Publications by authors named "Alexander J Gabourie"
Article Synopsis
- The text discusses advancements in machine-learned potentials (MLPs) utilizing the neuroevolution potential (NEP) framework, enhancing accuracy through improved atomic-environment descriptors and angular contributions.
- It highlights efficient implementation on graphics processing units and the application of NEP models in large-scale atomistic simulations, showcasing above-average accuracy and computational efficiency.
- The proposal includes an active-learning scheme for minimal training set construction and introduces three Python packages (gpyumd, calorine, and pynep) to facilitate integration with Python workflows.
Heterogeneous integration of nanomaterials has enabled advanced electronics and photonics applications. However, similar progress has been challenging for thermal applications, in part due to shorter wavelengths of heat carriers (phonons) compared to electrons and photons. Here, we demonstrate unusually high thermal isolation across ultrathin heterostructures, achieved by layering atomically thin two-dimensional (2D) materials.
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