Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning

1 code implementation • 19 Apr 2024 • Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop automated experiment, is emerging as a crucial objective for accelerating scientific research.

Active Learning

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

1 code implementation • 3 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

Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy

1 code implementation • 30 Jan 2024 • Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Sergei V. Kalinin

This paper presents a comprehensive analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Kernel Learning (DKL) within the realm of AE in Scanning Probe Microscopy.