Our group has been developing machine learning -based methods for characterizing functional materials . The main idea is based on the concept of “descriptors” of the material’s structure and/or electronic properties that are encoded in its spectrum. Based on that initial approach, the following publications summarize its extension to multiple systems and processes:

Patent:
System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra. A. I. Frenkel and J. Timosenko.
US Patent 11193884

Using XANES for decoding the coordination numbers of monometallic nanoparticles:

1. J. Timoshenko, D. Lu, Y. Lin, A. I. Frenkel
   Supervised machine learning-based determination of three-dimensional structure of metallic nanoparticles
   J. Phys. Chem. Lett., 8, 5091-5098 (2017)
2. J. Timoshenko, A. I. Frenkel
   “Inverting” X-ray Absorption Spectra of catalysts by machine learning in search for activity descriptors
   ACS Catalysis (Perspective) 9, 10192-10211 (2019)
3. J. Timoshenko, S. Roese, H. Hovel, A. I. Frenkel
   Silver clusters shape determination from in-situ XANES data
   Radiat. Phys. Chem. 175, 108049 (2020)
4. E. T. Dias, S. K. Gill, Y. Liu, P. Halstenberg, S. Dai,  J. Huang, J. Mausz, R. Gakhar, W. C. Phillips, S. Mahurin, S. M. Pimblott,  J. F. Wishart, A. I. Frenkel
   Radiation-assisted formation of metal nanoparticles in molten salts
   J. Phys. Chem. Lett. 12, 157-164 (2021)

Using XANES for decoding the coordination numbers of bimetallic nanoparticles:

1. N. Marcella, Y. Liu, J. Timoshenko, E. Guan, M. Luneau, T. Shirman, A. M. Plonka, J. E. S. v. der Hoeven, J. Aizenberg, C. M. Friend, A. I. Frenkel
   Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy
   Phys. Chem. Chem. Phys. 22, 18902-18910 (2020)
2. N. Marcella, J. S. Lim, A. M. Plonka, G. Yan, C. J. Owen, J. E. S. van der Hoeven, A. C. Foucher, H. T. Ngan, S. B. Torrisi, N. S. Marinkovic, E. A. Stach, J. F. Weaver, J. Aizenberg, P. Sautet, B. Kozinsky, A. I. Frenkel
   Decoding reactive structures in dilute alloy catalysts
   Nature Commun. 13, 832 (2022)

Using XANES for decoding the coordination numbers of mono- and bimetallic clusters:

1. J. Timoshenko, A. Halder, B. Yang, S. Seifert, M. Pellin, S. Vajda, A. I. Frenkel
   Subnanometer substructures in nanoassemblies formed from clusters under a reactive atmosphere revealed using machine learning
   J. Phys. Chem. C 122, 21686-21693 (2018)
2. Y. Liu, A. Halder, S. Seifert, N. Marcella, S. Vajda, A. I. Frenkel
   Probing active sites in Cu_xPd_y cluster catalysts by machine – learning – assisted X-ray absorption spectroscopy
   ACS Appl. Mater. Interf. 13, 53363-53374 (2021)

Using XANES for extracting the structure of metal oxide clusters:

1. Y. Liu, N. Marcella, J. Timoshenko, A. Halder, B. Yang, L. Kolipaka, M. J. Pellin, S. Seifert, S. Vajda, P. Liu, A. I. Frenkel
   Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning
   J. Chem. Phys. 151, 164201 (2019) 

Using XANES for decoding the structure of “single-atom” catalysts:

1. S. Xiang, P. Huang, J. Li, Y. Liu, N. Marcella, P. K. Routh, G. Li, A. I. Frenkel
   Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis
   Phys. Chem. Chem. Phys. 24, 5116-5124 (2022)

Using XANES for decoding the structure of bimetallic nanoparticles with elements that are neighbors in periodic table:

1. M. Xie, R. Shimogawa, Y. Liu, L. Zhang, A. C. Foucher, P. K. Routh, E. A. Stach, A. I. Frenkel, M. R. Knecht
   Biomimetic control over bimetallic nanoparticle structure and activity via peptide capping ligand sequence
   ACS Nano 18, 3286-3294 (2024)
2. Y. Liu, M. Xie, N. Marcella, A. C. Foucher, E. A. Stach, M. R. Knecht, A. I. Frenkel
   Z-contrast enhancement in Au-Pt nanocatalysts by correlative X-ray absorption spectroscopy and electron microscopy: Implications for composition determination
   ACS Appl. Nano Mater. 5, 8775-8782 (2022)

Using EXAFS for extracting partial radial distribution function from bulk materials (bcc-fcc transition in Fe):

1. J. Timoshenko, A. Anspoks, A. Cintins, A. Kuzmin, J. Purans, A. I. Frenkel
   Neural network approach for characterizing structural transformations by X-ray absorption fine structure spectroscopy
   Phys. Rev. Lett. 120, 225502 (2018)

Using EXAFS for extracting partial radial distribution function from mono- and bimetallic nanoparticles:

1. J. Timoshenko, C. J. Wrasman, M. Luneau, T. Shirman, M. Cargnello, S.  R. Bare, J. Aizenberg, C. M. Friend, A. I. Frenkel
   Probing atomic distributions in mono- and bimetallic nanoparticles by supervised machine learning
   Nano Letters 19, 520-529 (2019)

Using EXAFS for extracting partial radial distribution function from metal complexes in molten salts:

1. N. Marcella, S. Lam, V. Bryantsev, S. Roy, A. I. Frenkel
   Neural network based analysis of multimodal bond distributions using their extended X-ray absorption fine structure spectra
   Phys. Rev. B 109, 104201 (2024)
2. K. Zheng, N. Marcella, A. L. Smith, A. I. Frenkel
   Decoding the pair distribution function of uranium in molten fluoride salts from X-ray absorption spectroscopy data by machine learning
   J. Phys. Chem. C 128, 7635-7642 (2024)

Using latent space of autoencoders for analysis of information content in X-ray absorption spectra:

1. P. K. Routh, Y. Liu, N. Marcella, B. Kozinsky, A. I. Frenkel
   Latent representation learning for structural characterization of catalysts
   J. Phys. Chem Lett. (Perspective) 12, 2086-2094 (2021)

Using XANES for performing speciation of heterogeneous mixtures of metal species:

1. P. K. Routh, N. Marcella, A. I. Frenkel
   Speciation of nanocatalysts by X-ray absorption spectroscopy assisted by machine learning
   J. Phys. Chem. C (Perspective) 127, 5653-5662 (2023)