory Covalent Labeling and Clickable-Eu-Tagging-Based ICPMS: Measurement of pH-Dependent Absolute Activities of the Cathepsins in Hepatocyte Lysosomes von Ji, Caixia, Liang, Yong, Ge, Fuchun, Yang, Limin, Wang, Qiuquan

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Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations von Hou, Yi-Fan, Zhang, Lina, Zhang, Quanhao, Ge, Fuchun, Dral, Pavlo O.

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Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces von Hou, Yi-Fan, Ge, Fuchun, Dral, Pavlo O.

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Energy-conserving molecular dynamics is not energy conserving von Zhang, Lina, Hou, Yi-Fan, Ge, Fuchun, Dral, Pavlo O

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Fluorescent and mass spectrometric evaluation of the phagocytic internalization of a CD47-peptide modified drug-nanocarrier von Liu, Chunlan, Yu, Danxia, Ge, Fuchun, Yang, Limin, Wang, Qiuquan

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Viruslike Element-Tagged Nanoparticle Inductively Coupled Plasma Mass Spectrometry Signal Multiplier: Membrane Biomarker Mediated Cell Counting von Yuan, Rong, Ge, Fuchun, Liang, Yong, Zhou, Yang, Yang, Limin, Wang, Qiuquan

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Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models von Ge, Fuchun, Zhang, Lina, Hou, Yi-Fan, Chen, Yuxinxin, Ullah, Arif, Dral, Pavlo O.

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MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods von Zhang, Lina, Pios, Sebastian V., Martyka, Mikołaj, Ge, Fuchun, Hou, Yi-Fan, Chen, Yuxinxin, Chen, Lipeng, Jankowska, Joanna, Barbatti, Mario, Dral, Pavlo O.

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MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows von Dral, Pavlo O., Ge, Fuchun, Hou, Yi-Fan, Zheng, Peikun, Chen, Yuxinxin, Barbatti, Mario, Isayev, Olexandr, Wang, Cheng, Xue, Bao-Xin, Pinheiro Jr, Max, Su, Yuming, Dai, Yiheng, Chen, Yangtao, Zhang, Lina, Zhang, Shuang, Ullah, Arif, Zhang, Quanhao, Ou, Yanchi

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Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine von Ge, Fuchun, Wang, Ran, Qu, Chen, Zheng, Peikun, Nandi, Apurba, Conte, Riccardo, Houston, Paul L., Bowman, Joel M., Dral, Pavlo O.

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MLatom 2: An Integrative Platform for Atomistic Machine Learning von Dral, Pavlo O., Ge, Fuchun, Xue, Bao-Xin, Hou, Yi-Fan, Pinheiro, Max, Huang, Jianxing, Barbatti, Mario

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Physics-informed active learning for accelerating quantum chemical simulations von Yi-Fan, Hou, Zhang, Lina, Zhang, Quanhao, Ge, Fuchun, Dral, Pavlo O

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Energy-conserving molecular dynamics is not energy conserving von Zhang, Lina, Yi-Fan, Hou, Ge, Fuchun, Dral, Pavlo O

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Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models von Ge, Fuchun, Zhang, Lina, Yi-Fan, Hou, Chen, Yuxinxin, Ullah, Arif, Dral, Pavlo O

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MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods von Zhang, Lina, Pios, Sebastian V, Martyka, Mikołaj, Ge, Fuchun, Hou, Yi-Fan, Chen, Yuxinxin, Chen, Lipeng, Jankowska, Joanna, Barbatti, Mario, Dral, Pavlo O

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