Maxim Ziatdinov

About

Maxim Ziatdinov is a prominent researcher at the intersection of machine learning, autonomous experimentation, and materials science, with particular expertise in electron and scanning probe microscopy. His work has pioneered the integration of artificial intelligence into experimental workflows, enabling automated and autonomous discovery pipelines that dramatically accelerate materials characterization and synthesis. Among his most influential contributions is his development of AI-driven approaches for exploring metal halide perovskites — a class of materials critical to next-generation optoelectronics — including automated robotic chemistry platforms that leverage machine learning to tackle longstanding stability challenges, work that has garnered over 130 citations. Equally impactful is his research on autonomous electron and scanning probe microscopy, where he has advanced deep learning frameworks for atomic-scale fabrication and materials design. Ziatdinov has also made significant strides in Bayesian optimization and active learning, developing dynamic recommender systems that incorporate human expertise into automated experimental loops. His 2023 essay "Probe Microscopy is All You Need" reflects his broader vision of microscopy as an ideal testbed for next-generation AI methods. With a growing citation record exceeding 470 across these works, Ziatdinov is shaping the future of AI-accelerated scientific discovery.

Research Focus

Key Achievements

Top Papers

1. 1
   Chemical Robotics Enabled Exploration of Stability in Multicomponent Lead Halide Perovskites via Machine Learning

   138 citations · 2020
2. 2
   Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy

   134 citations · 2021
3. 3
   Machine learning for high-throughput experimental exploration of metal halide perovskites

   111 citations · 2021
4. 4
   Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication

   32 citations · 2022
5. 5
   A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments

   28 citations · 2024
6. 6
   Probe microscopy is all you need ^*

   18 citations · 2023
7. 7
   A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments

   6 citations · 2023
8. 8
   Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy

   4 citations · 2021
9. 9
   A dynamic Bayesian optimized active recommender system for curiosity-driven partially “Human-in-the-loop” automated experiments

   3 citations · 2023

Key Collaborators

Contact & Links