In future posts, I’ll try to get the Nion Swift microscope control software working, as well as our in-house transmission electron microscopy simulation code abTEM – hopefully, eventually with GPU-acceleration via Apple’s Metal APIs! Installing ASE and GPAWĬonda config -set auto_activate_base false # Optional I did some initial benchmarks, and the M1 chip was running circles around the Intel Xeon W chip in my iMac Pro. Please chime in the comments if you run into any problems, as this was a bit of a trial and error process and the software environment is quickly evolving. However, I immediately took it further, getting a working – and quite well-performing – installations of the Atomic Simulation Environment ( ASE ), used for building, manipulating and visualizing atomistic structure files, as well as a parallel installation of the density functional theory code GPAW. In this post, which I expect will be the first in a series, I’ll share the code that got me running with a basic Python 3.9, scipy, and matplotlib environment. I got my hands on a first-gen M1 Macbook Pro, and though it is very early days for native Apple silicon support, much of the Python code that I am using in my everyday work is already fully functional and running natively thanks to this Conda-forge project. Recently, Apple released their first Mac computers using a novel in-house Apple silicon ARM architecture, with the first processor in the series dubbed the M1. I’ve long been an Apple enthusiast, and greatly enjoy the combination of a sleek, modern GUI and extensive software support, coupled with a true terminal environment for my scientific computing work, that MacOS offers. Update : added missing PAW setup installation instructions.) Update : added benchmark for real DFT runs with GPAW. (Update : changed installation instructions to ASE and GPAW development versions from Git.
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