Designing New Energy Materials with Machine Learning with Rafael Gomez-Bombarelli - #558
Analysis
This article from Practical AI discusses the use of machine learning in designing new energy materials. It features an interview with Rafael Gomez-Bombarelli, an assistant professor at MIT, focusing on his work in fusing machine learning and atomistic simulations. The conversation covers virtual screening and inverse design techniques, generative models for simulation, training data requirements, and the interplay between simulation and modeling. The article highlights the challenges and opportunities in this field, including hyperparameter optimization. The focus is on the application of AI in materials science, specifically for energy-related applications.
Key Takeaways
- •Machine learning is being used to accelerate the design of new energy materials.
- •Atomistic simulations are being combined with machine learning for material design.
- •The article discusses virtual screening, inverse design, and generative models in this context.
Reference
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