612 citations
- University of TorontoCA23 papers
- Chinese Academy of SciencesCN21 papers
- Perimeter InstituteCA21 papers
- The University of WinnipegCA20 papers
- Wuhan Institute of Physics and MathematicsCN19 papers
- Centre National de la Recherche ScientifiqueFR18 papers
- Université du Québec à MontréalCA17 papers
- Winnipeg Institute for Theoretical PhysicsCA16 papers
- University of WindsorCA13 papers
- Center for Astrophysics Harvard & SmithsonianUS10 papers
- Université TÉLUQCA10 papers
- University of CologneDE9 papers
12 papers · 1 filter
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Kangming Li, Kamal Choudhary, Brian DeCost +2
High-entropy materials (HEMs) have recently emerged as a significant category of materials, offering highly tunable properties. However, the scarcity of HEM data in existing densit…
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
On the redundancy in large material datasets: efficient and robust learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary +3
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multip…
A critical examination of robustness and generalizability of machine learning prediction of materials properties
Kangming Li, Brian DeCost, Kamal Choudhary +2
Recent advances in machine learning (ML) methods have led to substantial improvement in materials property prediction against community benchmarks, but an excellent benchmark score…
Deep Learning and Crystal Plasticity: A Preconditioning Approach for Accurate Orientation Evolution Prediction
Peyman Saidi, Hadi Pirgazi, Mehdi Sanjari +5
Efficient and precise prediction of plasticity by data-driven models relies on appropriate data preparation and a well-designed model. Here we introduce an unsupervised machine lea…
3D Coupled Thermo-Mechanical Phase-Field Modeling of Shape Memory Alloy Dynamics via Isogeometric Analysis
R. Dhote, H. Gomez, R. Melnik +1
The paper focuses on numerical simulation of the phase-field (PF) equations for modeling martensitic transformations in shape memory alloys (SMAs), their complex microstructures an…