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 Society.

Research topics

• Computer Science
• Condensed matter physics
• Materials science
• Artificial Intelligence
• Physics
• Machine Learning
• Chemical physics
• Computational chemistry
• Chemistry
• Systems engineering
• Optics
• Thermodynamics
• Crystallography
• Engineering
• Quantum mechanics
• Data science
• Nanotechnology

Selected publications

• Strong acoustic phonon suppression leads to ultralow thermal conductivity and enhanced thermoelectric performance in BaCuGdTe$_3$

  ArXiv.org · 2025-07-08

  preprintOpen access

  Excitations and scatterings among the quantized lattice vibrations, i.e., phonons, govern the lattice thermal conductivity ($κ_l$) in crystalline solids. Therefore, effective modulation of $κ_l$ can be achieved through selective manipulation of phonon modes that strongly participate in the heat transport mechanisms. Here, combining accurate first-principles density functional theory calculations and Boltzmann transport theory, we report a layered quaternary chalcogenide semiconductor, BaCuGdTe$_3$, which exhibits unusually low $κ_l$ ($\sim$ 0.14 W/mK at room temperature) despite its ordered crystalline structure. Our analysis reveals that the ultralow $κ_l$ arises mainly from a strong suppression of acoustic phonon modes induced by local distortion, shear vibrations among the layers, and large acoustic-optical avoided-crossing between phonons, which collectively enhances the phonon-scattering rates. Further calculations of the electrical transport properties with explicit consideration of electron-phonon interactions reveal a high thermoelectric figure of merit exceeding unity for this compound at moderate temperature (400-700 K) and carrier concentration $(1\text{--}5 \times 10^{19}\ \text{cm}^{-3})$ ranges. Our theoretical predictions warrant experimental investigations of the intriguing phonon dynamics, thermal transport mechanisms, and thermoelectric properties in this compound. Moreover, insights from our analysis can be used to design and engineer compounds with ultralow $κ_l$.

  PublisherOA PDFDOI

• La ₃ CuTe ₅ : A Narrow-Gap Semiconductor with Indirect Gap and Dual-Regime Thermally Activated Transport

  Inorganic Chemistry · 2025-10-15 · 1 citations

  article

  Metal-chalcogenide systems remain a long-standing research topic because of their structural diversity and potential to host emergent phenomena. Here, we report a new compound, La3CuTe5, synthesized from the halide-flux method. Single-crystal X-ray diffraction studies indicate the structure to be unique among reported ones. The compound crystallizes in a novel structure type adopting the orthorhombic space group Pnma and a unit cell of a = 24.3947(14) Å, b = 4.4232(2) Å, and c = 10.2142(5) Å. The tetrahedral [CuTe4] building blocks form chains along [010] by corner sharing and link [LaTe7] and [LaTe8] polyhedra via edge sharing, resulting in a three-dimensional bulk structure. Thermal analysis results indicate that the material remains stable with a temperature up to 950 °C and decomposable at 1400 °C. First-principles calculations reveal an indirect electronic band gap and flat valence bands dominated by Te p and Cu d states. Optical absorption measurements yield a band gap of ∼0.65 eV, consistent with semiconducting behavior observed in transport measurements. Fittings to the temperature-dependent resistivity reveal two thermally activated regimes associated with Arrhenius-type conduction and three-dimensional variable range hopping, respectively.

  PublisherDOI

• Deterministic Control of Sn3+ Valence and Electronic Phase Evolution in AgSnSe2

  Research Square · 2025-12-08

  preprintOpen access
  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. Optimization has also benefited from AI's ability to enhance materials design and processes. The review will introduce fundamental AI and ML concepts, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning (RL), alongside advanced DL models such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), graph neural networks (GNNs), generative models, and Transformer-based models, which are critical for analyzing complex material data sets. It also covers core topics in materials informatics, including structure-property relationships, material descriptors, quantitative structure-property relationships (QSPR), and strategies for managing missing data and small data sets. Despite these advancements, challenges such as inconsistent data quality, limited model interpretability, and a lack of standardized data-sharing frameworks persist. Future efforts will focus on improving robustness, integrating causal reasoning and physics-informed AI, and leveraging multimodal models to enhance scalability and transparency, unlocking new opportunities for more advanced materials discovery, development, and optimization. Furthermore, the integration of quantum computing with AI will enable faster and more accurate results, and ethical frameworks will ensure responsible human-AI collaboration, addressing concerns of bias, transparency, and accountability in decision-making.

  PublisherDOI

• Dimensional Evolution Guides Property Control in the A_<i>n</i>Cu_4–<i>n</i>TiS₄ Semiconductor Series

  Journal of the American Chemical Society · 2025-06-13 · 3 citations

  article

  Through progressive reduction of the three-dimensional (3D) covalent network of Cu4TiS4, we isolate seven new members of the AnCu4–nTiS4 family (A = alkali metal; n = 0–4), spanning 3D, 2D, 1D, and 0D structural fragments. The dimensional reduction is rational, as it preserves the edge-sharing connectivity between [CuS4]7– and [TiS4]4– tetrahedra across the series. This structural evolution is driven by the stepwise substitution of Cu with alkali metals, guiding the formation of fragments with reduced dimensionality. The effects of “n” and “A” on the crystal structures, stabilities, electronic structures, and optoelectronic properties are profound, demonstrating that the manipulation of alkali metal size and AnCu4–nTiS4 stoichiometry enables predictable variations in structure and properties. For example, the n = 0 and n = 4 end members of the AnCu4–nTiS4 family set the range of achievable band gaps with 2.00 eV for Cu4TiS4, 2.60 eV for Na4TiS4, and intermediate values for the n = 1–3 members. Notably, CsCu3TiS4 exhibits exceptional air stability and congruent melting, with density functional theory (DFT) calculating moderate hole and electron effective masses in specific crystallographic directions (mh = 1.24m0, me = 0.87m0). Additionally, A3CuTiS4 (A = Na, K, Rb) displays direct band gap behavior and long photoluminescence lifetimes of 2.3–8.6 μs, and K3CuTiS4 has a PLQY of 5.19%. These findings underscore the potential of the AnCu4–nTiS4 family for applications in optoelectronics and demonstrate widely applicable design concepts that unveil rational stoichiometries within a given composition space to generate a series of crystal structures related through an evolving covalent dimensionality that corresponds to a predictable electronic structure and property progression.

  PublisherDOI

• Anomalous Reversal of Stability in Mo-containing Oxides: A Difficult Case Exhibiting Sensitivity to DFT+U and Distortion

  arXiv (Cornell University) · 2025-01-08

  preprintOpen accessSenior author

  Accurate predictions of the properties of transition metal oxides using density functional theory (DFT) calculations are essential for the computational design of energy materials. In this work, we investigate the anomalous reversal of the stability of structural distortions (where distorted structures go from being energetically favorable to sharply unfavorable relative to undistorted ones) induced by DFT+U on Mo d-orbitals in layered AMoO$_2$ (A = Li, Na, K) and rutile-like MoO$_2$. We highlight the significant impact of varying U$_{\text{eff}}$ values on the structural stability, convex hull, and thermodynamic stability predictions, noting that deviations can reach up to the order of 100 meV/atom across these energetic quantities. We find the transitions in stability are coincident with changes in the electron localization and magnetic behavior. The anomalous reversal persists across PBE, r$^2$SCAN functionals, and also with vdW-dispersion energy corrections (PBE+D3). In Mo-containing oxide systems, high U$_{\text{eff}}$ leads to inaccurate descriptions of physical quantities and structural relaxations under artificial symmetry constraints, as demonstrated by the phonon band structures, the Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional results, and comparisons with experimental structural data. We conclude that high U$_{\text{eff}}$ values (around 4 eV and above, depending on the specific structures and compositions) might be unsuitable for energetic predictions in A-Mo-O chemical spaces. Our results suggest that the common practice of applying DFT+U to convex hull constructions, especially with high U$_{\text{eff}}$ values derived from fittings, should be carefully evaluated to ensure that ground states are correctly reproduced, with careful consideration of dynamic stability and possible energetically favorable distortions.

  PublisherOA PDFDOI

• Anomalous reversal of stability in Mo-containing oxides: A difficult case exhibiting sensitivity to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>DFT</mml:mi><mml:mo>+</mml:mo><mml:mi>U</mml:mi></mml:mrow></mml:math> and distortion

  Physical Review Materials · 2025-05-07 · 2 citations

  articleSenior author

  Accurate predictions of the properties of transition-metal oxides using density functional theory (DFT) calculations are essential for the computational design of energy materials. In this work, we investigate the anomalous reversal of the stability of structural distortions (where distorted structures go from being energetically favorable to sharply unfavorable relative to undistorted ones) induced by $\mathrm{DFT}+{U}_{\text{eff}}$ on Mo d orbitals in layered $A{\mathrm{MoO}}_{2}$ $(A=\mathrm{Li}, \mathrm{Na}, \mathrm{K})$ and rutilelike ${\mathrm{MoO}}_{2}$. We highlight the significant impact of varying ${U}_{\text{eff}}$ values on the structural stability, convex hull, and thermodynamic stability predictions, noting that deviations can reach up to the order of 100 meV/atom across these energetic quantities. We find the transitions in stability are coincident with changes in the electron localization, magnetic behavior, and volume. The anomalous reversal persists across PBE, ${\mathrm{r}}^{2}\mathrm{SCAN}$ functionals, and also with vdW-dispersion energy corrections $(\mathrm{PBE}+\mathrm{D}3)$. In Mo-containing oxide systems, high ${U}_{\text{eff}}$ leads to inaccurate descriptions of physical quantities and structural relaxations under artificial symmetry constraints, as demonstrated by the phonon band structures, the Heyd-Scuseria-Ernzerhof hybrid functional results, and comparisons with experimental structural data. We conclude that high ${U}_{\text{eff}}$ values (around 4 eV and above, depending on the specific structures and compositions) might be unsuitable for energetic predictions in A-Mo-O chemical spaces. Our results suggest that the common practice of applying $\mathrm{DFT}+{U}_{\text{eff}}$ to convex hull constructions, especially with high ${U}_{\text{eff}}$ values derived from fittings, should be carefully evaluated to ensure that ground states are correctly reproduced, with careful consideration of dynamic stability and possible energetically favorable distortions.

  PublisherDOI

• Wide-ranging predictions of new stable compounds powered by recommendation engines

  Science Advances · 2025-01-03 · 8 citations

  articleOpen accessSenior authorCorresponding

  The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed. We conduct a systematic comparison of the performance of previously developed recommendation engines, specifically ones based on elemental substitution, data mining, and neural network prediction of formation enthalpy. After identifying ways to improve the recommendation engines, we find the neural network to be superior at recommending stable Heusler compounds. Armed with improved recommendation engines, we identify tens of thousands of compounds that are stable at zero temperature and pressure, now available in the Open Quantum Materials Database. We summarize this diverse pool of compounds, including the elusive mixed anion compounds, and two of their many applications: thermoelectricity and solar thermochemical fuel production.

  PublisherDOI

• Interpretable Nanoporous Materials Design with Symmetry-Aware Networks

  ArXiv.org · 2025-09-19

  preprintOpen access

  Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design. Machine learning offers a compelling pathway to accelerate the exploration, but existing models lack either interpretability or fidelity for elucidating the correlation between crystal geometry and property. Here, we report a three-dimensional periodic space sampling method that decomposes large nanoporous structures into local geometrical sites for combined property prediction and site-wise contribution quantification. Trained with a constructed database and retrieved datasets, our model achieves state-of-the-art accuracy and data efficiency for property prediction on gas storage, separation, and electrical conduction. Meanwhile, this approach enables the interpretation of the prediction and allows for accurate identification of significant local sites for targeted properties. Through identifying transferable high-performance sites across diverse nanoporous frameworks, our model paves the way for interpretable, symmetry-aware nanoporous materials design, which is extensible to other materials, like molecular crystals and beyond.

  PublisherOA PDFDOI

• A stoichiometrically conserved homologous series with infinite structural diversity

  Science · 2025-12-04 · 2 citations

  article

  We describe a compositionally guided structural evolution within a stoichiometrically conserved framework, BaSbQ 3 (Q = Te 1− x S x ), where each value of x gives rise to a distinct phase. The fundamental building blocks, A 1 (BaSbSTe 2 ) and B n (Ba n Sb n S n −1 Te 2 n +1 ), were composed of modular double rocksalt slabs stacked with functional polytelluride zigzag chains, with each phase differing only in the size and assembly of these blocks. Ten compounds were synthesized that maintained a coherent chemical identity that arose from this isovalent, isoelectronic substitution of Te and S. We envision that the phase formation at a molecular level unfolds in stages of extension, termination, and assembly and propose a design concept of “anionic disparity,” where phase homologies and polytelluride hierarchical networks can be controlled by leveraging differences in anion electron affinity and sizes.

  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

• Collaborative Research: Computational Thermochemistry of Compounds

  NSF · $248k · 2013–2017

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