T. Gawne, H. Bellenbaum, L. B. Fletcher, K. Appel, C. Baehtz, V. Bouffetier, E. Brambrink, D. Brown, A. Cangi, A. Descamps, S. Göde, N. J. Hartley, M.-L. Herbert, P. Hesselbach, H. Höppner, O. S. Humphries, Z. Konôpková, A. Laso, B. Lindqvist, J. Lütgert, M. J. MacDonald, M. Makita, W. Martin, M. Mishchenko, Z. A. Moldabekov, M. Nakatsutsumi, J.-P. Naedler, P. Neumayer, A. Pelka, C. Qu, L. Randolph, J. Rips, T. Toncian, J. Vorberger, L. Wollenweber, U. Zastrau, D. Kraus, T. R. Preston, T. Dornheim, Effects of Mosaic Crystal Instrument Functions on X-ray Thomson Scattering Diagnostics, arXiv:2406.03301 (2024). 

S. Nikolov, K. Ramakrishna, A. Rohskopf, M. Lokamani, J. Tranchida, J. Carpenter, A. Cangi, M. A. Wood, Probing Iron in Earth's Core With Molecular-Spin Dynamics, arXiv:2311.08737 (2023).

D. R. Vemula, D. Konar, S. Satheesan, S. M. Kalidasu, A. Cangi, A Scalable 5,6-Qubit Grover's Quantum Search Algorithm, arXiv:2205.00117 (2022).

D. Konar, E. Gelenbe, S. Bhandary, A. Das Sarma, A. Cangi, Random Quantum Neural Networks for Noisy Image Recognition, arXiv:2203.01764 (2022).

T. Gawne, Z. A. Moldabekov, O. S. Humphries, K. Appel, C. Bähtz, V. Bouffetier, E. Brambrink, A. Cangi, S. Göde, Z. Konôpková, M. Makita, M. Mishchenko, M. Nakatsutsumi, K. Ramakrishna, L. Randolph, S. Schwalbe, J. Vorberger, L. Wollenweber, U. Zastrau, T. Dornheim, T. R. Preston, Ultrahigh resolution x-ray Thomson scattering measurements at the European X-ray Free Electron Laser, Phys. Rev. B 109, L241112 (2024).

H. Tahmasbi, K. Ramakrishna, M. Lokamani, A. Cangi, Machine Learning-Driven Structure Prediction for Iron Hydrides, Phys. Rev. Mater. 8, 033803 (2024).

V. Martinetto, K. Shah, A. Cangi, A. Pribram-Jones, Inverting the Kohn-Sham equations with physics-informed machine learning, Mach. Learn.: Sci. Technol. 5 015050 (2024).

T. J. Callow, E. Kraisler, A. Cangi, Physics-enhanced neural networks for equation-of-state calculations,  Mach. Learn.: Sci. Technol. 4, 045055 (2023).

S. L. S. Balakrishnan, M. Lokamani, K. Ramakrishna, A. Cangi, D. Murali, M. Posselt, A. A. Sasikala Devi, A. Sharan, Ab-initio insights on the ultrafast strong-field dynamics of anatase TiO2, Phys. Rev. B 108, 195149 (2023).

K. Ramakrishna, M. Lokamani, A. Baczewski, J. Vorberger, A. Cangi, Impact of electronic correlations on high-pressure iron: insights from time-dependent density functional theory, Electron. Struct. 5, 045002 (2023).

L. Fiedler, N. A. Modine, K. D. Miller, A. Cangi, Machine learning the electronic structure of matter across temperatures, Phys. Rev. B 108, 125146 (2023).

S. Kumar, H. Tahmasbi, K. Ramakrishna, M. Lokamani, S. Nikolov, J. Tranchida, M. A. Wood, A. Cangi, Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter, Phys. Rev. Research 5, 033162 (2023).

L. Fiedler, N. A. Modine, S. Schmerler, D. J. Vogel, G. A. Popoola, A. P. Thompson, S. Rajamanickam, A. Cangi, Predicting electronic structures at any length scale with machine learning, Npj Comput. Mater. 9, 115 (2023).

T. W. Hentschel, A. Kononov, A. Olmstead, A. Cangi, A. D. Baczewski, S. B. Hansen, Improving dynamic collision frequencies: impacts on dynamic structure factors and stopping powers in warm dense matter, Phys. Plasmas 30, 062703 (2023).

T. Dornheim, M. P. Böhme, D. Chapman, D. Kraus, T. R. Preston, Z. A. Moldabekov, N. Schlünzen, A. Cangi, T. Döppner, J. Vorberger, Imaginary-time correlation function thermometry: A new, high-accuracy and model-free temperature analysis technique for x-ray Thomson scattering data, Phys. Plasmas 30, 042707 (2023).

T. Dornheim, Z. A. Moldabekov, K. Ramakrishna, P. Tolias, A. D. Baczewski, D. Kraus, T. R. Preston, D. A. Chapman, M. P. Böhme, T. Döppner, F. Graziani, M. Bonitz, A. Cangi, J. Vorberger, Electronic Density Response of Warm Dense Matter, Phys. Plasmas 30, 032705 (2023).

K. Ramakrishna, M. Lokamani, A. Baczewski, J. Vorberger, A. Cangi, Electrical Conductivity of Iron in Earth's Core from Microscopic Ohm's Law, Phys. Rev. B 107, 115131 (2023).

Z. A. Moldabekov, M. Lokamani, J. Vorberger, A. Cangi, T. Dornheim, Non-empirical mixing coefficient for hybrid XC functionals from analysis of the XC kernel, J. Phys. Chem. Lett. 14, 1326 (2023).

Z. A. Moldabekov, M. Lokamani, J. Vorberger, A. Cangi, T. Dornheim, Assessing the accuracy of hybrid exchange-correlation functionals for the density response of warm dense electrons, J. Chem. Phys. 158, 094105 (2023).

D. Konar, A. Das Sarma, S. Bhandary, S. Bhattacharyya, A. Cangi, V. Aggarwal, A shallow hybrid classical–quantum spiking feedforward neural network for noise-robust image classification, Appl. Soft Comput. 136, 110099 (2023).

T. J. Callow, E. Kraisler, A. Cangi, Improved calculations of mean ionization states with an average-atom model, Phys. Rev. Res. 5, 013049 (2023).

K. Shah, P. Stiller, N. Hoffmann, A. Cangi, Physics-Informed Neural Networks as Solvers for the Time-Dependent Schrödinger Equation, NeurIPS Workshop Machine Learning and the Physical Sciences (2022).

L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Cangi, Training-free hyperparameter optimization of neural networks for electronic structures in matter, Mach. Learn.: Sci. Technol. 3, 045008 (2022).

L. Fiedler, Z. A. Moldabekov, X. Shao, K. Jiang, T. Dornheim, M. Pavanello, A. Cangi, Accelerating Equilibration in First-Principles Molecular Dynamics with Orbital-Free Density Functional Theory, Phys. Rev. Res. 4, 043033 (2022).

M. Schörner, B. B. L. Witte, A. D. Baczewski, A. Cangi, R. Redmer, An ab-initio study of shock-compressed copper, Phys. Rev. B 106, 054304 (2022).

T. J. Callow, D. Kotik, E. Kraisler, A. Cangi, AtoMEC: an open-source average-atom Python code, Proceedings of the 21st Python in Science Conference, 37 (2022).

T. Dornheim, P. Tolias, Z. A. Moldabekov, A. Cangi, J. Vorberger, Effective electronic forces and potentials from ab initio path integral Monte Carlo simulations, J. Chem. Phys. 156, 244113 (2022).

T. J. Callow, E. Kraisler, S. B. Hansen, A. Cangi, First-principles derivation and properties of density-functional average-atom models, Phys. Rev. Res. 4, 023055 (2022). 

L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry, Phys. Rev. Mater. 6, 040301 (2022).

S. Nikolov, J. Tranchida, K. Ramakrishna, M. Lokamani, A. Cangi, M. A. Wood, Dissociating the phononic, magnetic and electronic contributions to thermal conductivity: a computational study in alpha-iron, J. Mater. Sci. 57, 10535 (2022).

Z. A. Moldabekov, T. Dornheim, G. Gregori, F. Graziani, M. Bonitz, A. Cangi, Towards a Quantum Fluid Theory of Correlated Many-Fermion Systems from First Principles, SciPost Phys. 12, 062 (2022).

Z. A. Moldabekov, T. Dornheim, J. Vorberger, A. Cangi, Benchmarking Exchange-Correlation Functionals in the Spin-Polarized Inhomogeneous Electron Gas under Warm Dense Conditions, Phys. Rev. B 105, 035134 (2022).

Z. A. Moldabekov, T. Dornheim, A. Cangi, Thermal Signals from Collective Electronic Excitations in Inhomogeneous Warm Dense Matter, Sci. Rep. 12, 1093 (2022).

Z. A. Moldabekov, Y. K. Aldakul, N. K. Bastykova, S. Sundar, A. Cangi, Higher harmonics in complex plasmas with alternating screening, Phys. Rev. Res. 3, 043187 (2021).

S. Nikolov, M. A. Wood, A. Cangi, J.-B. Maillet, M.-C. Marinica, A. P. Thompson, M. P. Desjarlais, J. Tranchida, Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics, Npj Comput. Mater. 7, 153 (2021).

Z. A. Moldabekov, T. Dornheim, M. Böhme, J. Vorberger, A. Cangi, The Relevance of Electronic Perturbations in the Warm Dense Electron Gas, J. Chem. Phys. 155, 124116 (2021).

J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, S. Rajamanickam, Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks, Phys. Rev. B 104, 035120 (2021).

T. Dornheim, Z. A. Moldabekov, A. Cangi, A Machine-Learning Surrogate Model for ab initio Electronic Correlations at Extreme Conditions, ICLR 2021 (2021).

K. Ramakrishna, A. Cangi, T. Dornheim, J. Vorberger, First-principles modeling of plasmons in aluminum under ambient and extreme conditions, Phys. Rev. B 103, 125118 (2021).

T. Dornheim, A. Cangi, K. Ramakrishna, M. P. Böhme, S. Tanaka, J. Vorberger, Effective static approximation: A fast and reliable tool for warm dense matter theory, Phys. Rev. Lett. 125, 235001 (2020).

T. Baldsiefen, A. Cangi, F. G. Eich, E. K. U. Gross, Exchange-correlation approximations for reduced-density-matrix-functional theory at finite temperature: Capturing magnetic phase transitions in the homogeneous electron gas, Phys. Rev. A 96, 062508 (2017).

T. Baldsiefen, A. Cangi, E. K. U. Gross, Reduced-density-matrix-functional theory at finite temperature: Theoretical foundations, Phys. Rev. A 92, 052514 (2015).

A. Cangi, A. Pribram-Jones, Efficient formalism for warm dense matter simulations, Phys. Rev. B 92, 161113(R) (2015).

P. Elliott, A. Cangi, S. Pittalis, E. K. U. Gross, K. Burke, Almost exact exchange at almost no computational cost in electronic structure, Phys. Rev. A 92, 022513 (2015).

R. F. Ribeiro, D. Lee, A. Cangi, P. Elliott, K. Burke, Corrections to Thomas-Fermi Densities at Turning Points and Beyond, Phys. Rev. Lett. 114, 050401 (2015).

H. Mirhosseini, A. Cangi, T. Baldsiefen, A. Sanna, C. R. Proetto, E. K. U. Gross, Virial theorem and exact properties of density functionals for periodic systems, Phys. Rev. B 89, 220102(R) (2014).

A. Cangi, E. K. U. Gross, K.  Burke, Potential functionals versus density functionals, Phys. Rev. A 88, 062505 (2013).

A. Cangi, D. Lee, P. Elliott, K. Burke, E. K. U. Gross, Electronic Structure via Potential Functional Approximations, Phys. Rev. Lett. 106, 236404 (2011).

A. Cangi, D. Lee, P. Elliott, K. Burke, Leading corrections to local approximations, Phys. Rev. B 81, 235128 (2010).

P. Elliott, D. Lee, A. Cangi, K. Burke, Semiclassical Origins of Density Functionals, Phys. Rev. Lett. 100, 256406 (2008).

M. Wood, S. Nikolov, A. Rohskopf, M. P. Desjarlais, A. Cangi, J. Tranchida, Quantum-Accurate Multiscale Modeling of Shock Hugoniots, Ramp Compression Paths, Structural and Magnetic Phase Transitions, and Transport Properties in Highly Compressed Metals, Technical Report SAND2022-12792, United States Department of Energy (2022).

S. B. Hansen, A. D. Baczewski, T. Gomez, T. W. Hentschel, C. A. Jennings, A. Kononov, T. Nagayama, K. Adler, A. Cangi, K. Cochrane, B. Robinson, A. Schleife, Improving Predictive Capability in REHEDS Simulations with Fast, Accurate, and Consistent Non-Equilibrium Material Properties, Technical Report SAND2022-13455, United States Department of Energy (2022).

N. A. Modine, J. A. Stephens, L. P. Swiler, A. P. Thompson, J. D. Vogel, L. Fiedler, A. Cangi, S. Rajamanickam, Accelerating Multiscale Materials Modeling with Machine Learning, Technical Report SAND2022-12875, United States Department of Energy (2022).

J. A. Hubbard, M. A. Omana, D. S. Jensen, A. Cangi, T. J. Boyle, Uranium Aerosol Dynamics: Chemical Kinetics, Primary Particle Formation, Coagulation and Condensation, Technical Report SAND2019-13154, United States Department of Energy (2019).

A. Cangi, F. Sagredo, E. Decolvenaere, A. E. Mattsson, Semi-local Density Functional Approximations for Bulk, Surface, and Confinement Physics, Technical Report SAND2019-11805, United States Department of Energy (2019).

A. Cangi, An Exchange-Correlation Functional Capturing Bulk, Surface, and Confinement Physics, Technical Report SAND2018-7447, United States Department of Energy (2018).

Author list hidden by publisher, Technical Report SAND2017-13554, United States Department of Energy (2017).

A. Cangi, J. Citrin, U. von Toussaint (Eds.), Special issue: Machine learning methods in plasma physics, Contrib. Plasma Phys. e202300060 (2023).

A. Cangi, M. L. Parks, Center for Computing Research Summer Proceedings 2018, The Center for Computing Research at Sandia National Laboratories, Technical Report SAND2019-5093R, United States Department of Energy (2019).

L. Fiedler, K. Shah, A. Cangi, Machine-Learning for Static and Dynamic Electronic Structure Theory. In: Qu, C., Liu, H. (eds) Machine Learning in Molecular Sciences. Challenges and Advances in Computational Chemistry and Physics, vol 36. Springer, Cham (2023).

T. J. Callow, D. Kotik, E. Tsvetoslavova Stankulova, N. Rahat, E. Kraisler, A. Cangi, atoMEC (2023).

A. Cangi, S. Rajamanickam, B. Brzoza, T. J. Callow, J. A. Ellis, O. Faruk, L. Fiedler, J. Fox, N. Hoffmann, K. D. Miller, D. Kotik, S. Kulkarni, N. Modine, P. Mohammed, V. Oles, G. A. Popoola, F. Pöschel, J. Romero, S. Schmerler, J. A. Stephens, H. Tahmasbi, A. P. Thompson, S. Verma, D. J. Vogel, Materials Learning Algorithms (MALA) (2023).