# Autonomous synthesis of metastable materials

arXiv: https://arxiv.org/abs/2101.07385 · HTML: https://mlsanity.com/paper/2101.07385/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 88 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Active learning machine learns to create new quantum experiments](https://mlsanity.com/paper/1706.00868/) (2017)

## Built on by (most cited first, 23 of 23)

- [Unexpected Improvements to Expected Improvement for Bayesian Optimization](https://mlsanity.com/paper/2310.20708/) (2023)
- [Latent Bayesian Optimization via Autoregressive Normalizing Flows](https://mlsanity.com/paper/2504.14889/) (2025)
- [Inversion-based Latent Bayesian Optimization](https://mlsanity.com/paper/2411.05330/) (2024)
- [Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping](https://mlsanity.com/paper/2306.10406/) (2023)
- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery](https://mlsanity.com/paper/2307.05378/) (2023)
- [Robust Gaussian Processes via Relevance Pursuit](https://mlsanity.com/paper/2410.24222/) (2024)
- [ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data](https://mlsanity.com/paper/2211.07881/) (2022)
- [Scalable Multi-Agent Lab Framework for Lab Optimization](https://mlsanity.com/paper/2208.09099/) (2022)
- [Active learning-assisted neutron spectroscopy with log-Gaussian processes](https://mlsanity.com/paper/2209.00980/) (2022)
- [Sustainable Concrete via Bayesian Optimization](https://mlsanity.com/paper/2310.18288/) (2023)
- [Probabilistic Phase Labeling and Lattice Refinement for Autonomous Material Research](https://mlsanity.com/paper/2308.07897/) (2023)
- [Real-time experiment-theory closed-loop interaction for autonomous materials science](https://mlsanity.com/paper/2410.17430/) (2024)
- [Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning](https://mlsanity.com/paper/2311.06228/) (2023)
- [Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges](https://mlsanity.com/paper/2411.15351/) (2024)
- [Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery](https://mlsanity.com/paper/2605.04375/) (2026)
- [Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materials](https://mlsanity.com/paper/2605.18033/) (2026)
- [Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films](https://mlsanity.com/paper/2207.02209/) (2022)
- [Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint](https://mlsanity.com/paper/2311.10278/) (2023)
- [Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design](https://mlsanity.com/paper/2502.11369/) (2025)
- [Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning](https://mlsanity.com/paper/2601.08185/) (2026)
- [BOxCrete: A Bayesian Optimization Open-Source AI Model for Concrete Strength Forecasting and Mix Optimization](https://mlsanity.com/paper/2603.21525/) (2026)
- [Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy](https://mlsanity.com/paper/2604.23821/) (2026)

## Abstract (arXiv)

Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery. Non-equilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery and development. The mapping of non-equilibrium synthesis phase diagrams has recently been accelerated via high throughput experimentation but still limits materials research because the parameter space is too vast to be exhaustively explored. We demonstrate accelerated synthesis and exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis and characterization along with a hierarchy of AI methods that efficiently reveal the structure of processing phase diagrams. SARA designs lateral gradient laser spike annealing (lg-LSA) experiments for parallel materials synthesis and employs optical spectroscopy to rapidly identify phase transitions. Efficient exploration of the multi-dimensional parameter space is achieved with nested active learning (AL) cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments as well as end-to-end uncertainty quantification. With this, and the coordination of AL at multiple scales, SARA embodies AI harnessing of complex scientific tasks. We demonstrate its performance by autonomously mapping synthesis phase boundaries for the Bi2O3 system, leading to orders-of-magnitude acceleration in establishment of a synthesis phase diagram that includes conditions for kinetically stabilizing δ-Bi2O3 at room temperature, a critical development for electrochemical technologies such as solid oxide fuel cells.