# Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping

arXiv: https://arxiv.org/abs/2306.10406 · HTML: https://mlsanity.com/paper/2306.10406/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 17 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Autonomous synthesis of metastable materials](https://mlsanity.com/paper/2101.07385/) (2021)
- [Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping](https://mlsanity.com/paper/2111.07478/) (2021)

## Built on by (most cited first, 10 of 10)

- [Osprey: Production-Ready Agentic AI for Safety-Critical Control Systems](https://mlsanity.com/paper/2508.15066/) (2025)
- [Polymer-Agent: Large Language Model Agent for Polymer Design](https://mlsanity.com/paper/2601.16376/) (2026)
- [Adaptive AI decision interface for autonomous electronic material discovery](https://mlsanity.com/paper/2504.13344/) (2025)
- [Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores](https://mlsanity.com/paper/2512.01080/) (2025)
- [Autonomous battery research: Principles of heuristic operando experimentation](https://mlsanity.com/paper/2601.00851/) (2025)
- [Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery](https://mlsanity.com/paper/2605.04375/) (2026)
- [Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning](https://mlsanity.com/paper/2601.08185/) (2026)
- [LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments](https://mlsanity.com/paper/2605.06839/) (2026)
- [LEAP: A closed-loop framework for perovskite precursor additive discovery](https://mlsanity.com/paper/2605.20242/) (2026)
- [Agentic programs: an emerging form of scientific software in computational materials science](https://mlsanity.com/paper/2609.00795/) (2026)

## Abstract (arXiv)

Autonomous experimentation (AE) combines machine learning and research hardware automation in a closed loop, guiding subsequent experiments toward user goals. As applied to materials research, AE can accelerate materials exploration, reducing time and cost compared to traditional Edisonian studies. Additionally, integrating knowledge from diverse sources including theory, simulations, literature, and domain experts can boost AE performance. Domain experts may provide unique knowledge addressing tasks that are difficult to automate. Here, we present a set of methods for integrating human input into an autonomous materials exploration campaign for composition-structure phase mapping. The methods are demonstrated on x-ray diffraction data collected from a thin film ternary combinatorial library. At any point during the campaign, the user can choose to provide input by indicating regions-of-interest, likely phase regions, and likely phase boundaries based on their prior knowledge (e.g., knowledge of the phase map of a similar material system), along with quantifying their certainty. The human input is integrated by defining a set of probabilistic priors over the phase map. Algorithm output is a probabilistic distribution over potential phase maps, given the data, model, and human input. We demonstrate a significant improvement in phase mapping performance given appropriate human input.