Research

Atomic resolution tomography

Measuring 3D atomic coordinates in materials has been a driving force behind many scientific and technological advances. Techniques such as X-ray crystallography are powerful tools to measure average atomic positions, but cannot identify individual, atomic scale defects which can strongly influence a material’s behavior, particularly in the case of nanomaterials. A single image from a high resolution electron microscope can measure crystal lattices, defects, and strain, but only in two dimensions. Extending atomic resolution electron microscopy to three dimensions via tilt axis tomography enables one to determine the 3D atomic structure of a material without averaging or using a priori information. This method has been used to isolate crystalline grains in 3D, to visualize the atomic arrangement of atoms in defects, and to localize individual atoms in a sample with 20 pm precision. From the measured atomic coordinates, 3D dislocation and strain fields can be determined and related to the surface conditions of the sample. In samples with more than one atomic species, chemical ordering can be mapped in 3D with single atom sensitivity. In this way, atomic electron tomography characterizes nanomaterials in unprecedented detail to connect local atomic structure to functionality.

Selected publications:

• Lee, J., Song, S.W., Cho, M.G. et al. PhaseT3M: 3D imaging at 1.6 Å resolution via electron cryo-tomography with nonlinear phase retrieval. Nat Commun 17, 690 (2026). https://doi.org/10.1038/s41467-025-67303-5
• Pelz, P.M., Griffin, S.M., Stonemeyer, S. et al. Solving complex nanostructures with ptychographic atomic electron tomography. Nat Commun 14, 7906 (2023). https://doi.org/10.1038/s41467-023-43634-z

Machine learning and big data

Old Edisonian methods of materials discovery are quickly being replaced by high throughput screening and synthesis. However, characterization techniques have lagged behind. In order to fully incorporate electron microscopy into the process of high throughput materials design, feedback must be given on the same timescale as screening and synthesis techniques. This project seeks to close that gap by providing an automated workflow to understanding local atomic structures. This will not only provide invaluable information for improving high throughput screening and synthesis, but will also help illuminate the role of heterogeneity in these materials. By leveraging advances in machine learning we can provide statistical information on structural properties such as shape and defect content.

Selected publications:

• ACS Nano 2024, 18, 43, 29736–29747. https://doi.org/10.1021/acsnano.4c09312
• Rangel DaCosta, L., Sytwu, K., Groschner, C.K. et al. A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM). npj Comput Mater 10, 165 (2024). https://doi.org/10.1038/s41524-024-01336-0

Characterization of energy materials

New methods of energy production and storage are critical for meeting the technological needs of the future. Unfortunately, the synthesis of new materials for energy applications often outpaces the scientific understanding of their function. For example, solid-state batteries (SSBs) could potentially improve energy density and safety over batteries with liquid electrolytes, but when the SSB stack is assembled, its components often interact in unexpected and deleterious ways at the solid electrolyte interface (SEI). Materials characterization is essential for understanding these phenomena and informing the design of next-generation systems with improved performance and longevity.

Microscopic heterogeneities and other local-scale nuances can often be obscured by the “broad-brush” nature of many characterization techniques. In this context, electron microscopy has the key advantage of delivering targeted, spatially-resolved information on the smallest of length scales through techniques like scanning nanodiffraction and STEM-EELS. Many candidate materials for batteries and solar cells are air-sensitive and degrade quickly under the electron beam, rendering high resolution characterization difficult. We therefore emphasize fast electron detection, and a broad suite of experimental techniques such as air-free transfer and cryogenic measurements, to protect samples from beam damage and environmental degradation.

Selected publications:

• Jianli Cheng, Xinxing Peng, Ya-Qian Zhang, Yaosen Tian, Tofunmi Ogunfunmi, Andrew Z. Haddad, Andrew Dopilka, Gerbrand Ceder, Kristin A. Persson, and Mary C. Scott. Chemistry of Materials 2024 36 (6), 2642-2651. https://doi.org/10.1021/acs.chemmater.3c02351
• M. S. Diallo, T. Ogunfunmi, X. Yang, S. T. Oyakhire, V. S. Avvaru, M. C. Scott, Q. H. Tu, G. Ceder, Mitigating Battery Cell Failure: Role of Ag-Nanoparticle Fillers in Solid Electrolyte Dendrite Suppression. Adv. Energy Mater.2025, 15, 2405700. https://doi.org/10.1002/aenm.202405700
• Mengyu Gao, Yoonjae Park, Jianbo Jin, Peng-Cheng Chen, Hannah Devyldere, Yao Yang, Chengyu Song, Zhenni Lin, Qiuchen Zhao, Martin Siron, Mary C. Scott, David T. Limmer, and Peidong Yang. Journal of the American Chemical Society 2023 145 (8), 4800-4807. https://doi.org/10.1021/jacs.2c1371

Amorphous materials

Unlike crystalline systems, amorphous materials lack long range order. This makes their structure a mystery – although average quantities can be determined, the local structure of a disordered material has never been directly measured. This project uses advanced TEM measurements to determine local, nanometer-range order and symmetries in metallic glasses and relate them their structure.

Selected publications:

• Y.Zhu, E. R.Kennedy, B.Yasar, H.Paik, Y.Zhang, Z. D.Hood, M.Scott, J. L.Rupp, Uncovering the Network Modifier for Highly Disordered Amorphous Li-Garnet Glass-Ceramics. Adv. Mater.2024, 36, 2302438. https://doi.org/10.1002/adma.202302438
• Kennedy E, Hollingworth E, Ceballos A, O’Mahoney D, Ophus C, Hellman F, Scott M. Exploring Structural Anisotropy in Amorphous Tb-Co via Changes in Medium-Range Ordering. Microsc. Microanal. 2025 Feb 17;31(1):ozae113.