99 citations · 277 across the 9 of their papers we have counts for
10 papers
Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning
Ming-Chiang Chang, Maximilian Amsler, Duncan R. Sutherland +9
Autonomous experimentation holds the potential to accelerate materials development by combining artificial intelligence (AI) with modular robotic platforms to explore extensive com…
Probabilistic Phase Labeling and Lattice Refinement for Autonomous Material Research
Ming-Chiang Chang, Sebastian Ament, Maximilian Amsler +6
X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially…
MHub: Unlocking the Potential of Machine Learning for Materials Discovery
Yuanqi Du, Yingheng Wang, Yining Huang +6
We introduce MHub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especial…
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…
The Fast Kernel Transform
John Paul Ryan, Sebastian Ament, Carla P. Gomes +1
Kernel methods are a highly effective and widely used collection of modern machine learning algorithms. A fundamental limitation of virtually all such methods are computations invo…
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…
Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System
Santosh K. Suram, Yexiang Xue, Junwen Bai +8
Rapid construction of phase diagrams is a central tenet of combinatorial materials science with accelerated materials discovery efforts often hampered by challenges in interpreting…