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Xiaohui Qu

Theory/Computation, Center for Functional Nanomaterials

Brookhaven National Laboratory

Center for Functional Nanomaterials
Bldg. 735, Room 1005
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-4350
xiaqu@bnl.gov

Dr. Xiaohui Qu is a Staff Scientist at CFN. He works in the Theory and Computation Group focusing on research, development, and implementation of data analytics techniques. Dr. Qu is a computational chemist by training, with strong accomplishments in specific areas of chemistry, energy storage, machine learning, and X-ray absorption spectroscopy. He combines materials science research with extensive experience in deep learning techniques and a data-centric approach.

Expertise

Deep learning/Data analytics
Reaction mechanism
Density functional theory
X-ray absorption spectroscopy

Research Activities

We are developing intelligent data pipelines to confront the challenges in large-scale data collection and analysis for state-of-the-art facilities at CFN. We leverage physical theory and in-situ and operando experimental facilities. In particular, we exploit emerging tools in deep learning, image recognition and artificial intelligence to solve the frontier problems in nanoscience and nanotechnology.

Education

Shandong University (China) B.S. 2004 Environmental Engineering
Shandong University (China) Ph.D. 2009 Environmental Science
Nanyang Technological University (Singapore) Postdoc 2011 Physics
Universidade Nova de Lisboa (Portugal) Postdoc 2013 Chemistry
Lawrence Berkeley National Lab Postdoc 2015 Materials

Professional Appointments

2021 –present Research Staff 4 Center for Functional Nanomaterials, Brookhaven National Laboratory
2019 – 2021 Assistant Scientist Center for Functional Nanomaterials, Brookhaven National Laboratory
2015 – 2018 Assistant Project Scientist Department of Materials Science and Engineering, UC Berkeley

Selected Publications

Tayal A, Barai P, Zhong H, et al (2024) In Situ Insights into Cathode Calcination for Predictive Synthesis: Kinetic Crystallization of LiNiO2 from Hydroxides. Advanced Materials. https://doi.org/10.1002/adma.202312027
Mun J, Sushko PV, Brass E, et al (2023) Probing Oxidation-Driven Amorphized Surfaces in a Ta(110) Film for Superconducting Qubit. ACS Nano 18:1126–1136. https://doi.org/10.1021/acsnano.3c10740
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
Li R, Jiang X, Zhou C, et al (2023) Deciphering phase evolution in complex metal oxide thin films via high-throughput materials synthesis and characterization. Nanotechnology 34:125701. https://doi.org/10.1088/1361-6528/acad09
Wu Q, Avanesian T, Qu X, Van Dam H (2022) PolyODENet: Deriving mass-action rate equations from incomplete transient kinetics data. The Journal of Chemical Physics 157:. https://doi.org/10.1063/5.0110313
Qu X, Yan D, Li R, Cen J, Zhou C, Zhang W, Lu D, Attenkofer K, Stacchiola DJ, Hybertsen MS, Stavitski E, Liu M (2021) Resolving the Evolution of Atomic Layer-Deposited Thin-Film Growth by Continuous In Situ X-Ray Absorption Spectroscopy. Chemistry of Materials 33:1740–1751. doi: 10.1021/acs.chemmater.0c04547
Wills LA, Qu X, Chang I-Y, Mustard TJL, Keszler DA, Persson KA, Cheong PH-Y (2017) Group additivity-Pourbaix diagrams advocate thermodynamically stable nanoscale clusters in aqueous environments. Nature Communications. doi: 10.1038/ncomms15852
Rajput NN, Qu X, Sa N, Burrell AK, Persson KA (2015) The Coupling between Stability and Ion Pair Formation in Magnesium Electrolytes from First-Principles Quantum Mechanics and Classical Molecular Dynamics. Journal of the American Chemical Society 137:3411–3420. doi: 10.1021/jacs.5b01004
Qu X, Jain A, Rajput NN, Cheng L, Zhang Y, Ong SP, Brafman M, Maginn E, Curtiss LA, Persson KA (2015) The Electrolyte Genome project: A big data approach in battery materials discovery. Computational Materials Science 103:56–67. doi: 10.1016/j.commatsci.2015.02.050
Qu X, Latino DA, Aires-de-Sousa J (2013) A big data approach to the ultra-fast prediction of DFT-calculated bond energies. Journal of Cheminformatics. doi: 10.1186/1758-2946-5-34