Data-Driven Materials Innovation: Where Machine Learning Meets Physics

Data-Driven Materials Innovation: Where Machine Learning Meets Physics

nanohubtechtalks via YouTube Direct link

Summary

43 of 44

43 of 44

Summary

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Data-Driven Materials Innovation: Where Machine Learning Meets Physics

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  1. 1 Data-driven materials innovation: where machine learning meets physics
  2. 2 Machine Learning for Materials Design/Discovery at Schrödinger
  3. 3 Supervised Learning in Materials Science
  4. 4 Featurization in Diverse Materials Systems
  5. 5 Automated Machine Learning and Visualization in Molecular Systems
  6. 6 AutoQSAR for Ionic Liquids
  7. 7 DeepAutoQSAR: Automated Model Selection & Parameter Optimization
  8. 8 Case Study - Redox Flow Batteries
  9. 9 AutoQSAR vs DeepAutoQSAR Results
  10. 10 Chemical Featurization using Physics
  11. 11 Customized Polymer Descriptors Outperform Simple Monomers
  12. 12 Viscosity Dataset for Machine Learning Module
  13. 13 Quantitave Structure-Property Relationships QSPR
  14. 14 Impact of MD-Derived Simulation Descriptors
  15. 15 Impact of MD-Derived Simulation Descriptors
  16. 16 Machine Learning Optoelectronics Properties with DFT descriptors
  17. 17 Database of Optical Properties of Organic Compounds
  18. 18 Benchmark of DFT Descriptors
  19. 19 Feature Importance Analysis
  20. 20 Machine Learning for Volatility of Organic Molecules
  21. 21 Evaporation/Sublimation of Organic Molecules
  22. 22 Benchmarking ML Algorithms
  23. 23 Prediction of Pressure-Temperature Relationships
  24. 24 Applications of Volatility Machine Learning
  25. 25 Machine Learning for Inorganic 3D Crystal Structures
  26. 26 Transparent Conducting Oxide Band Gap ML
  27. 27 User Interface
  28. 28 DeepAutoQSAR Results
  29. 29 Machine Learning Property Prediction Panel
  30. 30 ML for Formulations
  31. 31 Active Learning and Genetic Optimization
  32. 32 Active Learning OptoElectronics Multi-Parameter Optimization MPO
  33. 33 Active Learning Workflow for OptoElectronics
  34. 34 Optoelectronic Genetic Optimization
  35. 35 Machine Learning Forcefields
  36. 36 Neural Network Potentials NNPs
  37. 37 Our First NN Model: Schrödinger-ANI SANI
  38. 38 QRNN: Charge-Recursive Neural Network
  39. 39 Bulk Properties of Liquid Electrolytes
  40. 40 Enterprise Informatics
  41. 41 Schrodinger's Informatics Platform - LiveDesign®
  42. 42 Suitable for Diverse Materials and Data Types
  43. 43 Summary
  44. 44 Thank you

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