Zhilong Wang (汪志龙)
zhilongwang@cornell.edu
Office : Room 318, Olin Hall
Bio
I am an Eric and Wendy Schmidt AI in Science Postdoc Fellow at the Cornell University AI for Science Institute (CUAISci), Cornell University, under the supervision of Prof. Fengqi You and I am a member of the Process-Energy-Environmental Systems Engineering (PEESE) group. I received my Ph. D. in Electronic Science and Technology from the Artificial Intelligence and Micro Structure Laboratory (AIMS-Lab) at Shanghai Jiao Tong University (SJTU) in 2024, where I was supervised by Prof. Jinjin Li.
Research
My core research interest is AI for Materials Discovery and Design. I explore and develop AI models for learning, predicting, interpreting, and designing new materials with high performance, especially those for energy and electronic applications (e.g., batteries, photovoltaics, and catalysts) . Almost all of my research efforts are centered around this goal, touching on a diverse range of topics such as ensemble learning, graph neural networks, density functional theory, molecular dynamics, transfer learning, data mining, chemical interpretability, etc. A list of topics that I am interested in at the moment are:
- High-throughput theoretical calculations (DFT & MD) for materials discovery and design, etc.
- AI-driven modeling for materials, such as learning multi-scale representations, attention mechanisms, etc.
- Development of automated materials informatics tools and platforms.
The commonly used tools/techniques for molecular/materials simulation and AI modeling include:
- Materials Science: VASP, GAUSSIAN, ORCA, GROMACS, VESTA, ChemDraw, VASPKit, Materials Project, etc.
- AI-driven Modeling: Scikit-Learn, Crystal Graph Neural Nerwork, DeepChem, Transfer Learning, DeepMD, Matminer, etc.
News
- Oct. 2025: Our paper is published in JCIM 2025!
- Sep. 2025: I gave a talk on "Combining AI with First-principles Calculations to Accelerate the Discovery of Inorganic Crystalline Materials".
- May. 2025: Our generative model-based crystalline materials design was selected as the Cover of May 2025 issue of Nature Computational Science!
- Apr. 2025: I gave a talk on "AI-driven Discovery of High-performance Solid Electrolytes for Solid-state Batteries".
- Mar. 2025: Our paper is accepted for publication in Nature Computational Science 2025!
- Mar. 2025: Our paper is out in Joule 2025!
- Oct. 2024: Our paper is accepted for publication in ACS Nano 2024!
- Sep. 2024: I was selected as an Eric and Wendy Schmidt AI in Science Postdoc Fellow 2024!
- May. 2024: I gave a talk on "Predicting Materials Properties via Transfer Learning".
Academic Services
- Junior Editor Board: Journal of Materials Informatics
- Reviewer: Nature Communications, Science Advances, JACS Au, npj Computational Materials, Energy Storage Materials, Materials & Deisgn, Computational Materias Science, Applied Surface Science, Materials Today Communications, Journal of Physical Chemistry C, Sensors and actuators, Energy Materials, Discover Materials, Discover Applied Sciences, Brazilian Journal of Physics, etc.
Teaching
- ChemE-6820 AI for Materials（Spring 2025, Visiting Lecturer & TA）
Honors and Awards
- Eric and Wendy Schmidt AI in Science Postdoc Fellowship, 2025 - 2026
- Eric and Wendy Schmidt AI in Science Postdoc Fellowship, 2024 - 2025
- Outstanding Graduate, Ph. D degree, 2024
- 7th Forum of Materials Genome Engineering, Second Prize Poster Award, 2023
- National Scholarship for Ph.D student, 2023
- 14th International Conference on Computational Nanoscience and New Energy Materials (CNNEM), First Prize Poster Award, 2022
- National Scholarship for Ph.D student, 2022
- National Scholarship for Ph.D student, 2021
- Outstanding Graduate, B. S degree, 2019
- National Scholarship for Undergraduates, 2017
- China Undergraduate Mathematical Contest in Modeling (CUMCM), First Prize, 2017
Selected Publications
This is a selected list of my publications. For an up-to-date, complete list, please refer to my Google Scholar page.
(#) indicates equal contribution. (*) indicates the corresponding author.
Leveraging Generative Models with Periodicity-aware, Invertible and Invariant Representations for Crystalline Materials Design
This paper was selected as the Cover of May issue 2025 of Nature Computational Science!
Variable and Intelligent Catalyst Design based on Local Chemical Environments in Sulfur Redox Reactions
Steric Hindrance Effects of Linear/Branched Saccharides at Ceramic‐Modified Zinc Anodes Enable Ultrastable Aqueous Al‐Zn Hybrid Ion Batteries
Interpretable Surrogate Learning for Electronic Material Generation
This paper was selected as the Cover of Dec issue 2024 of ACS Nano!
MatGPT: A Vane of Materials Informatics from Past, Present, to Future
Synergistic Effect of Anodic Hydrophilic and Hydrophobic Interfaces for Long Cycle Life Aqueous Aluminum–Zinc Hybrid Ion Batteries
AlphaMat: A material Informatics Hub Connecting Data, Features, Models and Applications
IonML: A Physically Inspired Machine Learning Platform to Directed Design Superionic Conductors
This paper was selected as the Cover of May issue 2023 of Energy Storage Materials!
An End‐to‐end Artificial Intelligence Platform Enables Real‐time Assessment of Superionic Conductors
Unraveling the Anchoring Effect of MXene-supported Single Atoms as Cathodes for Aluminum–sulfur Batteries
DeepTMC: A Deep Learning Platform to Targeted Design Doped Transition Metal Compounds
An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites
Vision for Energy Material Design: A Roadmap for Integrated Data-driven Modeling
Computational Screening of Spinel Structure Cathodes for Li-ion Battery with Low Expansion and Rapid Ion Kinetics
Unsupervised Discovery of Thin-film Photovoltaic Materials from Unlabeled Data
Deep Learning for Ultra-fast and High Precision Screening of Energy Materials
Predicting Adsorption Ability of Adsorbents at Arbitrary Sites for Pollutants using Deep Transfer Learning
Accelerated Discovery of Stable Spinels in Energy Systems via Machine Learning
Harnessing Artificial Intelligence to Holistic Design and Identification for Solid Electrolytes
