As a surrogate model of the meso-scale homogenised response for non-linear multi-scale simulations, the Deep Material Network (DMN) incorporates analytical homogenisation solutions and material constitutive relations into a neural network model yielding mechanistic building blocks [1]. In order to improve its training efficiency, the DMN was reformulated in [2] from the interaction view-point [2]. Since it is thermodynamically consistent, the IB-DMN can extrapolate the response to different micro-scale material behaviours and loading histories, limiting the size of the data set that is needed for its training. In most of the cases, only the elastic meso-scale homogenised response is required for the training. In the present work, the IB-DMN parameters are first redefined by decoupling the phase volume fraction from the topological interaction parameters, leading to a IB-DMN that can handle arbitrary phase volume fraction. Then, the architecture of the interactions is modified in order to study its effect on the accuracy of the IB-DMN predictions when the phases exhibit a damage process. Besides, a training strategy relying on a non-linear response exhibiting softening is considered in order to improve its accuracy. This project has received funding from the European Union’s Horizon Europe Framework Programme under grant agreement No. 101056682 for the project ‘‘DIgital DEsign strategies to certify and mAnufacture Robust cOmposite sTructures (DIDEAROT)’’. The contents of this publication are the sole responsibility of ULiege and do not necessarily reflect the opinion of the European Union. Neither the European Union nor the granting authority can be held responsible for them. REFERENCES [1] Liu Z., Wu C., Koishi M. A deep material network for multiscale topology learning and accelerated nonlinear modelling of heterogeneous materials, Computer Methods in Applied Mechanics and Engineering, Vol. 345, pp. 1138–1168, 2019. [2] Nguyen V.D., Noels L. Interaction-based material network: A general framework for (porous) microstructured materials, Computer Methods in Applied Mechanics and Engineering, Vol. 389, pp. 114300, 2022.