99 citations · 266 across the 4 of their papers we have counts for
5 papers
Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings
Shufeng Kong, Francesco Ricci, Dan Guevarra +3
Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important…
Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning
Di Chen, Yiwei Bai, Sebastian Ament +7
Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials.…
Materials Representation and Transfer Learning for Multi-Property Prediction
Shufeng Kong, Dan Guevarra, Carla P. Gomes +1
The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine…
Autonomous synthesis of metastable materials
Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland +7
Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery. Non-equilibrium materials synthesis is emblematic of…
Optical Identification of Materials Transformations in Oxide Thin Films
Duncan R. Sutherland, Aine Boyer Connolly, Maximilian Amsler +9
Recent advances in high-throughput experimentation for combinatorial studies have accelerated the discovery and analysis of materials across a wide range of compositions and synthe…