Bibliography ============ Key references behind the algorithms implemented in `mcpy`. The list is intentionally short; add domain-specific citations from your own work as you adapt the library. .. note:: When `sphinxcontrib-bibtex` is enabled (see ``docs/requirements.txt``), this page will be regenerated automatically from ``bibliography.bib``. The entries below are placeholders so the page renders before that switch is made. Hybrid Grand Canonical Monte Carlo ---------------------------------- Senftle, T. P., Janik, M. J., van Duin, A. C. T. *A ReaxFF investigation of hydride formation in palladium nanoclusters via Monte Carlo and molecular dynamics simulations.* **Journal of Physical Chemistry C**, 118 (9), 4967–4981 (2014). `doi:10.1021/jp411015a `_. The hybrid scheme — every trial insertion or deletion is followed by a local relaxation before acceptance — is the basis of the GCMC loop in :class:`GrandCanonicalEnsemble`. Replica Exchange / Parallel Tempering ------------------------------------- Swendsen, R. H., Wang, J.-S. *Replica Monte Carlo simulation of spin-glasses.* **Physical Review Letters**, 57 (21), 2607–2609 (1986). `doi:10.1103/PhysRevLett.57.2607 `_. The exchange acceptance rule used by :class:`ReplicaExchange` follows the standard parallel-tempering criterion derived in this paper. Atomic Simulation Environment ----------------------------- Larsen, A. H., Mortensen, J. J., Blomqvist, J., et al. *The atomic simulation environment — a Python library for working with atoms.* **Journal of Physics: Condensed Matter**, 29 (27), 273002 (2017). `doi:10.1088/1361-648X/aa680e `_. `mcpy` is built directly on ASE: every configuration is an :class:`ase.Atoms` object, and any ASE-compatible calculator can drive the sampling. MACE ---- Batatia, I., Kovács, D. P., Simm, G. N. C., Ortner, C., Csányi, G. *MACE: Higher order equivariant message passing neural networks for fast and accurate force fields.* **NeurIPS 2022**. `arXiv:2206.07697 `_. The reference MLIP used in the bundled examples and tutorials.