| Uncertainty quantification in machine learning and nonlinear least squares regression models N Zhan, JR Kitchin AIChE Journal 68 (6), e17516, 2022 | 32 | 2022 |
| Graphical models for financial time series and portfolio selection N Zhan, Y Sun, A Jakhar, H Liu Proceedings of the first acm international conference on ai in finance, 1-6, 2020 | 11 | 2020 |
| Space Group Equivariant Crystal Diffusion R Chang, A Pak, A Guerra, N Zhan, N Richardson, E Ertekin, RP Adams arXiv preprint arXiv:2505.10994, 2025 | 6 | 2025 |
| Model-Specific to Model-General Uncertainty for Physical Properties N Zhan, JR Kitchin Industrial & Engineering Chemistry Research 61 (24), 8368-8377, 2022 | 5 | 2022 |
| AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs? O Press, B Amos, H Zhao, Y Wu, SK Ainsworth, D Krupke, P Kidger, ... arXiv preprint arXiv:2507.15887, 2025 | 3 | 2025 |
| Expressivity of determinantal ansatzes for neural network wave functions N Zhan, WA Wheeler, G Goldshlager, E Ertekin, RP Adams, LK Wagner Journal of Chemical Theory and Computation 21 (19), 9612-9619, 2025 | 2 | 2025 |
| Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schr\" odinger Equation KH Huang, N Zhan, E Ertekin, P Orbanz, RP Adams arXiv preprint arXiv:2502.05318, 2025 | 2 | 2025 |
| Origin of the Stokes–Einstein deviation in liquid Al–Si N Zhan, JR Kitchin Molecular Simulation 48 (4), 303-313, 2022 | 2 | 2022 |
| Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics M Wang, J Ding, G Xiong, N Zhan, CJ Owen, A Musaelian, Y Xie, ... arXiv preprint arXiv:2503.15389, 2025 | 1 | 2025 |
| Practical Application of Machine Learning in Catalysis ZW Ulissi, K Tran, J Yoon, M Shuaibi, M Liu, N Zhan, K Broderick, ... | | 2024 |
| Neural Network Wave Function Solver N Zhan, W Wheeler, K Huang, P Orbanz, L Wagner, E Ertekin, RP Adams 2024 AIChE Annual Meeting, 2024 | | 2024 |
| Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits N Zhan arXiv preprint arXiv:2101.09230, 2021 | | 2021 |
| Uncertainty Measurement Method for Machine Learned Potentials N Zhan, J Kitchin 2019 AIChE Annual Meeting, 2019 | | 2019 |
| Crystal Generative Modeling with Explicit Autoregressive Conditional Likelihoods and Nontrivial Space Group Stabilizers R Chang, A Guerra, N Richardson, N Zhan, S Liu, A Pak, R Marr, ... AI for Accelerated Materials Design-ICLR 2025, 0 | | |
John KitchinCarnegie Mellon UniversityVerified email at andrew.cmu.edu
