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The emerging demand for advanced structural and biological materials callsfor novel modeling tools that can rapidly yield high-fidelity estimation onmaterials properties in design cycles. Lattice spring model (LSM), acoarse-grained particle spring network, has gained attention in recent yearsfor predicting the mechanical properties and giving insights into the fracturemechanism with high reproducibility and generalizability. However, to simulatethe materials in sufficient detail for guaranteed numerical stability andconvergence, most of the time a large number of particles are needed, greatlydiminishing the potential for high-throughput computation and therewith datageneration for machine learning frameworks. Here, we implement CuLSM, aGPU-accelerated CUDA C++ code realizing parallelism over the spring listinstead of the commonly used spatial decomposition, which requires intermittentupdates on the particle neighbor list. Along with the image-to-particleconversion tool Img2Particle, our toolkit offers a fast and flexible platformto characterize the elastic and fracture behaviors of materials, expediting thedesign process between additive manufacturing and computer-aided design. Withthe growing demand for new lightweight, adaptable, and multi-functionalmaterials and structures, such tailored and optimized modeling platform hasprofound impacts, enabling faster exploration in design spaces, better qualitycontrol for 3D printing by digital twin techniques, and larger data generationpipelines for image-based generative machine learning models.

ImageMech: From image to particle spring network for mechanical characterization