1 citations · 1 across the 7 of their papers we have counts for
4 papers
Autonomous Probe Microscopy with Robust Bag-of-Features Multi-Objective Bayesian Optimization: Pareto-Front Mapping of Nanoscale Structure-Property Trade-Offs
Kamyar Barakati, Haochen Zhu, C Charlotte Buchanan +3
Combinatorial materials libraries are an efficient route to generate large families of candidate compositions, but their impact is often limited by the speed and depth of character…
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…
Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores
Benhour Amirian, Ashley S. Dale, Sergei Kalinin +1
Accelerated material discovery increasingly relies on artificial intelligence and machine learning, collectively termed "AI/ML". A key challenge in using AI is ensuring that human…
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.…