A differentiable programming framework for spin models
This work addresses the challenge of efficiently simulating complex spin systems for researchers in computational physics and materials science, though it is incremental as it adapts existing algorithms to a new paradigm.
The authors tackled the simulation of spin models by introducing a differentiable programming framework, resulting in enhanced computational speed through parallel processing on GPUs and TPUs.
We introduce a novel framework for simulating spin models using differentiable programming, an approach that leverages the advancements in machine learning and computational efficiency. We focus on three distinct spin systems: the Ising model, the Potts model, and the Cellular Potts model, demonstrating the practicality and scalability of our framework in modeling these complex systems. Additionally, this framework allows for the optimization of spin models, which can adjust the parameters of a system by a defined objective function. In order to simulate these models, we adapt the Metropolis-Hastings algorithm to a differentiable programming paradigm, employing batched tensors for simulating spin lattices. This adaptation not only facilitates the integration with existing deep learning tools but also significantly enhances computational speed through parallel processing capabilities, as it can be implemented on different hardware architectures, including GPUs and TPUs.