Tongqi Wen

Yun Hao, Che Fan, Beilin Ye, Wenhao Lu, Zhen Lu, Peilin Zhao, Zhifeng Gao*, Qingyao Wu*, Yanhui Liu*, Tongqi Wen*

February 25, 2025

🏗 How to efficiently mine high-quality knowledge from literature and apply it to new material discovery?

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.

Zhuoyuan Li, Siyu Liu, Beilin Ye, David J. Srolovitz*, Tongqi Wen*

February 24, 2025

⚛️ Can AI automatically help us discover new materials with given properties?

Artificial intelligence (AI) is transforming materials science, enabling both theoretical advancements and accelerated materials discovery. Recent progress in crystal generation models, which design crystal structures for targeted properties, and foundation atomic models (FAMs), which capture interatomic interactions across the periodic table, has significantly improved inverse materials design. However, an efficient integration of these two approaches remains an open challenge. Here, we present an active learning framework that combines crystal generation models and foundation atomic models to enhance the accuracy and efficiency of inverse design. As a case study, we employ Con-CDVAE to generate candidate crystal structures and MACE-MP-0 FAM as one of the high-throughput screeners for bulk modulus evaluation. Through iterative active learning, we demonstrate that Con-CDVAE progressively improves its accuracy in generating crystals with target properties, highlighting the effectiveness of a property-driven fine-tuning process. Our framework is general to accommodate different crystal generation and foundation atomic models, and establishes a scalable approach for AI-driven materials discovery. By bridging generative modeling with atomic-scale simulations, this work paves the way for more accurate and efficient inverse materials design.

Bo Hu, Siyu Liu, Beilin Ye, Yun Hao, Tongqi Wen*

November 25, 2024

🚀 Does AI possess the intelligence to autonomously discover materials laws?

This paper introduces a multi-agent framework based on large language models (LLMs) specifically designed for symbolic regression in materials science. The framework was applied to derive an interpretable formula for the glass-forming ability of metallic glasses, achieving a correlation coefficient of up to 0.948 with low formula complexity.

Siyu Liu, Tongqi Wen*, Beilin Ye, Zhuoyuan Li, David J. Srolovitz*

November 19, 2024

🧀 Can the materials knowledge stored in LLMs help us predict material properties?

Efficient and accurate prediction of material properties is critical for advancing materials design and applications. The rapid-evolution of large language models (LLMs) presents a new opportunity for material property predictions, complementing experimental measurements and multi-scale computational methods. We focus on predicting the elastic constant tensor, as a case study, and develop domain-specific LLMs for predicting elastic constants and for materials discovery. The proposed ElaTBot LLM enables simultaneous prediction of elastic constant tensors, bulk modulus at finite temperatures, and the generation of new materials with targeted properties. Moreover, the capabilities of ElaTBot are further enhanced by integrating with general LLMs (GPT-4o) and Retrieval-Augmented Generation (RAG) for prediction. A specialized variant, ElaTBot-DFT, designed for 0 K elastic constant tensor prediction, reduces the prediction errors by 33.1% compared with domain-specific, material science LLMs (Darwin) trained on the same dataset. This natural language-based approach lowers the barriers to computational materials science and highlights the broader potential of LLMs for material property predictions and inverse design.