1 citations · 1 across the 8 of their papers we have counts for
9 papers
Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials
Steven R. Spurgeon, Milad Abolhasani, Frederick Baddour +28
Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising systems never reach deployment…
PATHFINDER: Multi-objective discovery in structural and spectral spaces
Kamyar Barakati, Boris N. Slautin, Utkarsh Pratiush +2
Automated decision-making is becoming key for automated characterization including electron and scanning probe microscopies and nano indentation. Most machine learning driven workf…
AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design
Utkarsh Pratiush, Huaixun Huyan, Maryam Zahiri Azar +4
Scanning transmission electron microscopy (STEM) has become a cornerstone instrument for semiconductor materials metrology, enabling nanoscale analysis of complex multilayer struct…
Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy
Utkarsh Pratiush, Kamyar Barakati, Boris N. Slautin +4
Modern automated microscopy faces a fundamental discovery challenge: in many systems, the most important scientific information does not reside in the immediately visible image fea…
Sequential versus Manifold Bayesian Optimization under Realistic Experimental Time Constraints
Boris Slautin, Sergei Kalinin
Bayesian optimization (BO) is widely used for autonomous materials discovery, yet its classical sequential formulation is insufficient for design of experimental workflows that oft…
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.…