AutoMAT: A Hierarchical Framework for Autonomous Alloy Discovery

• URL: http://arxiv.org/abs/2507.16005v1
• Date: Mon, 21 Jul 2025 18:55:03 GMT
• Title: AutoMAT: A Hierarchical Framework for Autonomous Alloy Discovery
• Authors: Penghui Yang, Chendong Zhao, Bijun Tang, Zhonghan Zhang, Xinrun Wang, Yanchen Deng, Yuhao Lu, Cuntai Guan, Zheng Liu, Bo An,
• Abstract summary: AutoMAT is a hierarchical and autonomous framework grounded in and validated by experiments.<n>It integrates large language models, automated CALPHAD-based simulations, and AI-driven search to accelerate alloy design.<n>It achieves high efficiency, accuracy, and interpretability without the need for manually curated large datasets.
• Score: 25.866718912999893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Alloy discovery is central to advancing modern industry but remains hindered by the vastness of compositional design space and the costly validation. Here, we present AutoMAT, a hierarchical and autonomous framework grounded in and validated by experiments, which integrates large language models, automated CALPHAD-based simulations, and AI-driven search to accelerate alloy design. Spanning the entire pipeline from ideation to validation, AutoMAT achieves high efficiency, accuracy, and interpretability without the need for manually curated large datasets. In a case study targeting a lightweight, high-strength alloy, AutoMAT identifies a titanium alloy with 8.1% lower density and comparable yield strength relative to the state-of-the-art reference, achieving the highest specific strength among all comparisons. In a second case targeting high-yield-strength high-entropy alloys, AutoMAT achieves a 28.2% improvement in yield strength over the base alloy. In both cases, AutoMAT reduces the discovery timeline from years to weeks, illustrating its potential as a scalable and versatile platform for next-generation alloy design.

Related papers

• MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys [42.606804375198465]
  MATAI is a machine learning framework for inverse design of as-cast alloys.<n>It integrates a curated database, deep neural network-based property predictors, a constraint-aware optimization engine, and an iterative AI-experiment feedback loop.<n>We demonstrate MATAI's capabilities on the Ti-based alloy system, a canonical class of lightweight structural materials.
  arXiv  Detail & Related papers  (2025-11-13T09:15:56Z)
• Data Driven Insights into Composition Property Relationships in FCC High Entropy Alloys [28.495739557732175]
  Structural High Entropy Alloys (HEAs) are crucial in advancing technology across various sectors.<n>The scarcity of integrated chemistry, process, structure, and property data presents significant challenges for predictive property modeling.<n>This work presents several sensitivity analyses, highlighting key elemental contributions to mechanical behavior.
  arXiv  Detail & Related papers  (2025-08-06T19:41:15Z)
• Improved Training Strategies for Physics-Informed Neural Networks using Real Experimental Data in Aluminum Spot Welding [0.0]
  Resistance spot welding is the dominant joining process for the body-in-white in the automotive industry, where the weld nugget diameter is the key quality metric.<n>This paper introduces a physics-informed neural network to reconstruct internal process states from experimental data.<n>It supports transferring welding stages from steel to aluminum, and demonstrates strong potential for fast, model-based quality control in industrial applications.
  arXiv  Detail & Related papers  (2025-08-06T16:14:00Z)
• AutoMind: Adaptive Knowledgeable Agent for Automated Data Science [70.33796196103499]
  Large Language Model (LLM) agents have shown great potential in addressing real-world data science problems.<n>Existing frameworks depend on rigid, pre-defined and inflexible coding strategies.<n>We introduce AutoMind, an adaptive, knowledgeable LLM-agent framework.
  arXiv  Detail & Related papers  (2025-06-12T17:59:32Z)
• SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving [51.47621083057114]
  SOLVE is an innovative framework that synergizes Vision-Language Models with end-to-end (E2E) models to enhance autonomous vehicle planning.<n>Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive interaction between VLM and E2E components.
  arXiv  Detail & Related papers  (2025-05-22T15:44:30Z)
• AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
  We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs. Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
  arXiv  Detail & Related papers  (2024-11-02T13:24:30Z)
• Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI [0.0]
  This paper introduces an advanced intrusion detection system (IDS) called KD-XVAE that uses a Variational Autoencoder (VAE)-based knowledge distillation approach. Our model significantly reduces complexity, operating with just 1669 parameters and achieving an inference time of 0.3 ms per batch.
  arXiv  Detail & Related papers  (2024-10-11T17:57:16Z)
• Restore Anything Model via Efficient Degradation Adaptation [129.38475243424563]
  RAM takes a unified path that leverages inherent similarities across various degradations to enable efficient and comprehensive restoration.<n> RAM's SOTA performance confirms RAM's SOTA performance, reducing model complexity by approximately 82% in trainable parameters and 85% in FLOPs.
  arXiv  Detail & Related papers  (2024-07-18T10:26:53Z)
• AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning [54.47116888545878]
  AutoAct is an automatic agent learning framework for QA. It does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models.
  arXiv  Detail & Related papers  (2024-01-10T16:57:24Z)
• AutoML for Large Capacity Modeling of Meta's Ranking Systems [29.717756064694278]
  We present a sampling-based AutoML method for building large capacity models. We show that our method achieves outstanding Return on Investment (ROI) versus human tuned baselines. The proposed AutoML method has already made real-world impact where a discovered Instagram CTR model with up to -0.36% NE gain was selected for large-scale online A/B test and show statistically significant gain.
  arXiv  Detail & Related papers  (2023-11-14T03:00:50Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field with Attention Mechanism and Perceptual Features [1.0660502023086995]
  We propose a new data-driven mesh-density boost model named SuperMeshingNet. It strengthens the advantages of finite element analysis (FEA) with low mesh-density as inputs to the deep learning model. Compared to the baseline that applied the linear method, SuperMeshingNet achieves a prominent reduction in the mean squared error (MSE) and mean absolute error (MAE) on test data.
  arXiv  Detail & Related papers  (2021-03-12T06:02:30Z)
• Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL [53.40030379661183]
  Auto-PyTorch is a framework to enable fully automated deep learning (AutoDL) It combines multi-fidelity optimization with portfolio construction for warmstarting and ensembling of deep neural networks (DNNs) We show that Auto-PyTorch performs better than several state-of-the-art competitors on average.
  arXiv  Detail & Related papers  (2020-06-24T15:15:17Z)

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.