  The emerging demand for advanced structural and biological materials calls
for novel modeling tools that can rapidly yield high-fidelity estimation on
materials properties in design cycles. Lattice spring model (LSM), a
coarse-grained particle spring network, has gained attention in recent years
for predicting the mechanical properties and giving insights into the fracture
mechanism with high reproducibility and generalizability. However, to simulate
the materials in sufficient detail for guaranteed numerical stability and
convergence, most of the time a large number of particles are needed, greatly
diminishing the potential for high-throughput computation and therewith data
generation for machine learning frameworks. Here, we implement CuLSM, a
GPU-accelerated CUDA C++ code realizing parallelism over the spring list
instead of the commonly used spatial decomposition, which requires intermittent
updates on the particle neighbor list. Along with the image-to-particle
conversion tool Img2Particle, our toolkit offers a fast and flexible platform
to characterize the elastic and fracture behaviors of materials, expediting the
design process between additive manufacturing and computer-aided design. With
the growing demand for new lightweight, adaptable, and multi-functional
materials and structures, such tailored and optimized modeling platform has
profound impacts, enabling faster exploration in design spaces, better quality
control for 3D printing by digital twin techniques, and larger data generation
pipelines for image-based generative machine learning models.
