CudaCAC is a Cuda accelerated implementation of the Concurrent Atomistic-Continuum (CAC) method.
## Background
### Molecular Dynamics
Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic evolution of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanics force fields.
### Concurrent Atomistic-Continuum (CAC) Method
The Concurrent Atomistic-Continuum (CAC) method is a multiscale modeling technique used for simulating materials at the nano and micro-scale. It partitions a simulation into a coarse-grained domain and an atomistic domain. This allows for the detailed, fully-resolved atomistic simulation of important regions, like those with lattice defects, while more efficiently modeling the rest of the material as a continuum. A key feature of the CAC method is its use of a unified set of governing equations and interatomic potentials across both the atomistic and continuum domains. This avoids the need for complex coupling procedures at the interface of the two regions.
## Tech Stack
This project leverages a high-performance computing stack for its simulations:
***C++:** The core application logic is written in modern C++, providing a balance of performance and high-level abstractions.
***CUDA:** NVIDIA's CUDA platform is used to accelerate the computationally intensive parts of the simulation on the GPU.
***CMake:** A cross-platform build system used to manage the compilation and linking of the project.
***Google Test:** A testing framework for writing C++ tests.
***Doxygen:** A documentation generator for C++ code.
## Roadmap
- [ ] Complete basic molecular dynamics atomistic solver using Cuda using Lennard-Jones pair potential with order O(n^2) calculations
- [ ] Implement CAC rhombohedral finite element solver
- [ ] Adding neighbor lists with cutoff distances to reduce runtime complexity
- [ ] Adding multi-body potential support
- [ ] Adding support for overlaying multiple potentials
## Contact
For any questions or inquiries, please contact Alex Selimov at [alex@alexselimov.com](mailto:alex@alexselimov.com) or visit his website at [alexselimov.com](https://alexselimov.com).
