The scientific goal of MCMET is to develop a novel strategy for simulating complex energy systems by building upon recent advances in Monte Carlo (MC) path-sampling methods. Recent advances in statistical physics and computer graphics have paved the way to tackle a long standing issue: solving non linear models with MC methods while preserving its fundamental capacity to scale up with the geometry and physics complexity. The project focuses on one specific type of non linearity: the one related to the collision frequency parameter, which defines the geometry of the (radiative, conductive, electronical...) paths. Three applications are targeted: radiant energy conversion systems (photoreactive and photovoltaic) for solar fuels and electricity production; thermal performance of buildings targeting both energy consumption reduction and thermal comfort of inhabitants; and estimation of the ground solar resource in presence of clouds in climate simulations.The locks in these applications can be formulated in a common framework and are due to non linear dependencies to the models’ collision frequency parameters. In this project , the following questions will be addressed: (1) How to formulate non linear physical models under the path-space formulation? (2) How to conceive scientific computation libraries for sampling these new multi-scale multi-physics path spaces? (3) How to implement algorithms in this framework to meet the application needs? The consortium brings together MC specialists, computer scientists and physicists specialized in energy, climate and buildings, to develop a novel modelling paradigm allowing the gain of orders of magnitude in performance and cost of numerical simulations. Impacts will be immediate for the applicative domains thanks ,to the development of application-driven codes. Longer-term outlooks include the creation of MC-based climate services for the energy sector.