Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning

• URL: http://arxiv.org/abs/2601.08185v1
• Date: Tue, 13 Jan 2026 03:30:14 GMT
• Title: Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning
• Authors: Ming-Chiang Chang, Maximilian Amsler, Duncan R. Sutherland, Sebastian Ament, Katie R. Gann, Lan Zhou, Louisa M. Smieska, Arthur R. Woll, John M. Gregoire, Carla P. Gomes, R. Bruce van Dover, Michael O. Thompson,
• Abstract summary: We present an autonomous materials synthesis extension to SARA, the Scientific Autonomous Reasoning Agent.<n>We use phase information provided by an automated probabilistic phase labeling algorithm to expedite the search for targeted phase regions.<n>We demonstrate the efficiency of our AI implementation and show that the human input can contribute to significant improvement in sampling efficiency.
• Score: 15.330065664305211
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous experimentation holds the potential to accelerate materials development by combining artificial intelligence (AI) with modular robotic platforms to explore extensive combinatorial chemical and processing spaces. Such self-driving laboratories can not only increase the throughput of repetitive experiments, but also incorporate human domain expertise to drive the search towards user-defined objectives, including improved materials performance metrics. We present an autonomous materials synthesis extension to SARA, the Scientific Autonomous Reasoning Agent, utilizing phase information provided by an automated probabilistic phase labeling algorithm to expedite the search for targeted phase regions. By incorporating human input into an expanded SARA-H (SARA with human-in-the-loop) framework, we enhance the efficiency of the underlying reasoning process. Using synthetic benchmarks, we demonstrate the efficiency of our AI implementation and show that the human input can contribute to significant improvement in sampling efficiency. We conduct experimental active learning campaigns using robotic processing of thin-film samples of several oxide material systems, including Bi$_2$O$_3$, SnO$_x$, and Bi-Ti-O, using lateral-gradient laser spike annealing to synthesize and kinetically trap metastable phases. We showcase the utility of human-in-the-loop autonomous experimentation for the Bi-Ti-O system, where we identify extensive processing domains that stabilize $δ$-Bi$_2$O$_3$ and Bi$_2$Ti$_2$O$_7$, explore dwell-dependent ternary oxide phase behavior, and provide evidence confirming predictions that cationic substitutional doping of TiO$_2$ with Bi inhibits the unfavorable transformation of the metastable anatase to the ground-state rutile phase. The autonomous methods we have developed enable the discovery of new materials and new understanding of materials synthesis and properties.

Related papers

• PeroMAS: A Multi-agent System of Perovskite Material Discovery [51.859972927223936]
  Perovskite Solar Cells (PSCs) are renowned for their superior optoelectronic performance and cost potential.<n>Existing AI approaches focus predominantly on discrete models, including material design, process optimization, and property prediction.<n>We propose a multi-agent system for perovskite material discovery, named PeroMAS.
  arXiv  Detail & Related papers  (2026-02-10T09:33:06Z)
• Towards Agentic Intelligence for Materials Science [73.4576385477731]
  This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
  arXiv  Detail & Related papers  (2026-01-29T23:48:43Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems [62.08647860272078]
  This paper uses the term "Gentelligent system" to refer to systems that incorporate inherent component information and automated mechanisms.<n>By implementing reliable fault detection methods, manufacturers can achieve several benefits, including improved product quality, increased yield, and reduced production costs.
  arXiv  Detail & Related papers  (2025-11-20T23:50:55Z)
• Human-AI collaborative autonomous synthesis with pulsed laser deposition for remote epitaxy [1.7745320870460644]
  We develop and deploy a human-AI collaborative (HAIC) workflow that integrates large language models for hypothesis generation and analysis.<n>HAIC accelerated the hypothesis formation and experimental design and efficiently mapped the growth space to graphene-damage.
  arXiv  Detail & Related papers  (2025-11-14T18:48:52Z)
• AgentEvolver: Towards Efficient Self-Evolving Agent System [51.54882384204726]
  We present AgentEvolver, a self-evolving agent system that drives autonomous agent learning.<n>AgentEvolver introduces three synergistic mechanisms: self-questioning, self-navigating, and self-attributing.<n>Preliminary experiments indicate that AgentEvolver achieves more efficient exploration, better sample utilization, and faster adaptation compared to traditional RL-based baselines.
  arXiv  Detail & Related papers  (2025-11-13T15:14:47Z)
• Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges [38.405196084093454]
  We present a proof-of-concept multi-agent Artificial Intelligence framework to support sustainable protein production research.<n>A literature search agent retrieves relevant scientific literature on microbial protein production for a specified microbial strain.<n>An information extraction agent processes the retrieved content to extract relevant biological and chemical information.
  arXiv  Detail & Related papers  (2025-06-25T16:37:46Z)
• Adaptive AI decision interface for autonomous electronic material discovery [9.228340729592736]
  We develop and implement an AI decision interface on our AI/AE system.<n>The central element of the interface is an AI advisor that performs real-time progress monitoring, data analysis, and interactive human-AI collaboration.<n>We apply this platform to an emerging type of electronic materials-mixed ion-electron conducting polymers.
  arXiv  Detail & Related papers  (2025-04-17T21:26:48Z)
• ChemMiner: A Large Language Model Agent System for Chemical Literature Data Mining [56.15126714863963]
  ChemMiner is an end-to-end framework for extracting chemical data from literature.<n>ChemMiner incorporates three specialized agents: a text analysis agent for coreference mapping, a multimodal agent for non-textual information extraction, and a synthesis analysis agent for data generation.<n> Experimental results demonstrate reaction identification rates comparable to human chemists while significantly reducing processing time, with high accuracy, recall, and F1 scores.
  arXiv  Detail & Related papers  (2024-02-20T13:21:46Z)
• Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [55.30328162764292]
  Chemist-X is a comprehensive AI agent that automates the reaction condition optimization (RCO) task in chemical synthesis.<n>The agent uses retrieval-augmented generation (RAG) technology and AI-controlled wet-lab experiment executions.<n>Results of our automatic wet-lab experiments, achieved by fully LLM-supervised end-to-end operation with no human in the lope, prove Chemist-X's ability in self-driving laboratories.
  arXiv  Detail & Related papers  (2023-11-16T01:21:33Z)
• Automated Biodesign Engineering by Abductive Meta-Interpretive Learning [8.788941848262786]
  We propose an automated biodesign engineering framework empowered by Abductive Meta-Interpretive Learning ($Meta_Abd$) In this work, we propose an automated biodesign engineering framework empowered by Abductive Meta-Interpretive Learning ($Meta_Abd$)
  arXiv  Detail & Related papers  (2021-05-17T12:10:26Z)
• Autonomous synthesis of metastable materials [13.506040229814499]
  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. We demonstrate its performance by autonomously mapping synthesis phase boundaries for the Bi$$O$_3$ system, leading to orders-of- acceleration in establishment of a synthesis phase diagram.
  arXiv  Detail & Related papers  (2021-01-19T00:29:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.