Inverse Materials Design by Large Language Model-Assisted Generative Framework

• URL: http://arxiv.org/abs/2502.18127v1
• Date: Tue, 25 Feb 2025 11:52:59 GMT
• Title: Inverse Materials Design by Large Language Model-Assisted Generative Framework
• Authors: Yun Hao, Che Fan, Beilin Ye, Wenhao Lu, Zhen Lu, Peilin Zhao, Zhifeng Gao, Qingyao Wu, Yanhui Liu, Tongqi Wen,
• Abstract summary: AlloyGAN is a framework that integrates Large Language Model (LLM)-assisted text mining with Conditional Generative Adversarial Networks (CGANs)<n>For metallic glasses, the framework predicts thermodynamic properties with discrepancies of less than 8% from experiments.<n>By bridging generative AI with domain knowledge, AlloyGAN offers a scalable approach to accelerate the discovery of materials with tailored properties.
• Score: 35.04390544440238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep generative models hold great promise for inverse materials design, yet their efficiency and accuracy remain constrained by data scarcity and model architecture. Here, we introduce AlloyGAN, a closed-loop framework that integrates Large Language Model (LLM)-assisted text mining with Conditional Generative Adversarial Networks (CGANs) to enhance data diversity and improve inverse design. Taking alloy discovery as a case study, AlloyGAN systematically refines material candidates through iterative screening and experimental validation. For metallic glasses, the framework predicts thermodynamic properties with discrepancies of less than 8% from experiments, demonstrating its robustness. By bridging generative AI with domain knowledge and validation workflows, AlloyGAN offers a scalable approach to accelerate the discovery of materials with tailored properties, paving the way for broader applications in materials science.

Related papers

• CrystalFormer-RL: Reinforcement Fine-Tuning for Materials Design [2.290956583394892]
  We explore the applications of reinforcement fine-tuning to the autoregressive transformer-based materials generative model CrystalFormer. By optimizing reward signals, fine-tuning infuses knowledge from discriminative models into generative models. The resulting model, CrystalFormer-RL, shows enhanced stability in generated crystals and successfully discovers crystals with desirable yet conflicting material properties.
  arXiv  Detail & Related papers  (2025-04-03T07:59:30Z)
• 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. We generate experimental datasets from 17-4 PH and 316L stainless steels (SSs) in Laser Powder Bed Fusion (LPBF) 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)
• Causal Discovery from Data Assisted by Large Language Models [50.193740129296245]
  It is essential to integrate experimental data with prior domain knowledge for knowledge driven discovery. Here we demonstrate this approach by combining high-resolution scanning transmission electron microscopy (STEM) data with insights derived from large language models (LLMs) By fine-tuning ChatGPT on domain-specific literature, we construct adjacency matrices for Directed Acyclic Graphs (DAGs) that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO3 (SmBFO)
  arXiv  Detail & Related papers  (2025-03-18T02:14:49Z)
• UniGenX: Unified Generation of Sequence and Structure with Autoregressive Diffusion [61.690978792873196]
  Existing approaches rely on either autoregressive sequence models or diffusion models. We propose UniGenX, a unified framework that combines autoregressive next-token prediction with conditional diffusion models. We validate the effectiveness of UniGenX on material and small molecule generation tasks.
  arXiv  Detail & Related papers  (2025-03-09T16:43:07Z)
• Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models [2.8408587358426725]
  We present an active learning framework that combines crystal generation models and foundation atomic models to enhance the accuracy and efficiency of inverse design.<n>Our framework is general to accommodate different crystal generation and foundation atomic models, and establishes a scalable approach for AI-driven materials discovery.
  arXiv  Detail & Related papers  (2025-02-24T09:15:38Z)
• Open Materials Generation with Stochastic Interpolants [14.939468363546384]
  We introduce Open Materials Generation (OMG), a unifying framework for the generative design and discovery of crystalline materials.<n>OMG employs inorganic interpolants (SI) to bridge an arbitrary base distribution to the target distribution of crystals.<n>We benchmark OMG's performance on two tasks: Crystal Structure Prediction (CSP) for specified compositions, and 'de novo' generation (DNG) aimed at discovering stable, novel, and unique structures.
  arXiv  Detail & Related papers  (2025-02-04T18:56:47Z)
• 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)
• Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient [52.2669490431145]
  PropEn is inspired by'matching', which enables implicit guidance without training a discriminator. We show that training with a matched dataset approximates the gradient of the property of interest while remaining within the data distribution.
  arXiv  Detail & Related papers  (2024-05-28T11:30:19Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
  We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization. We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons. Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
  arXiv  Detail & Related papers  (2024-03-20T00:41:12Z)
• InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling [66.3072381478251]
  Reward hacking, also termed reward overoptimization, remains a critical challenge. We propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective. We show that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets.
  arXiv  Detail & Related papers  (2024-02-14T17:49:07Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• Evaluating the diversity and utility of materials proposed by generative models [38.85523285991743]
  We show how one state-of-the-art generative model, the physics-guided crystal generation model, can be used as part of the inverse design process. Our findings suggest how generative models might be improved to enable better inverse design.
  arXiv  Detail & Related papers  (2023-08-09T14:42:08Z)
• Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery [4.923937591056569]
  We introduce physics-informed multimodal autoencoders (PIMA) PIMA is a variational inference framework for discovering shared information in multimodal scientific datasets. A dataset of lattice metamaterials from metal additive manufacturing demonstrates accurate cross modal inference.
  arXiv  Detail & Related papers  (2022-02-07T14:47:00Z)

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.