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Found 4 papers, 3 papers with code
Using GNN property predictors as molecule generators
In this article, we exploit the invertible nature of these neural networks to directly generate molecular structures with desired electronic properties.
Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning
The results demonstrate that our approach achieves high prediction accuracy, facilitates interpretable feature extraction, and enables multicriteria design optimization, leading to significant reduction of computing power and time (10x reduction of required DFT calculations) in high-performance catalyst discovery.
Machine Learning for a Sustainable Energy Future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient harvesting, storage, conversion, and management of renewable energy.
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials.
