METHODS OF COMPUTATIONAL PHYSICS

• Computer Meets Theoretical Physics, G. Battimelli et al. (Springer, 2020)
• Ten computer codes that transformed science, J. Perkel, Nat. Phys. 589, 345 (2021)
• Monte Carlo simulations: U. Wolff, Phys. Rev. Lett. 62, 361 (1989); D. Kandel, et al., Phys. Rev. Lett. 60, 1591 (1988); B. A. Berg & T. Neuhaus, Phys. Rev. Lett. 68, 9 (1992); F. Wang & D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001); B. Mehlig, et al., Phys. Rev. B 45, 679 (1992)
• Minimally entangled typical thermal state algorithms, E. M. Stoudenmire & S. R. White, New J. Phys. 12, 055026 (2010)
• Boltzmann generators: sampling equilibrium states of many-body systems with deep learning: F. Noe, et al., Science 365, eaaw1147 (2019)
• Integrating neural networks with a quantum simulator for state reconstruction: G. Torlai, et al., Phys. Rev. Lett. 123, 230504 (2019)
• Machine learning for the physical sciences: G. Carleo, et al., Rev. Mod. Phys. 91, 045002 (2020)
• Boltzmann machine modeling of layered MoS2 synthesis on a quantum annealer: J. Liu, et al., Comput. Mater. Sci. 173, 109429 (2020)
• Statistical Mechanics of Neural Networks, H. Huang (Springer, 2021)
• Molecular dynamics integrator: M. Tuckerman, B. J. Berne & G. J. Martyna, J. Chem. Phys. 97, 1990 (1992)
• Higher-order symplectic integrators: H. Yoshida, Phys. Lett. A 150, 262 (1990)
• Backward error analysis for numerical integrators: S. Reich, SIAM J. Numer. Anal. 36, 1549 (1999)
• A unified formulation of the constant temperature molecular dynamics methods: S. Nose, J. Chem. Phys. 81, 511 (1984)
• Explicit reversible integrators for extended systems dynamics: G. J. Martyna, M. E. Tuckerman, D. J. Tobias & M. L. Klein, Mol. Phys. 87, 117 (1996)
• Product formula algorithms for solving the time dependent Schrodinger equation: H. de Raedt, Comput. Phys. Rep. 7, 1 (1987)
• Iterative minimization techniques for ab initio total-energy calculations: molecular dynamics and conjugate gradients: M. C. Payne, et al., Rev. Mod. Phys. 64, 1045 (1992)
• Inhomogeneous electron gas: P. Hohenberg & W. Kohn, Phys. Rev. 136, B864 (1964)
• CACS DFT (density functional theory)-seminar papers
• Quantum molecular dynamics in the post-petaflops era: N. Romero, et al., IEEE Computer, 48(11), 33 (2015)
• Transferable tight-binding models for silicon: I. Kwon, et al., Phys. Rev. B 49, 7242 (1994)
• Density-matrix algorithms for quantum renormalization groups: S. R. White, Phys. Rev. B 48, 10345 (1993)
• The density-matrix renormalization group in quantum chemistry: G. K.-L. Chan & S. Sharma, Annu. Rev. Phys. Chem. 62, 465 (2011)
• Quantum Monte Carlo simulations of solids: W. M. C. Foulkes, et al., Rev. Mod. Phys. 73, 33 (2001)
• Accelerated molecular dynamics methods: D. Perez, et al., Annu. Rep. Comput. Chem. 5, 79 (2009)
• An introduction to Monte Carlo simulations of surface reactions: A. P. J. Jansen (Springer, 2012)
• Theoretical foundations of dynamical Monte Carlo simulations: K. A. Fichthorn & W. H. Weinberg, J. Chem Phys. 95, 1090 (1991)
• Molecular kinetics simulation: A. Nakano, Comput. Phys. Commun. 176, 292 (2007); ibid. 178, 280 (2008)
• Multiscale Simulation Methods in Molecular Sciences: J. Grotendorst, N. Attig, S. Bluegel & D. Marx (John von Neumann Institute for Computing, Juelich, Germany, 2009)