most citedMic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cond-mat.mtrl-sci2026

Dynamics of Null and Electrostatic Blind Spots for Quantitative PFM

Marti Checa, Holland Swicegood, Ruben Millan-Solsona +5

Piezoresponse force microscopy is a cornerstone technique for probing nanoscale electromechanical phenomena, yet quantitative and in some cases qualitative interpretation remains h…

cond-mat.mtrl-sci2026

AEcroscopyWave: Towards Self-Driving Characterization Platforms for Agentic AI

Yongtao Liu, Jawad Chowdhury, Ganesh Narasimha +8

The characterization of electronic materials has traditionally been stratified into two distinct regimens: industry-scale automated systems to inspect materials for defects and ens…

cs.LG2026

Beyond Scalar Objectives: Expert-Feedback-Driven Autonomous Experimentation for Scientific Discovery at the Nanoscale

Ralph Bulanadi, Jefferey Baxter, Arpan Biswas +5

Self-driving laboratories or autonomous experimentation are emerging as transformative platforms for accelerating scientific discovery. Bayesian optimization (BO) is among the most…

cond-mat.mtrl-sci2025

Auto-3DPFM: Automating Polarization-Vector Mapping at the Nanoscale

Ralph Bulanadi, Marti Checa, Michelle Wang +9

The functional properties of ferroelectric materials are strongly influenced by ferroelectric polarization orientation; as such, access to consistent and precise characterization o…

cond-mat.mtrl-sci2025

Human-AI collaborative autonomous synthesis with pulsed laser deposition for remote epitaxy

Asraful Haque, Daniel T. Yimam, Jawad Chowdhury +10

Autonomous laboratories typically rely on data-driven decision-making, occasionally with human-in-the-loop oversight to inject domain expertise. Fully leveraging AI agents, however…

cs.LG2025

Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments

Ralph Bulanadi, Jawad Chowdhury, Funakubo Hiroshi +4

Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primari…

cond-mat.mtrl-sci2025★ 1 cited

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…