arXiv subjects

Qionghua Zhou

Publications and source records attributed to Qionghua Zhou.

6 recordsLinked to original sources

arXiv2026

Physics-grounded generative design of inherently stable, novel and controllable crystal structures

Generative inverse design is reshaping the discovery of functional crystalline materials. Yet current generative models face challenges in simultaneously achieving stability, novelty, and precise controllability in a single trained model. We address these challenges with a key physical insight: the diversity of crystals is governed by their crystallographic information (CI), namely composition, space group and lattice, whereas only a few stable atomic configurations remain once the CI is fixed. Built on this insight, we introduce SCGEN (stable and controllable crystal structure generation), a physics-grounded generative model with two components. A variational autoencoder samples diverse, physically plausible CI, and a symmetry- and Wyckoff-position-constrained optimizer locates stable atomic positions via universal machine-learning potentials. Benchmarked on roughly two million structures, SCGEN reaches state-of-the-art stability while preserving comparable novelty, and it satisfies any specified composition, space group, lattice or joint constraint with 100% success and no task-specific retraining. Applied to photocatalytic water splitting, property-guided optimization with SCGEN generates 200,000 candidate structures and identify top 22 stable, active and synthesizable photocatalysts. By decoupling CI generation from coordinate optimization, SCGEN establishes a physics-grounded inverse-design paradigm that yields synthesis-ready crystals on demand, rather than structures requiring post hoc repair, relaxation, or retraining.

cond-mat.mtrl-sci↗

arXiv2026

Multi-Source Domain Transfer Learning for Accurate Property Prediction in Two-Dimensional Materials

Machine learning has revolutionized materials discovery, but data scarcity remains a critical bottleneck for complex functional properties. As emerging systems, two-dimensional (2D) materials possess limited overall data volumes. Evaluating their diverse functional properties requires time-consuming simulations, hindering unified high-throughput screening. Furthermore, restrictions in known structural prototypes lead to highly fragmented data distributions. To address these challenges, we propose a multi-source domain transfer learning framework to extract generalizable and complementary knowledge from diverse crystalline systems. To mitigate data scarcity, the framework employs a shared feature extractor that integrates adversarial transfer learning with maximum mean discrepancy, mapping crystal structures into a domain-invariant latent space while preserving underlying physical correlations. To resolve distribution fragmentation, a sample-adaptive weighted ensemble strategy is subsequently utilized to dynamically aggregate predictions from multiple source domains. Relying solely on crystal structures, the framework predicts 2D carrier mobilities with an R2 score exceeding 0.90. The framework successfully screened 55 novel high-mobility 2D semiconductors, which were validated via first-principles electron-phonon coupling analysis, confirming their exceptional transport properties and stability. This work can potentially accelerate machine learning-assisted materials design and discovery with less data restriction.

cond-mat.mtrl-sci↗

arXiv2025

T2MAT (text-to-materials): A universal agent for generating material structures with goal properties from a single sentence

Artificial Intelligence-Generated Content (AIGC)-content autonomously produced by AI systems without human intervention-has significantly boosted efficiency across various fields. However, AIGC in material science faces challenges in efficiently discovering novel materials that surpass existing databases, while simultaneously addressing the invariance and stability of crystal structures. To address these challenges, we develop T2MAT (text-to-material), a comprehensive agent processing from a user-input sentence to inverse design material structures with goal properties beyond the existing database via globally exploring chemical space, followed by an entirely automated workflow of first-principles validation. Furthermore, we propose CGTNet (Crystal Graph Transformer NETwork), a graph neural network model that captures long-range interactions, to enhance the accuracy and data utilization efficiency of property prediction and thereby strengthen the reliability of inverse design. Through these contributions, T2MAT minimizes the dependency on human expertise and significantly improves the efficiency of discovering novel, high-performance functional materials, offering a robust way toward more autonomous materials design.

cond-mat.mtrl-sci↗

arXiv2025

LLM-Feynman: Leveraging Large Language Models for Universal Scientific Formula and Theory Discovery

Distilling underlying principles from data has historically driven scientific breakthroughs. However, conventional data-driven machine learning often produces complex models that lack interpretability and generalization due to insufficient domain expertise. Here, we present LLM-Feynman, a novel framework that leverages large language models (LLMs) alongside systematic optimization to derive concise, interpretable formulas from data and domain knowledge. Our method integrates automated feature engineering, LLM-guided symbolic regression with self-evaluation, and Monte Carlo tree search to enhance formula discovery and clarity. The embedding of domain knowledge simplifies the formula, while self-evaluation based on this knowledge further minimizes prediction errors, surpassing conventional symbolic regression in accuracy and interpretability. Our LLM-Feynman successfully rediscovered over 90% of fundamental physical formulas and demonstrated its efficacy in key materials science applications, including classification of two-dimensional material and perovskite synthesizability and determination of the Green's function and screened Coulomb interaction bandgaps, and prediction of ionic conductivity in lithium solid-state electrolytes. By transcending mere data fitting through the integration of deep domain knowledge, this LLM-Feynman offers a transformative paradigm for the automated discovery of generalizable scientific formulas and theories across disciplines.

cond-mat.mtrl-sci↗

arXiv2024

Is Large Language Model All You Need to Predict the Synthesizability and Precursors of Crystal Structures?

Accessing the synthesizability of crystal structures is pivotal for advancing the practical application of theoretical material structures designed by machine learning or high-throughput screening. However, a significant gap exists between the actual synthesizability and thermodynamic or kinetic stability, which is commonly used for screening theoretical structures for experiments. To address this, we develop the Crystal Synthesis Large Language Models (CSLLM) framework, which includes three LLMs for predicting the synthesizability, synthesis methods, and precursors. We create a comprehensive synthesizability dataset including 140,120 crystal structures and develop an efficient text representation method for crystal structures to fine-tune the LLMs. The Synthesizability LLM achieves a remarkable 98.6% accuracy, significantly outperforming traditional synthesizability screening based on thermodynamic and kinetic stability by 106.1% and 44.5%, respectively. The Methods LLM achieves a classification accuracy of 91.02%, and the Precursors LLM has an 80.2% success rate in predicting synthesis precursors. Furthermore, we develop a user-friendly graphical interface that enables automatic predictions of synthesizability and precursors from uploaded crystal structure files. Through these contributions, CSLLM bridges the gap between theoretical material design and experimental synthesis, paving the way for the rapid discovery of novel and synthesizable functional materials.

cond-mat.mtrl-sci↗

arXiv2024

Inverse Design of Promising Alloys for Electrocatalytic CO$_2$ Reduction via Generative Graph Neural Networks Combined with Bird Swarm Algorithm

Directly generating material structures with optimal properties is a long-standing goal in material design. One of the fundamental challenges lies in how to overcome the limitation of traditional generative models to efficiently explore the global chemical space rather than a small localized space. Herein, we develop a framework named MAGECS to address this dilemma, by integrating the bird swarm algorithm and supervised graph neural network to effectively navigate the generative model in the immense chemical space towards materials with target properties. As a demonstration, MAGECS is applied to design compelling alloy electrocatalysts for CO$_2$ reduction reaction (CO$_2$RR) and works extremely well. Specifically, the chemical space of CO$_2$RR is effectively explored, where over 250,000 promising structures with high activity have been generated and notably, the proportion of desired structures is 2.5-fold increased. Moreover, five predicted alloys, i.e., CuAl, AlPd, Sn$_2$Pd$_5$, Sn$_9$Pd$_7$, and CuAlSe$_2$ are successfully synthesized and characterized experimentally, two of which exhibit about 90% Faraday efficiency of CO$_2$RR, and CuAl achieved 76% efficiency for C$_2$ products. This pioneering application of inverse design in CO$_2$RR catalysis showcases the potential of MAGECS to dramatically accelerate the development of functional materials, paving the way for fully automated, artificial intelligence-driven material design.

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