Deyu Lu

Brookhaven National Laboratory

Center for Functional Nanomaterials
Bldg. 735, Room 1002
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-2089
dlu@bnl.gov

Expertise | Research | Education | Appointments | Publications

Expertise

• Electronic Structure Theory
• Many-body perturbation theory
• Structure, electronic and optical properties
• Computational X-ray absorption spectroscopy
• Machine learning in materials science

Research Activities

My research interest is to develop and apply of first-principle methods, including density functional theory and many-body perturbation theory, to study fundamental physical properties of materials. Current research topics include catalytic properties of 2D model zeolite, first-principles modeling of X-ray spectroscopy (e.g. XPS, XAS, and XES) in catalysis and battery materials, and understanding the structure-property relationship of materials with machine learning methods.

Education

• B.S., Physics: Tsinghua University, China,1997
• M.S., Physics: Chinese Academy of Sciences, 2000
• PhD, Physics: University of Illinois at Urbana-Champaign, 2000

Professional Appointments

Research and Professional Experience

• 2018 – present: Physicist with continuing appointment, Center for Functional Nanomaterials, Brookhaven National Laboratory
• 2015 – 2018: Physicist, Center for Functional Nanomaterials, Brookhaven National Laboratory
• 2012 – 2015: Associate Physicist, Center for Functional Nanomaterials, Brookhaven National Laboratory
• 2012 – present: Adjunct Professor, Materials Science and Engineering Dept., Stony Brook University
• 2010 – 2012: Assistant Physicist, Center for Functional Nanomaterials, Brookhaven National Laboratory
• 2006 – 2010: Postdoctoral Researcher, Department of Chemistry, University of California, Davis

Synergistic Activities

• 2023         Co-organizer of Workshop 9 “Curation, Data Analysis and Computational Modeling of Xray Absorption Spectroscopy” at the 2023 NSLS-II, CFN and LBMS User Meeting, Upton, NY
• 2022         Organizer of Workshop 2 "Electronic Structure of Nanomaterials: A Special Symposium in Honor of Dr. Mark Hybertsen" at the 2022 NSLS-II & CFN User Meeting, Upton, NY
• 2022         Co-organizer of Workshop 13 "Data-Driven Analysis, Characterization and Modeling in Battery Development and Manufacturing" at the 2022 NSLS-II & CFN User Meeting, Upton, NY
• 2021         Co-organizer of Workshop "Machine Learning Augmented X-Ray Scattering and Spectroscopies" at the 2021 NSLS-II & CFN User Meeting, Upton, NY

Selected Publications

• Meng F, Maurer B, Peschel F, et al (2024) Multicode benchmark on simulated Ti K-edge x-ray absorption spectra of Ti-O compounds. Physical Review Materials 8:. https://doi.org/10.1103/physrevmaterials.8.013801
• Carbone MR, Meng F, Vorwerk C, et al (2023) Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files. Journal of Open Source Software 8:5182. https://doi.org/10.21105/joss.05182
• Yilmaz T, Jiang X, Lu D, et al (2023) Dirac nodal arc in 1T-VSe2. Communications Materials 4:. https://doi.org/10.1038/s43246-023-00376-1
• Liang Z, Carbone MR, Chen W, et al (2023) Decoding structure-spectrum relationships with physically organized latent spaces. Physical Review Materials 7:. https://doi.org/10.1103/physrevmaterials.7.053802
• Yu H, Lu D, Wu Q, Wei T-C (2022) Geometric quantum adiabatic methods for quantum chemistry. Physical Review Research 4:. https://doi.org/10.1103/physrevresearch.4.033045
• Carbone MR, Topsakal M, Lu D, Yoo S (2020) Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy. Physical Review Letters. doi: 10.1103/physrevlett.124.156401
• Yan D, Topsakal M, Selcuk S, Lyons JL, Zhang W, Wu Q, Waluyo I, Stavitski E, Attenkofer K, Yoo S, Hybertsen MS, Lu D, Stacchiola DJ, Liu M (2019) Ultrathin Amorphous Titania on Nanowires: Optimization of Conformal Growth and Elucidation of Atomic-Scale Motifs. Nano Letters 19:3457–3463. doi: 10.1021/acs.nanolett.8b04888
• Carbone MR, Yoo S, Topsakal M, Lu D (2019) Classification of local chemical environments from x-ray absorption spectra using supervised machine learning. Physical Review Materials. doi: 10.1103/physrevmaterials.3.033604
• Zhang W, Topsakal M, Cama C, Pelliccione CJ, Zhao H, Ehrlich S, Wu L, Zhu Y, Frenkel AI, Takeuchi KJ, Takeuchi ES, Marschilok AC, Lu D, Wang F (2017) Multi-Stage Structural Transformations in Zero-Strain Lithium Titanate Unveiled by in Situ X-ray Absorption Fingerprints. Journal of the American Chemical Society 139:16591–16603. doi: 10.1021/jacs.7b07628
• Timoshenko J, Lu D, Lin Y, Frenkel AI (2017) Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles. The Journal of Physical Chemistry Letters 8:5091–5098. doi: 10.1021/acs.jpclett.7b02364