Precision evaluation criteria for simulation algorithms in infinite systems: A network model-based approach

As the particle count escalates, the computational demands of diverse simulation algorithms surge, paralleled by a marked improvement in accuracy. The question arises whether this heightened precision asymptotically dwindles toward zero or plateaus at a finite constant. To address this, this work introduces an approach that translates infinite systems into finite-node network architectures, providing a rigorous framework for assessing this question. Employing the Monte Carlo algorithm’s application to the Ising model as a case study, this paper demonstrates that, despite the simulation’s extension to an infinite lattice size, a fundamental error bound persists. This work explicitly derives this lower bound on the error, offering a quantitative understanding of the algorithm’s limitations in the limit of infinite scale. Furthermore, I extend this methodology to molecular dynamics simulations, exemplified through its application to battery systems. This conversion strategy not only underscores the generality of this approach but also highlights its practical significance in guiding the optimization of simulation algorithms. Moreover, it offers insights into estimating microlevel information from macrolevel data. The network architecture enables precise prediction of the phase transition point in the two-dimensional Ising model with 98.4% accuracy.