Research

The Materials AI Lab develops physics-informed machine learning and generative models to accelerate materials discovery and understand complex structure–property relationships at multiple scales.

Materials Characterization

Current Challenges:

Amorphous carbon (a-C) is widely used in electronics, energy storage, and coatings. However, its non-crystalline structure makes accurate characterization difficult.

Our Approach:

We apply machine learning to predict a-C structures from spectroscopy data, uncovering new structure-spectrum relationships. We are also developing generative models to design materials with targeted properties.

a-C Inverse Design Research Figure a-C Research Figure

Key Publications:

  1. Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon
    H Kwon et al.
    J. Phys. Chem. C 127 (33), 16473–16484
    Read the paper

  2. Spectroscopy-Guided Discovery of 3D Structures of Disordered Materials with Diffusion Models
    H Kwon1, T Hsu1 et al.
    Machine Learning: Science & Technology 5, 045037
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Porous and Nanostructured Materials

Proton Transfer in Nanoporous TiO₂

We use machine learning potentials to study proton transfer in TiO₂ nanopores, revealing confinement effects and enhancing photocatalytic design.

TiO2 Research Figure

Hydrogen Storage in MOFs

Using DFT, we study hydrogen adsorption thermodynamics in MOFs to guide the design of improved energy storage materials.

Key Publications:

  1. Confinement Effects on Proton Transfer in TiO₂ Nanopores
    H Kwon et al.
    ACS Applied Materials & Interfaces
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    DOE News

  2. Tuning Metal–Dihydrogen Interaction in MOFs for Hydrogen Storage
    H Kwon, D Jiang
    J. Phys. Chem. Lett. 13 (39), 9129–9133
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Environmental Remediation

PFAS Degradation and Toxicity Prediction

We use machine learning to understand and mitigate persistent pollutants like PFAS, predicting both bioactivity and degradability.

PFAS Research Figure Cover Art Figure

Key Publications:

  1. Harnessing Semi-supervised ML to Predict PFAS Bioactivities
    H Kwon, ZA Ali, BM Wong
    Environ. Sci. Technol. Lett. 10 (11), 1017–1022
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    Best paper award

  2. Degradation of Perfluoroalkyl Ether Carboxylic Acids with Hydrated Electrons
    MJ Bentel, Y Yu, H Kwon, et al.
    Environ. Sci. Technol. 54 (4), 2489–2499
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  3. ML Prediction of PFAS Defluorination for Efficient Treatment
    A Raza, H Kwon, et al.
    Environ. Sci. Technol. Lett. 6 (10), 624–629
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