5 papers
Random Combinatorial Libraries and Automated Nanoindentation for High-Throughput Structural Materials Discovery
Vivek Chawla, Dayakar Penumadu, Sergei Kalinin
Accelerating the discovery of structural materials is essential for applications in hard and refractory alloys, hypersonic platforms, nuclear systems, and other extreme environment…
Accelerated Materials Discovery through Cost-Aware Bayesian Optimization of Real-World Indentation Workflows
Vivek Chawla, Stephen Puplampu, Haochen Zhu +3
Accelerating the discovery of mechanical properties in combinatorial materials requires autonomous experimentation that accounts for both instrument behavior and experimental cost.…
DIVIDE: A Framework for Learning from Independent Multi-Mechanism Data Using Deep Encoders and Gaussian Processes
Vivek Chawla, Boris Slautin, Utkarsh Pratiush +2
Scientific datasets often arise from multiple independent mechanisms such as spatial, categorical or structural effects, whose combined influence obscures their individual contribu…
Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy
Utkarsh Pratiush, Austin Houston, Kamyar Barakati +70
Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with me…
Automating Nanoindentation: Optimizing Workflows for Precision and Accuracy
Vivek Chawla, Dayakar Penumadu, Sergei Kalinin
Nanoindentation is vital for probing mechanical properties, yet traditional grid-based workflows are inefficient for targeting specific microstructural features. We present an auto…