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Model-Based Reinforcement Learning Control of Reaction-Diffusion Problems
Mathematical and computational tools have proven to be reliable in decision-making processes.
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
Machine learning has emerged as a powerful approach in materials discovery.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
Topological Descriptors Help Predict Guest Adsorption in Nanoporous Materials
Our results not only show a considerable improvement compared to the baseline, but they also highlight that topological features capture information complementary to the structural features: this is especially important for the adsorption at low pressure, a task particularly difficult for the traditional features.
