The discrete element method (DEM) is a powerful simulation technique that is capable of numerically modelling the behaviour of complex granular media, being used to better understand and optimise the internal dynamics of a large number of systems in both academic fields and industrial sectors, from fundamental research into contact mechanics to improving plant-scale reactors [2]. DEM can offer exceptional accuracy through its lack of approximations over meshes and, if correctly calibrated, simulations can provide results with quantitative precision. It is this “if”, however, that also represents DEM’s biggest drawback: without choosing appropriate contact models and carefully calibrating multiple DEM parameters, the simulation outputs simply cannot be trusted. This calibration is a time-consuming process, typically involving the measurement of diverse particle properties including size, density, restitution and friction coefficients and, for purely ”virtual” parameters such as the cohesive energy density, a great deal of experimentation [1]. To automate DEM calibration against experimental measurements, we have developed ACCESS – Autonomous Characterisation and Calibration using Evolutionary Simulation Software. ACCESS enables a researcher to calibrate virtually any DEM parameters against a user-defined cost function, quantifying and subsequently minimising the disparity between the simulated system and the experimental reality using state-of-the-art evolutionary strategies – in essence, autonomously ‘learning’ the physical properties of the particles within the system, without the need for human input. This cost function is completely general, allowing ACCESS to calibrate DEM against measurements as simple as photographed occupancy plots, or complex system properties captured through e.g. Lagrangian particle tracking. The algorithm itself is completely DEM engine-agnostic; it was implemented in an open-source Python library, providing an interface that is easy to use, but powerful enough to automatically parallelise arbitrary user scripts through code inspection and metaprogramming. It was used successfully from laptop-scale shared-memory machines to multi-node supercomputing clusters. References [1] Luding, S. (2008). Introduction to discrete element methods: basic of contact force models and how to perform the micro-macro transition to continuum theory. [2] Rosato, A. D. and Windows-Yule, C. (2020). Segregation in Vibrated Granular Systems.