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

arXiv: https://arxiv.org/abs/2304.02484 · HTML: https://mlsanity.com/paper/2304.02484/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 65 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [Bayesian Optimization in a Billion Dimensions via Random Embeddings](https://mlsanity.com/paper/1301.1942/) (2013)
- [A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization](https://mlsanity.com/paper/2111.05040/) (2021)
- [BOCK : Bayesian Optimization with Cylindrical Kernels](https://mlsanity.com/paper/1806.01619/) (2018)
- [High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning](https://mlsanity.com/paper/2106.03609/) (2021)
- [Constrained Bayesian Optimization for Automatic Chemical Design](https://mlsanity.com/paper/1709.05501/) (2017)
- [Bayesian Optimization in Materials Science: A Survey](https://mlsanity.com/paper/2108.00002/) (2021)
- [Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns](https://mlsanity.com/paper/2202.00204/) (2022)
- [Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach](https://mlsanity.com/paper/2207.00128/) (2022)
- [Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning](https://mlsanity.com/paper/2203.10181/) (2022)
- [A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-box Design Problems](https://mlsanity.com/paper/2110.11070/) (2021)

## Built on by (most cited first, 2 of 2)

- [Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities](https://mlsanity.com/paper/2402.13402/) (2024)
- [Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization](https://mlsanity.com/paper/2507.19316/) (2025)

## Abstract (arXiv)

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. In virtually all cases, the target property of interest for optimization is defined apriori with limited human feedback during operation. In contrast, here we present the development of a new type of human in the loop experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly, employing human feedback. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and then implement this in real time on an atomic force microscope, where the optimization proceeds to find symmetric piezoresponse amplitude hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human-augmented machine learning approaches for curiosity-driven exploration of systems across experimental domains. The analysis reported here is summarized in Colab Notebook for the purpose of tutorial and application to other data: https://github.com/arpanbiswas52/varTBO