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Literature review on machine learning density functional theory (ML-DFT) and related topics

Title: An introduction to training algorithms for neuromorphic computing and on-line learning Abstract: Training neural networks implemented in neuromorphic hardware is challenging due to the dynamic, sparse, and local nature of the computations. This tutorial will describe some established gradient-based solutions to address these challenges in the context of real-valued recurrent neural networks and spiking neural networks. Insights into gradient-based training algorithms and associated autodifferentiation methods lead to online synaptic plasticity rules and the necessary assumptions to implement them in in-memory computing devices. The tutorial will conclude with methods that can be used to improve and optimize learning algorithms using meta-learning and other meta-optimization approaches.

GNU Scientific library mirror

https://iffmd.fz-juelich.de/RhXA4J_JTwebjYKG35TbuQ#

Repository containing utilities for creating documentations.

SPEX documentation

Electron transport calculation code based on the real-space finite-difference formalism within the framework of the density functional theory

Tool for synching config files between several hosts. It manages differences between systems with Git branches.

SPEX documentation