Chris Wolverton

About

Chris Wolverton is the Frank C. Engelhart Professor of Materials Science and Engineering at Northwestern University. His research is centered on computational materials science, specifically utilizing first-principles quantum mechanical simulation tools. These computational methods enable the virtual synthesis of materials and the prediction of their properties prior to laboratory synthesis. Wolverton's work also involves the development of materials informatics, where machine learning tools are used to explore materials datasets and discover new materials, akin to recommendation systems used by Netflix and Amazon. His research focuses on materials for alternative energies and sustainability, including hydrogen storage, batteries, light-weight metals, fuel cells, and thermoelectrics. Key topics include the discovery of novel hydrogen storage materials, phase transformations in metallic and ceramic alloys, microstructural evolution during aging, and the theoretical prediction of new materials. Wolverton also works on methodologies that link atomistic and microstructural length scales, combining first-principles methods with Monte Carlo simulations, phase-field models, and CALPHAD calculations to produce predictive models of microstructural evolution and mechanical properties in new materials. His contributions have been recognized through various awards, including the Ford Motor Company Technical Achievement Award and the Noah Greenberg Award from the American Musicological…

Research topics

• Computer Science
• Condensed matter physics
• Materials science
• Artificial Intelligence
• Physics
• Machine Learning
• Chemical physics
• Computational chemistry
• Chemistry
• Systems engineering

Selected publications

• Recent advances and applications of deep learning methods in materials science

  npj Computational Materials · 2022 · 1031 citations

  Senior authorCorresponding

  Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data…

  PublisherOA PDFDOI

• Electronic-structure methods for materials design

  Nature Materials · 2021 · 249 citations

  Senior authorCorresponding

  The accuracy and efficiency of electronic-structure methods to understand, predict and design the properties of materials has driven a new paradigm in research. Simulations can greatly accelerate the identification, characterization and optimization of materials, with this acceleration driven by continuous progress in theory, algorithms and hardware, and by adaptation of concepts and tools from computer science. Nevertheless, the capability to identify and characterize materials relies on the pr…

  DOI

• Particlelike Phonon Propagation Dominates Ultralow Lattice Thermal Conductivity in Crystalline <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>Tl</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>VSe</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>

  Physical Review Letters · 2020 · 200 citations

  Senior authorCorresponding

  We investigate the microscopic mechanisms of ultralow lattice thermal conductivity (κ_{l}) in Tl_{3}VSe_{4} by combining a first principles density functional theory based framework of anharmonic lattice dynamics with the Peierls-Boltzmann transport equation for phonons. We include contributions of the three- and four-phonon scattering processes to the phonon lifetimes as well as the temperature dependent anharmonic renormalization of phonon energies arising from an unusually strong quartic anha…

  PublisherOA PDFDOI

• Microscopic Mechanisms of Glasslike Lattice Thermal Transport in Cubic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>Cu</mml:mi></mml:mrow><mml:mrow><mml:mn>12</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:msub><mml:mrow><mml:mi>Sb</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant="normal">S</mml:mi></mml:mrow><mml:mrow><mml:mn>13</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> Tetrahedrites

  Physical Review Letters · 2020 · 144 citations

  Senior authorCorresponding

  Materials based on cubic tetrahedrites (Cu_{12}Sb_{4}S_{13}) are useful thermoelectrics with unusual thermal and electrical transport properties, such as very low and nearly temperature-independent lattice thermal conductivity (κ_{L}). We explain the microscopic origin of the glasslike κ_{L} in Cu_{12}Sb_{4}S_{13} by explicitly treating anharmonicity up to quartic terms for both phonon energies and phonon scattering rates. We show that the strongly unstable phonon modes associated with trigonall…

  PublisherOA PDFDOI

• Artificial Intelligence for Materials Discovery, Development, and Optimization

  ACS Nano · 2025-07-25 · 91 citations

  review

  This review highlights the recent transformative impact of artificial intelligence (AI), machine learning (ML), and deep learning (DL) on materials science, emphasizing their applications in materials discovery, development, and optimization. AI-driven methods have revolutionized materials discovery through structure generation, property prediction, high-throughput (HT) screening, and computational design while advancing development with improved characterization and autonomous experimentation.…

  PublisherDOI

Recent grants

• Collaborative Research: Integrated Measurement and Predictive Modeling of Adsorbate Coverage and Compositional Effects on Catalytic Activity

  NSF · $198k · 2013–2017

• Collaborative Research: Predictive Modeling of Catalysis with Multiple Adsorbate Species

  NSF · $300k · 2007–2012

• Collaborative Research: First-Principles Engineering of Nanoscale Kinetics in Advanced Hydrides

  NSF · $150k · 2007–2011

Frequent coauthors

• Logan Ward

  Argonne National Laboratory

  505 shared

• Sean D. Griesemer

  Northwestern University

  501 shared

• Mercouri G. Kanatzidis

  Northwestern University

  170 shared

• Shiqiang Hao

  National Energy Technology Laboratory

  111 shared

• Vinayak P. Dravid

  Northwestern University

  88 shared

• Vidvuds Ozoliņš

  Yale University

  60 shared

• Yi Xia

  58 shared

• Jiahong Shen

  Northwestern University

  56 shared

Awards & honors

• Ford Motor Company Technical Achievement Award, 2006
• USCAR Recognition Award, 2006
• Noah Greenberg Award, American Musicological Society, 2006
• Ford Motor Company Patent Award, 2005
• Ford Motor Company Publication Award, 2005

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