arXiv subjects

Chongyi Ling

Publications and source records attributed to Chongyi Ling.

6 recordsLinked to original sources

arXiv2025

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↗

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

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.

cond-mat.mtrl-sci↗

arXiv2018

Defect Engineering for Modulating the Trap States in Two-dimensional Photoconductor

Defect induced trap states are essential in determining the performance of semiconductor photodetectors. The de-trap time of carriers from a deep trap could be prolonged by several orders of magnitude as compared to shallow trap, resulting in additional decay/response time of the device. Here, we demonstrate that the trap states in two-dimensional ReS2 could be efficiently modulated by defect engineering through molecule decoration. The deep traps that greatly prolong the response time could be mostly filled by Protoporphyrin (H2PP) molecules. At the same time, carrier recombination and shallow traps would in-turn play dominant roles in determining the decay time of the device, which can be several orders of magnitude faster than the as-prepared device. Moreover, the specific detectivity of the device is enhanced (as high as ~1.89 x 10^13 Jones) due to the significant reduction of dark current through charge transfer between ReS2 and molecules. Defect engineering of trap states therefore provides a solution to achieve photodetectors with both high responsivity and fast response.

cond-mat.mtrl-sci↗

arXiv2016

High Intrinsic Catalytic Activity of Two-Dimensional Boron Monolayers for Hydrogen Evolution Reaction

Two-dimensional (2D) boron monolayers have been successfully synthesized on silver substrate very recently. Their potential application is thus of great significance. In this work, we explore the possibility of boron monolayers (BMs) as electrocatalysts for hydrogen evolution reaction (HER) by first-principle method. Our calculations show that the BMs are active catalysts for HER with nearly zero free energy of hydrogen adsorption, metallic conductivity and plenty of active sites in the basal plane. The effect of the substrate on the HER activity is further assessed. It is found that the substrate has a positive effect on the HER performance caused by the competitive effect of mismatch strain and charge transfer. The indepth understanding of the structure dependent HER activity is also provided.

physics.chem-ph↗

arXiv2016

Repairing atomic vacancies in single-layer MoSe2 field-effect transistor and its defect dynamics

Here we repair the single-layer MoSe2 field-effect transistors by the EDTA processing, after which the devices' room-temperature carrier mobility increases from 0.1 to over 70cm2/Vs. The atomic dynamics is constructed by the combined study of the first-principle calculation, aberration-corrected transmission electron microscopy and Raman spectroscopy. Single/double Se vacancies are revealed originally, which cause some mid-gap impurity states and localize the device carriers. They are found repaired with the result of improved electronic transport. Such a picture is confirmed by a 1.5cm-1 red shift in the Raman spectra.

cond-mat.mes-hall↗