Search Results for author: Maxim A. Ziatdinov

Found 7 papers, 2 papers with code

Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries

1 code implementation3 Feb 2024 Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov, Yongtao Liu, Rohit Pant, Xiaohang Zhang, Ichiro Takeuchi, Maxim A. Ziatdinov, Sergei V. Kalinin

This can be exemplified by the combinatorial libraries that can be explored in multiple locations by multiple tools simultaneously, or downstream characterization in automated synthesis systems.

Bayesian Optimization Dimensionality Reduction +2

A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments

1 code implementation5 Apr 2023 Arpan Biswas, Yongtao Liu, Nicole Creange, Yu-Chen Liu, Stephen Jesse, Jan-Chi Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Rama K. Vasudevan

Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites.

Active Learning Recommendation Systems

Deep Kernel Methods Learn Better: From Cards to Process Optimization

no code implementations25 Mar 2023 Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces.

Active Learning

Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space

no code implementations6 Jan 2023 Ayana Ghosh, Sergei V. Kalinin, Maxim A. Ziatdinov

Discovery of the molecular candidates for applications in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries, necessitates development of machine learning algorithms capable of rapid exploration of the chemical spaces targeting the desired functionalities.

Active Learning Symbolic Regression

Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy

no code implementations22 Mar 2021 Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama K. Vasudevan

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis.

Autonomous Driving Decision Making +1

Mapping causal patterns in crystalline solids

no code implementations2 Mar 2021 Chris Nelson, Anna N. Morozovska, Maxim A. Ziatdinov, Eugene A. Eliseev, Xiaohang Zhang, Ichiro Takeuchi, Sergei V. Kalinin

The evolution of the atomic structures of the combinatorial library of Sm-substituted thin film BiFeO3 along the phase transition boundary from the ferroelectric rhombohedral phase to the non-ferroelectric orthorhombic phase is explored using scanning transmission electron microscopy (STEM).

Data Analysis, Statistics and Probability Materials Science

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