ME 697R Lecture 8.2: Machine Learning Techniques - Machine Learning Based Interatomic Potentials (MLIPs)

By Xiulin Ruan

Mechanical Engineering, Purdue University, West Lafayette, IN

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Researchers should cite this work as follows:

  • Xiulin Ruan (2020), "ME 697R Lecture 8.2: Machine Learning Techniques - Machine Learning Based Interatomic Potentials (MLIPs)," https://nanohub.org/resources/33068.

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2004 Mechanical Engineering, Purdue University, West Lafayette, IN

ME 697R Lecture 8.2: Machine Learning Techniques - Machine Learning Based Interatomic Potentials (MLIPs)
  • Lecture 8.2: Machine learning based interatomic potentials (MLIPs) 1. Lecture 8.2: Machine learning … 0
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  • Development of Interatomic Potentials 2. Development of Interatomic Pot… 148.04804804804806
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  • Parameter Fitting with Machine Learning 3. Parameter Fitting with Machine… 315.68234901568235
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  • Machine Learning Potentials 4. Machine Learning Potentials 413.94728061394727
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  • Representation and Regression 5. Representation and Regression 645.2118785452119
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  • Advantages of MLP 6. Advantages of MLP 1086.1194527861194
    00:00/00:00
  • Materials Structures 7. Materials Structures 1342.4090757424092
    00:00/00:00
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