122 citations · 143 across the 6 of their papers we have counts for
6 papers
XxaCT-NN: Structure Agnostic Multimodal Learning for Materials Science
Jithendaraa Subramanian, Linda Hung, Daniel Schweigert +2
Recent advances in materials discovery have been driven by structure-based models, particularly those using crystal graphs. While effective for computational datasets, these models…
UniMat: Unifying Materials Embeddings through Multi-modal Learning
Janghoon Ock, Joseph Montoya, Daniel Schweigert +3
Materials science datasets are inherently heterogeneous and are available in different modalities such as characterization spectra, atomic structures, microscopic images, and text-…
Network analysis of synthesizable materials discovery
Muratahan Aykol, Vinay I. Hegde, Linda Hung +4
Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that de…
Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery
Yexiang Xue, Junwen Bai, Ronan Le Bras +8
High-Throughput materials discovery involves the rapid synthesis, measurement, and characterization of many different but structurally-related materials. A key problem in materials…
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…
Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery
Stefano Ermon, Ronan Le Bras, Santosh K. Suram +4
Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining. Motivated by a pattern decomposition problem in mat…