30 citations · 99 across the 12 of their papers we have counts for
14 papers
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…
Physics-Informed Tree Search for High-Dimensional Computational Design
Suvo Banik, Troy D. Loeffler, Henry Chan +4
High-dimensional design spaces underpin a wide range of physics-based modeling and computational design tasks in science and engineering. These problems are commonly formulated as…
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…
Active Learning A Neural Network Model For Gold Clusters \& Bulk From Sparse First Principles Training Data
Troy D Loeffler, Sukriti Manna, Tarak K Patra +3
Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs d…
Combinatorial Black-Box Optimization with Expert Advice
Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios +4
We consider the problem of black-box function optimization over the boolean hypercube. Despite the vast literature on black-box function optimization over continuous domains, not m…
Accelerating Copolymer Inverse Design using AI Gaming algorithm
Tarak K Patra, Troy D. Loeffler, Subramanian K R S Sankaranarayanan
There exists a broad class of sequencing problems, for example, in proteins and polymers that can be formulated as a heuristic search algorithm that involve decision making akin to…
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,…
BLAST: Bridging Length/time scales via Atomistic Simulation Toolkit
Henry Chan, Badri Narayanan, Mathew Cherukara +4
The ever-increasing power of supercomputers coupled with highly scalable simulation codes have made molecular dynamics an indispensable tool in applications ranging from predictive…
Active Learning the Coarse-Grained Energy Landscape For Water Clusters From Sparse Training Data
Troy D. Loeffler, Tarak K. Patra, Henry Chan +2
ANNs are currently trained by generating large quantities (On the order of or greater) of structural data in hopes that the ANN has adequately sampled the energy landscape…
A coarse-grained deep neural network model for liquid water
Tarak K Patra, Troy D. Loeffler, Henry Chan +3
We introduce a coarse-grained deep neural network model (CG-DNN) for liquid water that utilizes 50 rotational and translational invariant coordinates, and is trained exclusively ag…
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…