30 citations · 99 across the 12 of their papers we have counts for
7 papers · 1 filter
Symbolic Ensemble Learning Enables Discovery of Fast Accurate Physics-Based Interatomic Potentials
Bilvin Varughese, Aditya Koneru, Adil Muhammad +6
Machine learning has transformed materials simulation by delivering force fields with ab initio accuracy, yet bridging the gap between high-dimensional regression and physical inte…
Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning
Bilvin Varughese, Troy D. Loeffler, Suvo Banik +9
The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture mul…
Machine learning for classifying and interpreting coherent X-ray speckle patterns
Mingren Shen, Dina Sheyfer, Troy David Loeffler +4
Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure…
Learning with Delayed Rewards -- A case study on inverse defect design in 2D materials
Suvo Banik, Troy D Loeffler, Rohit Batra +3
Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends…
Machine Learning the Metastable Phase Diagram of Materials
Srilok Srinivasan, Rohit Batra, Duan Luo +8
Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition. Conventional phase diagram…
Ultrafast formation of transient 2D diamond-like structure in twisted bilayer graphene
Duan Luo, Dandan Hui, Bin Wen +17
Due to the absence of matching carbon atoms at honeycomb centers with carbon atoms in adjacent graphene sheets, theorists predicted that a sliding process is needed to form AA, AB,…
Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
Sandeep Madireddy, Ding-Wen Chung, Troy Loeffler +4
Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial…