AI for Electrochemistry

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Published August 14, 2024 | Version v1
Dataset Open

Towards a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts

Creators

  • 1. State Key Laboratory of Physical Chemistry of Solid Surface, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
  • 2. DP Technology, Beijing 100080, China
  • 3. Department of Chemistry, University of California, Davis, CA 95616, USA
  • 4. Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361005, China
  • 5. Laboratory of AI for Electrochemistry (AI4EC), Tan Kah Kee Innovation Laboratory (IKKEM), Xiamen 361005, China
  • 6. AI for Science Institute, Beijing 100080, China
  • 7. Center for Machine Learning Research, Peking University, Beijing 100871, China
  • 8. School of Mathematical Sciences, Peking University, Beijing 100871, China
  • 9. Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China

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