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MALA

Authors: Cangi, Attila; Rajamanickam, Sivasankaran; Brzoza, Bartosz; Callow, Timothy J.; Ellis, J. Austin; Faruk, Omar; Fiedler, Lenz; +17 Authors
Abstract

New features Pre- and postprocessing for elemental mixtures added MALA can now natively handle multielement systems (both with bispectrum and ACE descriptors, see below) This feature is still tested in production, so now extensive documentation exists at this point Splitting along the energy axis now supported This massively reduces the data cost for systems including core electrons ACE descriptors implemented, require specific LAMMPS version to function More details will be described in upcoming paper Currently, now GPU calculation available Descriptors can now be calculated on-the-fly This is especially useful in the GPU-accelerated case, since the GPU calculation of the descriptors is typically very fast and can be done directly before the training Through this functionality, descriptor related hyperparameters can be fixed more easily Changes to API/user experience DataScaler are now saved in .json format MALA tested up to Python 3.13.3 Fixes Fixed bug that broke DDP performance Several smaller internal API fixes Improved CI

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Keywords

machine-learning, dft

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
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