🎲 Monte Carlo · Monte Carlo Simulation
Methodology
Monte Carlo methods simulate systems statistically through random sampling. They are the classic approach to statistical-physics problems such as alloy ordering, adsorption statistics, phase transition statistics and thin-film growth, and they complement first-principles and molecular dynamics across scales.
Applications
Related projects
Order-disorder transitions ★ Flagship
Transition temperature and degree of order — flagship scenario
KMC thin-film growth
Kinetic Monte Carlo morphology
Irradiation damage statistics
Defect formation and buildup
Segregation behavior
Grain-boundary segregation stats
Case Studies
FAQ
If you care about equilibrium statistical properties — degree of order, adsorption capacity, transition temperature — Monte Carlo is more efficient; if you care about time evolution, choose molecular dynamics. Bian Xiaobo gives a free preliminary assessment once you submit your request.
They can be fitted from first-principles calculations by expanding into a cluster expansion, giving a multiscale DFT+MC workflow.
5–7 days for routine systems; 10–14 days for combined DFT+MC projects, written into the SLA at signing.
Includes the Computational Traceability Report: models, interaction parameters and sampling steps are all archived.