周跫桦 (Qionghua Zhou)

southeast university - china副教授

H指数:34

被引数:6,256

个人简介

"周跫桦，副教授/博士，博士生导师。2017年6月博士毕业于东南大学物理学院，主要从事数据驱动的材料设计研究。"

教育背景

2017.06
东南大学物理学院博士

工作经历

暂无内容

荣誉奖项

暂无内容

Continuous discovery of novel 2D materials via dual active learning-driven generative models

通过双主动学习驱动的生成模型持续发现新型二维材料

National Science Review

IF17.1

2026-02-120

Boosting Crystalline Property Prediction through Dynamical Feature Updating and Wavelet-Denoised Features: A New Deep Learning Model

通过动态特征更新和小波去噪特征提升晶体性质预测：一种新的深度学习模型

Journal of Physical Chemistry Letters

IF4.6

2026-02-010

PRE

Extending the Schottky-Mott Rule to Atomic Thickness: Descriptors for Two-Dimensional Semiconductor-Metal Contacts

NANO LETTERS

IF9.1

2025-12-010

PRE

Two-Dimensional Semiconductor-Metal Contact Engineering: Challenges and Strategies for High-Performance Electronics

二维半导体-金属接触工程: 高性能电子的挑战和策略

ADVANCED FUNCTIONAL MATERIALS

IF19

2025-12-010

PRE

A Descriptor-Driven Thermodynamic Framework for Achieving Unidirectional Nucleation in 2D Material Epitaxy

一种基于描述符的热力学框架，用于实现二维材料外延生长中的单向形核

ACS Nano

IF16

2025-10-260

PRE

Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models

通过大型语言模型准确预测3D晶体结构的可合成性和前体

Nature Communications

IF15.7

2025-07-150

OAAI

Machine Learning Assisted Material Discovery: A Small Data Approach

机器学习辅助材料发现：一种小数据方法

Accounts of Materials Research

IF14.7

2025-05-230

PRE

Machine Learning Assisted Design of Type-II Two-Dimensional Heterostructures for Photocatalytic Water Splitting

机器学习辅助设计用于光催化水分解的II型二维异质结构

ACS MATERIALS LETTERS

IF8.7

2025-02-090

PRE

From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning (vol 15, 5391, 2024)

NATURE COMMUNICATIONS

IF15.7

2024-07-310

OAAI

From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning

通过对抗性迁移学习从体有效质量到二维载流子迁移率的准确预测

NATURE COMMUNICATIONS

IF15.7

2024-06-254

OAAI

Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory

典型实验室中二维钙钛矿的通用机器学习辅助合成方法

NATURE COMMUNICATIONS

IF15.7

2024-01-0213

OAAI

Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression

MATERIALS HORIZONS

IF10.7

2023-01-019

PRE

Accurate property prediction with interpretable machine learning model for small datasets via transformed atom vector

PHYSICAL REVIEW MATERIALS

IF3.4

2022-12-218

PRE

Two-Dimensional Perovskites with Tunable Room-Temperature Phosphorescence

具有可调室温磷光的二维钙钛矿

ADVANCED FUNCTIONAL MATERIALS

IF19

2022-07-2124

OAAI

Inverse design with deep generative models: next step in materials discovery

使用深度生成模型的逆向设计: 材料发现的下一步

NATIONAL SCIENCE REVIEW

IF17.1

2022-06-119

OAAI

Accurate energy prediction of large-scale defective two-dimensional materials via deep learning

APPLIED PHYSICS LETTERS

IF3.6

2022-05-265

PRE

Accelerated Discovery of Single-Atom Catalysts for Nitrogen Fixation via Machine Learning

通过机器学习加速发现用于固氮的单原子催化剂

ENERGY & ENVIRONMENTAL MATERIALS

IF14.1

2022-04-0839

OAAI

On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high Curie temperature

CHEM

IF19.6

2022-03-0152

OAAI

High-performing nanocatalyst screened via machine learning and confirmed experimentally

CHEM CATALYSIS

IF11.6

2021-09-010

OAAI

Accelerated design of promising mixed lead-free double halide organic-inorganic perovskites for photovoltaics using machine learning

NANOSCALE

IF5.1

2021-01-0133

PREAI

周跫桦 (Qionghua Zhou)

个人简介

教育背景

工作经历

荣誉奖项

研究方向