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