Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime
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DOI: 10.1002/aenm.202200553
Abstract: Machine learning (ML)‐based approaches to battery design are relatively new but demonstrate significant promise for accelerating the timeline for new materials discovery, process optimization, and cell lifetime prediction. Battery modeling represents an interesting and unconventional… read more here.
Keywords: small data; battery; data regime; machine learning ... See more keywords
Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c00997
Abstract: Motivated by the challenging of deep learning on the low data regime and the urgent demand for intelligent design on highly energetic materials, we explore a correlated deep learning framework, which consists of three recurrent… read more here.
Keywords: detonation velocity; low data; framework; data regime ... See more keywords