American Journal of Physics®

For Readers - Computational Physics

The Computational Physics Section publishes articles that help students and instructors learn about the computational tools used in contemporary research. Interested authors are encouraged to send a proposal to the editors of the Section, Jan Tobochnik ( or Harvey Gould ( Summarize the physics and the algorithm you wish to discuss and how the material would be accessible to advanced undergraduates or beginning graduate students.

Computational Physics Section of the American Journal of Physics
Jan Tobochnik and Harvey Gould, editors

Information about the Computational Physics Section is available at Jan Tobochnik and Harvey Gould, “New Computational Physics Section,” Am. J. Phys. 80, 1041 (2012). We welcome your submissions and suggestions.

  1. K. Binder, B. J. Block, P. Virnau, and A. Tröster, “Beyond the Van Der Waals loop: What can be learned from simulating Lennard-Jones fluids inside the region of phase coexistence,” Am. J. Phys. 80, 1099–1109 (2012).
  2. G. Volpe and G. Volpe, “Simulation of a Brownian particle in an optical trap,” Am. J. Phys. 81, 224–230 (2013).
  3. F. J. Vesely, “Of pendulums, polymers and robots: Computational mechanics with constraints,” Am. J. Phys. 81, 537–544 (2013).
  4. M. Patriarca and A. Chakraborti, “Kinetic exchange models: From molecular physics to social science,” Am. J. Phys. 81, 618–623 (2013).
  5. R. H. Swendsen, “Using computation to teach the properties of the van der Waals fluid,” Am. J. Phys. 81, 776–781 (2013).
  6. T. Price and R. H. Swendsen, “Numerical computation for teaching quantum statistics,” Am. J. Phys. 81, 866–872 (2013).
  7. Robert M. Dimeo, “Wave packet scattering from time-varying potential barriers in one dimension,” Am. J. Phys. 82, 142–152 (2014).
  8. Milovan Suvakov and V. Dmitravsinovic, “A guide to hunting periodic three-body orbits,” Am. J. Phys. 82, 609–619 (2014).
  9. Tao Pang, “Diffusion Monte Carlo: A powerful tool for studying quantum many-body systems,” Am. J. Phys. 82, 980–988 (2014).
  10. Adriana Gomes Dickman and Ronald Dickman, “Computational model of a vector-mediated epidemic,” Am. J. Phys. 83, 468–474 (2015).
  11. Fernando M. S. Silva Fernandes, “Gibbs ensemble Monte Carlo,” Am. J. Phys. 83, 809–816 (2015).
  12. Larry Engelhardt, “Magnetic resonance: Using computer simulations and visualizations to connect quantum theory with classical concepts,” Am. J. Phys. 83, 1051–1056 (2015).
  13. L. L. Iannini and Ronald Dickman, “Kinetic theory of vehicular traffic,” Am. J. Phys. 84, 135–145 (2016).
  14. Christian G. Fink, “Simulating synchronization in neuronal networks,” Am. J. Phys. 84, 467–473 (2016).
  15. Yanyan Claire Ji and Flavio H. Fenton, “Numerical solutions of reaction-diffusion equations: Application to neural and cardiac models,” Am. J. Phys. 84, 626–638 (2016).
  16. William Graham Hoover, Julien Clinton Sprott, and Carol Griswold Hoover, “Adaptive Runge-Kutta integration for stiff systems: Comparing Nosé and Nosé-Hoover dynamics for the harmonic oscillator,” Am. J. Phys. 84, 786–794 (2016).
  17. Marija Vucelja, “Lifting -- A nonreversible Markov chain Monte Carlo algorithm,” Am. J. Phys. 84, 958–968 (2016).
  18. Yu Liu, Chao Li, Huai-Yu Wang, and Yun-Song Zhou, “The generalized scattering coefficient method for plane wave scattering in layered structures,” Am. J. Phys. 85, 146–150 (2017).
  19. Daniel V. Schroeder, “The variational-relaxation algorithm for finding quantum bound states,” Am. J. Phys. 85, 698–704 (2017).
  20. J. Wang and Y. Hao, “Meshfree computation of electrostatics and related boundary value problems,” Am. J. Phys. 85, 542–549 (2017.
  21. F. Esquembre, W. Christian, and M. Belloni, “Parallel programming with Easy Java Simulations,” Am. J. Phys. 86, 54–67 (2018).
  22. Marise J. E. Westbroek, Peter R. King, Dimitri D. Vvedensky, and Stephan Dürr, “User's guide to Monte Carlo methods for evaluating path integrals,” Am. J. Phys. 86, 293–304 (2018).
  23. Alessandro Santini and Paolo Politi, “Learning universality and scaling from simple deposition models,,” Am. J. Phys. 86, 616–621 (2018).
  24. Abigail H. Chown, Christopher J. Cook, and Nigel B. Wilding, “A simulated annealing approach to the student-project allocation problem,” Am. J. Phys. 86, 701–708 (2018).
  25. Ge Zhang, “Random sequential adsorption and its long-time limit,” Am. J. Phys. 86, 772–776 (2018).
  26. Renato Pakter, and Yan Levin, “Stability of planetary systems: A numerical didactic approach,” Am. J. Phys. 87, 69–74 (2019).
  27. Alfred C. K. Farris, Thomas Würst, and David P. Landau, “Statistical physics meets biochemistry: Wang-Landau sampling of the HP model of protein folding,” Am. J. Phys. 87, 310–316 (2019).
  28. Ruth Chabay and Bruce Sherwood, “Polarization in electrostatics and circuits: Computing and visualizing surface charge distributions,” Am. J. Phys. 87, 341–349 (2019).
  29. Genevieve Godec and Karen Livesey, “Computing the effective permittivity of composite materials using a finite difference method,” Am. J. Phys. 87, 465–470 (2019).
  30. Siu A. Chin and John Masse, “The hardwall method of solving the radial Schrödinger equation and unmasking hidden symmetries,” Am. J. Phys. 87, 682–686 (2019).
  31. István Donkó, Peter Hartmann, and Zoltán Donkó, “Molecular dynamics simulation of a two-dimensional dusty plasma,” Am. J. Phys. 87, 986–993 (2019).
  32. Mark H. Holmes, “Conservative numerical methods for nonlinear oscillators,” Am. J. Phys. 88, 60–69 (2020).
  33. Katharina Vollmayr-Lee, “Introduction to molecular dynamics simulations,” Am. J. Phys. 88, 401–422 (2020).
  34. D. F. Rodriguez-Patiño, S. Ramirez, J. S. Salcedo-Gallo, J. H. Hoyos, and E. Restrepo-Parra, “Implementation of the two-dimensional electrostatic particle-in-cell method,” Am. J. Phys. 88, 159–167 (2020).
  35. Isaac Bowser, Ken Kiers, Erica Mitchell, and Joshua Kiers, “Weyl's problem: A computational approach,” Am. J. Phys. 88, 769–783 (2020).
  36. Swayamshree Patra, Swagata Dey, Krishanu Ray, and Debashish Chowdhury, “Digital imaging of a random walk by computer simulation: Using a simple model to interpret the effects of finite spatio-temporal resolution,” Am. J. Phys. 89, 437–442 (2021).
  37. Tiare Guerrero and Danielle McDermott, “Molecular dynamics simulation of synchronization of a driven particle,” Am. J. Phys. 89, 975–981 (2021).
  38. Daniel M. Zuckerman and John D. Russo, “A gentle introduction to the non-equilibrium physics of trajectories: Theory, algorithms, and biomolecular applications,” Am. J. Phys. 89, 1048–1061 (2021).
  39. Christian Scholz and Sandy Scholz, “Exploring complex pattern formation with convolutional neural networks,” Am. J. Phys. 90, 141–151 (2022).
  40. Frederico Campos Freitas, Sandra Byju, Asem Hassan, Ronaldo Junio de Oliveira, and Paul C. Whitford, “Quantifying biomolecular diffusion with a “spherical cow” model,” Am. J. Phys. 90, 225–238 (2022).
  41. Giorgio Mantica, “Simulating epidemics via a theory of dynamical systems,” Am. J. Phys. 90, 380–393 (2022).
  42. Pablo Jensen, “ Introducing simple models of social systems, “ Am. J. Phys. 90, 462–468 (2022).
  43. Emanuel A. Lazar, Jiayin Lu, and Chris H. Rycroft, “ Voronoi cell analysis: The shapes of particle systems,“ Am. J. Phys. 90, 469–480 (2022).
  44. Wenlong Wang, “ An introduction to the Markov chain Monte Carlo method,“ Am. J. Phys. 90, 921 (2022).

Updated 27 January 2023.