Yilei Wu

About me

I am a Postdoctoral Researcher at the University of Cambridge, working at the intersection of machine learning, quantum chemistry, and solid-state physics.

I work in the Department of Materials Science & Metallurgy at the University of Cambridge, hosted by Prof. Bartomeu Monserrat and co-hosted by Prof. Lixue Cheng from the Department of Chemistry at the Hong Kong University of Science and Technology.

My current research uses machine learning and first-principles calculations to accelerate the discovery of passivation molecules for perovskite solar cells.

I received my Ph.D. in Physics from Southeast University in 2023, co-supervised by Prof. Jinlan Wang and Prof. Ming-Gang Ju. My doctoral work developed data-driven approaches for the design, discovery, and synthesis of perovskite optoelectronic materials.

Research interests AI for materials · Perovskites · DFT & AIMD · Materials synthesis · Causal inference

Research

My research connects atomistic understanding with data-driven prediction. I am particularly interested in methods that do more than predict a property: they should reveal actionable chemical principles and guide the next experiment.

01

Machine learning-guided perovskite discovery

Accelerating the discovery and design of perovskite materials with targeted stability, optoelectronic, and photovoltaic properties.

02

Data-driven perovskite synthesis

Interpretable machine learning frameworks that integrate physicochemical knowledge, calculations, and laboratory synthesis.

03

Computational materials design

First-principles studies of low-dimensional, lead-free, and hybrid perovskites for photovoltaic and light-emitting applications.

Selected publications

Full list ↗

  1. Universal machine learning aided synthesis approach in typical laboratory: a case study of two-dimensional perovskites.

    Yilei Wu, Chang-Feng Wang, Ming-Gang Ju, et al.

    Nature Communications 15, 138 (2024). DOI

  2. How to accelerate inorganic materials synthesis: from computational guidelines to data-driven method?

    Yilei Wu, Xiaoyan Li, Rong Guo, Ruiqi Xu, Ming-Gang Ju, and Jinlan Wang.

    National Science Review 12, nwaf081 (2025). DOI

  3. Two-dimensional perovskites with tunable room-temperature phosphorescence.

    Yilei Wu, Shuaihua Lu, Qionghua Zhou, Ming-Gang Ju, Xiao Cheng Zeng, and Jinlan Wang.

    Advanced Functional Materials (2022). DOI

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

    Yilei Wu, Shuaihua Lu, Ming-Gang Ju, Qionghua Zhou, and Jinlan Wang.

    Nanoscale 13, 12250 (2021). DOI