99 citations · 211 across the 11 of their papers we have counts for
12 papers
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…
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction
Junwen Bai, Yuanqi Du, Yingheng Wang +3
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property…
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…
Deep Reasoning Networks: Thinking Fast and Slow
Di Chen, Yiwei Bai, Wenting Zhao +3
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving complex tasks, typically in an unsupervised or weakly-…
Exponentially-Modified Gaussian Mixture Model: Applications in Spectroscopy
Sebastian Ament, John Gregoire, Carla Gomes
We propose a novel exponentially-modified Gaussian (EMG) mixture residual model. The EMG mixture is well suited to model residuals that are contaminated by a distribution with posi…
End-to-End Refinement Guided by Pre-trained Prototypical Classifier
Junwen Bai, Zihang Lai, Runzhe Yang +3
Many real-world tasks involve identifying patterns from data satisfying background or prior knowledge. In domains like materials discovery, due to the flaws and biases in raw exper…
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…