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Found 3 papers, 1 papers with code
Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning
DRNets compensate for the limited data by exploiting and magnifying the rich prior knowledge about the thermodynamic rules governing the mixtures of crystals with constraint reasoning seamlessly integrated into neural network optimization.
Materials Representation and Transfer Learning for Multi-Property Prediction
To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning.
BIG-bench Machine Learning
Generative Adversarial Network
+4
Autonomous synthesis of metastable materials
Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery.
