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20242026
most citedAutomated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries

2 citations · 2 across the 4 of their papers we have counts for

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cond-mat.mtrl-sci2026

Multitask Scanning Probe Microscopy

Aditya Raghavan, Yu Liu, Ian Mercer +2

Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials. Its increasing use for wafer-sca…

cond-mat.mtrl-sci2026

From Closed-Loop Optimization to Open Decision Making: Coupled Digital Twins for Predictive and Autonomous Microscopy

Yu Liu, Boris Slautin, Ian Mercer +2

Automated experimentation is moving from closed-loop optimization toward open decision-making, where human or AI planners must forecast the consequences of candidate actions before…

cond-mat.mtrl-sci20252 cited

Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries

Yu Liu, Aditya Raghavan, Utkarsh Pratiush +13

Combinatorial materials libraries provide a powerful platform for mapping how physical properties evolve across binary and ternary cross-sections of multicomponent phase diagrams.…

cond-mat.mtrl-sci2025

Causal Discovery from Data Assisted by Large Language Models

Kamyar Barakati, Alexander Molak, Chris Nelson +3

Knowledge driven discovery of novel materials necessitates the development of the causal models for the property emergence. While in classical physical paradigm the causal relation…

cond-mat.mtrl-sci2024

Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments

Boris N. Slautin, Yu Liu, Jan Dec +4

We have developed a Bayesian optimization (BO) workflow that integrates intra-step noise optimization into automated experimental cycles. Traditional BO approaches in automated exp…