For an automatically updating list of citable work, please of course see my Google Scholar (as I only periodically update my site). I do host PDF's here when possible, though! You can find a copy of my dissertation here. * Indicates equally contributing authors.


  1. Y. Basdogan, D. R. Pollard, T. Shastry, , S. K. Kumar & Z.-G. Wang. Machine Learning-Guided Discovery of Polymer Membranes for CO2 Separation. chemrxiv-2023-5h4s7 (2023).
  2. H. Kwon, T. Hsu, W. Sun, W. Jeong, F. Aydin, J. Chapman, X. Chen, , D. Lu, F. Zhou & T. A. Pham. Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models. arXiv:2312.05472 (2023). [Under review: Machine Learning Science and Technology]


  1. C. Cao*, *, C. Komurcuoglu, J. S. Shekhawat, K. Sun, H. Guo, S. Liu, K. Chen, S.-M. Bak, Y. Du, C. Weiland, X. Tong, D. Steingart, S. Yoo, N. Artrith, A. Urban, D. Lu & F. Wang. Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes. Cell Reports Physical Science 5, 101909 (2024).
  2. , P. M. Maffettone, X. Qu, S. Yoo & D. Lu. Accurate, uncertainty-aware classification of molecular chemical motifs from multi-modal X-ray absorption spectroscopy. The Journal of Physical Chemistry A 128, 1948 (2024).
  3. T. Shastry, Y. Basdogan, Z.-G. Wang, S. K. Kumar & . Machine learning-based discovery of molecular descriptors that control polymer gas permeation. The Journal of Membrane Science 697, 122563 (2024).
  4. S. Goswami, K. Barros & . Physically interpretable approximations of many-body spectral functions. Physical Review E 109, 015302 (2024). [PDF]
  5. , H. J. Kim, C. Fernando, S. Yoo, D. Olds, H. Joress, B. DeCost, B. Ravel, Y. Zhang & P. M. Maffettone. Flexible formulation of value for experiment interpretation and design. Matter 7, 685 (2024). [PDF]


  1. *, S. Fomichev*, A. J. Millis, M. Berciu, D. R. Reichman & J. Sous. The Generalized Green's function Cluster Expansion: A Python package for simulating polarons. The Journal of Open Source Software 8, 5115 (2023). [GitHub]
  2. W. Chen, Y. Ren, A. Kagawa, , S. Y.-C. Chen, X. Qu, S. Yoo, A. Clyde, A. Ramanathan, R. L. Stevens, H. J. J. van Dam & D. Lu. Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats. arXiv.2308.01921 (2023). [Accepted: ICLMA 2023]
  3. H. Kwon, W. Sun, T. Hsu, W. Jeong, F. Aydin, S. Sharma, F. Meng, , X. Chen, D. Lu, L. F. Wan, M. H. Nielsen & T. A. Pham. Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon. The Journal of Physical Chemistry C 127, 16473 (2023).
  4. *, F. Meng*, C. Vorwerk, B. Maurer, F. Peschel, X. Qu, E. Stavitski, C. Draxl, J. Vinson & D. Lu. Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files. The Journal of Open Source Software 8, 5182 (2023). [GitHub]
  5. H. Guo*, *, C. Cao, J. Qu, Y. Du, S. Bak, C. Weiland, F. Wang, S. Yoo, N. Artrith, A. Urban & D. Lu. Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes. Scientific Data 10, 349 (2023).
  6. J. Lee, & W. Yin. Machine-learning the spectral function of a hole in a quantum antiferromagnet. Physical Review B 107, 205132 (2023). [PDF]
  7. Z. Liang, , W. Chen, F. Meng, E. Stavitski, D. Lu, M. S. Hybertsen & X. Qu. Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces. Physical Review Materials 7, 053802 (2023). [PDF]
  8. A. Ghose, M. Segal, F. Meng, Z. Liang, M. S. Hybertsen, X. Qu, E. Stavitski, S. Yoo, D. Lu & . Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles. Physical Review Research 5, 013180 (2023).


  1. P. M. Maffettone, D. B. Allan, S. I. Campbell, , T. A. Caswell, B. L. DeCost, D. Gavrilov, M. D. Hanwell, H. Joress, J. Lynch, B. Ravel, S. B. Wilkins, J. Wlodek & D. Olds. Self-driving Multimodal Studies at User Facilities. arXiv:2301.09177 (2023). [Presented at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)]
  2. . When not to use machine learning: A perspective on potential and limitations. MRS Bulletin 47, 968–974 (2022).
  3. & M. Baity-Jesi. Competition between Barrier- and Entropy-Driven Activation in Glasses. Physical Review E 106, 024603 (2022). [PDF]
  4. S. B. Torrisi, J. M. Gregoire, J. Yano, , C. P. Gomes, L. Hung & S. K. Suram. Accelerated Materials Discovery: How to Use Artificial Intelligence to Speed Up Development / Chapter 3: Artificial intelligence for materials spectroscopy. Berlin, Boston: De Gruyter (2022).


  1. C. Miles, , E. J. Sturm, D. Lu, A. Weichselbaum, K. Barros & R. M. Konik. Machine learning of Kondo physics using variational autoencoders and symbolic regression. Physical Review B 104, 235111 (2021). [PDF]
  2. , A. J. Millis, D. R. Reichman & J. Sous. Bond Peierls polaron: Moderate mass enhancement and current carrying ground state. Physical Review B 104, L140307 (2021). [PDF]
  3. , D. R. Reichman & J. Sous. Numerically Exact Generalized Green’s Function Cluster Expansions for Electron-Phonon Problems. Physical Review B 104, 035106 (2021). [PDF]
  4. E. J. Sturm*, *, D. Lu, A. Weichselbaum & R. M. Konik. Computing Anderson Impurity Model Spectra Using Machine Learning. Physical Review B 103, 245118 (2021). [PDF]


  1. S. B. Torrisi, , B. A. Rohr, J. H. Montoya, Y. Ha, J. Yano, S. K. Suram & L. Hung. Random Forest Machine Learning Models for Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships. npj Computational Materials 6, 109 (2020).
  2. , M. Z. Mayers & D. R. Reichman. Microscopic model of the doping dependence of linewidths in monolayer transition metal dichalcogenides. The Journal of Chemical Physics 152, 194705 (2020). [PDF]
  3. , V. Astuti & M. Baity-Jesi. Effective Trap-like Activated Dynamics in a Continuous Landscape. Physical Review E 101, 052304 (2020). [PDF]
  4. , M. Topsakal, D. Lu & S. Yoo. Machine-learning X-ray absorption spectra to quantitative accuracy. Physical Review Letters 124, 156401 (2020). [PDF]


  1. , S. Yoo, M. Topsakal & D. Lu. Classification of local chemical environments from x-ray absorption spectra using supervised machine learning. Physical Review Materials 3, 033604 (2019). [PDF]
  2. , G. A. Centola, A. Haas, K. P. McClelland, M. D. Moskowitz, A. M. Verderame, M. S. Olezeski, L. J. Papa, S. C. M. Dorn, W. W. Brennessel & D. J. Weix. Crystal structures of (RS)-N-[(1R,2S)-2-benzyloxy-1-(2,6-dimethylphenyl)propyl]-2-methylpropane-2-sulfinamide and (RS)-N-[(1S,2R)-2-benzyloxy-1-(2,4,6-trimethylphenyl)propyl]-2-methylpropane-2-sulfinamide: two related protected 1,2-amino alcohols. Acta Crystallographica E70, 365-369 (2014).