PeroMAS: A Multi-agent System of Perovskite Material Discovery

• URL: http://arxiv.org/abs/2602.13312v1
• Date: Tue, 10 Feb 2026 09:33:06 GMT
• Title: PeroMAS: A Multi-agent System of Perovskite Material Discovery
• Authors: Yishu Wang, Wei Liu, Yifan Li, Shengxiang Xu, Xujie Yuan, Ran Li, Yuyu Luo, Jia Zhu, Shimin Di, Min-Ling Zhang, Guixiang Li,
• Abstract summary: 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.
• Score: 51.859972927223936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a pioneer of the third-generation photovoltaic revolution, Perovskite Solar Cells (PSCs) are renowned for their superior optoelectronic performance and cost potential. The development process of PSCs is precise and complex, involving a series of closed-loop workflows such as literature retrieval, data integration, experimental design, and synthesis. However, existing AI perovskite approaches focus predominantly on discrete models, including material design, process optimization,and property prediction. These models fail to propagate physical constraints across the workflow, hindering end-to-end optimization. In this paper, we propose a multi-agent system for perovskite material discovery, named PeroMAS. We first encapsulated a series of perovskite-specific tools into Model Context Protocols (MCPs). By planning and invoking these tools, PeroMAS can design perovskite materials under multi-objective constraints, covering the entire process from literature retrieval and data extraction to property prediction and mechanism analysis. Furthermore, we construct an evaluation benchmark by perovskite human experts to assess this multi-agent system. Results demonstrate that, compared to single Large Language Model (LLM) or traditional search strategies, our system significantly enhances discovery efficiency. It successfully identified candidate materials satisfying multi-objective constraints. Notably, we verify PeroMAS's effectiveness in the physical world through real synthesis experiments.

Related papers

• 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)
• Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders [0.015295722752489374]
  Variational Autoencoders (VAEs) provide a powerful means of constructing such low-dimensional representations.<n>VAEs are often optimized through trial-and-error and empirical analysis.<n>We investigated reward-based strategies for evaluating latent space representations.
  arXiv  Detail & Related papers  (2025-09-30T20:15:42Z)
• AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models [78.08374249341514]
  The rapid development of AI-generated content (AIGC) has led to the misuse of AI-generated images (AIGI) in spreading misinformation.<n>We introduce a large-scale and comprehensive dataset, Holmes-Set, which includes an instruction-tuning dataset with explanations on whether images are AI-generated.<n>Our work introduces an efficient data annotation method called the Multi-Expert Jury, enhancing data generation through structured MLLM explanations and quality control.<n>In addition, we propose Holmes Pipeline, a meticulously designed three-stage training framework comprising visual expert pre-training, supervised fine-tuning, and direct preference optimization
  arXiv  Detail & Related papers  (2025-07-03T14:26:31Z)
• Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization [80.82828320306464]
  Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications.<n>PDD is an iterative, five-step process that consists of: i.e. deriving device behavior from design parameters, ii. simulating device performance, iv. fabricating the optimal device, and v. measuring device performance.<n>PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes.<n>In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD
  arXiv  Detail & Related papers  (2025-06-24T23:32:54Z)
• A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion [0.0]
  A major challenge that impedes the construction of foundation process-property models is data scarcity.<n>We generate experimental datasets from 17-4 PH and 316L stainless steels (SSs) in Laser Powder Bed Fusion (LPBF)<n>We then leverage Gaussian processes (GPs) for process-property modeling in various configurations to test if knowledge about one material system or property can be leveraged to build more accurate machine learning models for other material systems or properties.
  arXiv  Detail & Related papers  (2025-03-20T19:29:38Z)
• Towards Fully-Automated Materials Discovery via Large-Scale Synthesis Dataset and Expert-Level LLM-as-a-Judge [6.500470477634259]
  Our work aims to support the materials science community by providing a practical, data-driven resource.<n>We have curated a comprehensive dataset of 17K expert-verified synthesis recipes from open-access literature.<n>AlchemicalBench offers an end-to-end framework that supports research in large language models applied to synthesis prediction.
  arXiv  Detail & Related papers  (2025-02-23T06:16:23Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence [0.0]
  The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLM) Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys.
  arXiv  Detail & Related papers  (2024-07-13T22:46:02Z)
• Predictive modeling approaches in laser-based material processing [59.04160452043105]
  This study aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms. Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
  arXiv  Detail & Related papers  (2020-06-13T17:28:52Z)
• Intelligent multiscale simulation based on process-guided composite database [0.0]
  We present an integrated data-driven modeling framework based on process modeling, material homogenization, and machine learning. We are interested in the injection-molded short fiber reinforced composites, which have been identified as key material systems in automotive, aerospace, and electronics industries.
  arXiv  Detail & Related papers  (2020-03-20T20:39:19Z)

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.