Our group focuses on computational materials science using first-principles quantum mechanical simulations and machine learning to virtually design and discover new materials before experiments. These tools enable materials informatics approaches for exploring large datasets and predicting new compounds, similar to recommendation systems used by Netflix or Amazon.
We are particularly interested in materials for energy and sustainability, including hydrogen storage, batteries, lightweight alloys, fuel cells, and thermoelectrics, as well as topics such as phase transformations and microstructural evolution.
We also develop multi-scale methodologies that link atomistic simulations with Monte Carlo, phase-field, and CALPHAD approaches to overcome size and time-scale limitations, enabling predictive modeling of microstructure evolution and materials properties.
The members of the Wolverton Research Group come from many backgrounds including Materials Science, Physics, Chemistry, Mechanical Engineering, and Mathematics. We are open to students from all majors, and if you have a passion for discovering outstanding materials, you are always welcome to become part of our group’s family.
I am working on high-throughput screening of thermoelectric materials, Li-ion battery electrodes, and thin film solar cell materials. For specific candidates, I use DFT calculations combined with Boltzmann transport theory to further unravel the electrical and thermal transport mechanisms.
I also study the calculation of phase diagrams (CALPHAD) and defect chemistry, which gives insight into materials synthesis and modification.
Postdoctoral fellow Materials Science & Engineering, Northwestern University (present)
Ph. D. Materials Science & Engineering, Wuhan University of Technology (2022)
B.S. Materials Science & Engineering, Wuhan University of Technology (2016)
zhi.li@northwestern.edu
Theoretical study of Li-ion battery cathode materials
Predicting and screening Li-ion battery cathode materials
Postdoctoral fellow Materials Science and Engineering, Northwestern University (present)
Ph.D. Mechanical Engineering, Seoul National University (2023)
B.S. Naval Architecture and Ocean Engineering, Seoul National University (2017)
hyungjun.kim@northwestern.edu
Ab initio simulations on crystalline disordered materials, including multi-cation disordered rocksalt cathodes, high-entropy alloys, mixed-anion/cation perovskites, and complex transition metal oxides for energy applications.
Theoretical investigations of ground states and finite-temperature order/disorder in cluster-expansion descriptions, and accurate remediation of DFT overdelocalization in complex TM oxides using hybrid functionals and DFT+U.
Ph.D. Materials Science and Engineering, Northwestern University, Aug 2025
B.S. Materials Science and Engineering, National Taiwan University, Jan 2019Â Â
tzu-chenliu2024@u.northwestern.edu
High-Throughput and Machine Learning Studies of Inorganic Layered Structures for Critical Metal Ion Separation
 Postdoctoral fellow Materials Science and Engineering, Northwestern University (present)
Ph.D. Chemical Engineering, Ulsan National Institute of Science and Technology (2020)
B.S. Nanochemistry and Bioengineering, Ulsan National Institute of Science and Technology (2014)
woocheol.jeon@northwestern.edu
Machine learning, materials discovery, and high-throughput density functional theory (DFT)
Thermal transport, phonons, thermoelectrics
B.S. Materials Science & Engineering, University of Florida (2018)
Minor in Physics
dalegaines2023@u.northwestern.edu
Nanoparticles, interfaces
Predicting nanoparticle interface morphology
B.S. NanoEngineering, University of California San Diego
elodiesandraz2023@u.northwestern.edu
My current research involves using DFT and cluster expansions to study short-range order in multi-principal element alloys. This work is part of a MURI aimed at connecting short-range order to corrosion and oxidation behavior to enable longer-lasting structural materials.
B.S. Materials Science & Engineering, Johns Hopkins University (2015-2019)
nathansmith2024@u.northwestern.edu
Cathode materials, quantum chemistry calculations, catalysis, high throughput DFT calculations, machine learning
Databases and machine learning for materials design, synthesis, and discovery  - Benchmarking interfacial energy calculations
Predicting and screening materials for lithium-ion battery cathodes
Industrial experience (Designing chemical engineering processes and researching pulp mill processes), Chile - 2016 to 2020
M.S. in Chemical Engineering, University of Concepción, Chile - 2017
B.S. in Chemical Engineering, University of Concepción, Chile - 2015
Trekking in national parks, astrophotography, and juggling. Lately learning guitar/ukelele
asalgado@u.northwestern.edu
Theoretical study of the structure-property relationships of nanomaterials
Machine learning prediction for materials properties
M.S. School of Chemical Biological Engineering, Seoul National University (2021)
B.S. School of Chemical Biological Engineering, Seoul National University (2019)
dohunkang2026@u.northwestern.edu
High-Throughput Materials Discovery
Predicting and Screening Nanoparticle Megalibraries
B.S. Materials Science & Engineering, University of Michigan (2021)
jacobpietryga2026@u.northwestern.edu
Doping strategies to enhance thermoelectric performance
Thermal and electrical transportÂ
Chemical bonding in materials design
M.S. Physical Chemistry, Shahid Beheshti University (2022)
B.S. Chemistry, Shahid Beheshti University (2020)
shimashahabfar2027@u.northwestern.edu
Interfacial structures prediction using minima hopping method
Proton conductivity across grain boundary
M.S. Materials Science & Engineering, National Yang Ming Chiao Tung University (2021)
B.S. Materials Science & Engineering, National Chiao Tung University (2019)
chang-tichou2027@u.northwestern.edu
High-Throughput Materials DiscoveryÂ
Property - Structure relationships of nanomaterials
M.S Materials Science & Engineering, Imperial College London, UK (2022)
B.S Physics, King's College London, UK (2021)
yichenli2028@u.northwestern.edu
Phonons
High-throughput DFT calculations
Phase stability and phase diagrams
M.S. Chemical Engineering, University of California San Diego (2024)
B.S. Chemical Engineering, University of California San Diego (2023)
jiaqifeng2029@u.northwestern.edu
Characterization Method of Nanoparticles
AI for Materials Science
Computational Science (DFT and MD)
M.S. Mechanical Engineering, Soongsil University (2025)
B.S. Mechanical Engineering, Soongsil University (2023)
Weight training, Playing guitar, Drinking
JiwonSun2030@u.northwestern.edu
Composition-Structure relationships of Nanoparticles
MC simulations of nanoparticle phases
M.S. Physics, University of Liége, Belgium (2023)
M.S. Materials Science, Technical University Darmstadt, Germany (2023)
B.S. Physics, University of Groningen, Netherlands (2021)
Bouldering, running, hiking in nature, music
brunobanas2030@u.northwestern.edu
AI for Materials Science
Computational Science (DFT)
B.S. Materials Science and Engineering, University of Toronto (2024)
Basketball, Golf, Poker
chungshan2030@u.northwestern.edu
Undergraduates
Grace Lu
Julija Vinckeviciute
Michelle Chen
Scott Grindy
Jihye Park
Wenhao Sun
Sail Wu
Yang Yu
Kareem Youseff
Kyle Bushick
Roland G St. Michel
Colton Gerber
Alumni
Graduate Students
Tzu-chen Liu (2025)
Andrew Lee (2023)
Jiahong Shen (2023)
Sean Griesemer (2023)
Bianca Baldassarri (2023)
Shane Patel (2023)
Abhijith Gopakumar (2022)
Cheol Peter Park (2021)
Eric Schwenker (2021)
Xia Hua (2020)
Vinay I. Hegde (2019)
Mohan Liu (2019)
Zhenpeng Yao (2018)
Zhi Lu (2018)
Soo Kim (2017)
Antoine Emery (2017)
Kyoungdoc Kim (2017)
Logan Ward (2016)
David Snydacker (2016)
Muratahan Aykol (2015)
Jeff W. Doak (2015)
Ahmed Issa (2014)
Scott Kirklin (2014)
Yongli Wang (2014)
Alexander Thompson (2013)
Wei Chen (2012)
Bryce Meredig (2012)
PostDocs
Jiangang He
Yi Xia
Yizhou Zhu
Dilpuneet Aidhy
William Counts
Dongwon Shin