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wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and Related Atomistic Simulation Workflows
Elena Gelžinytė, Simon Wengert, Tamás K. Stenczel +4
Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle t…
Atomic structures and orbital energies of 61,489 crystal-forming organic molecules
Annika Stuke, Christian Kunkel, Dorothea Golze +5
Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets w…
A Practical Guide to Surface Kinetic Monte Carlo Simulations
Mie Andersen, Chiara Panosetti, Karsten Reuter
This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently…
Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects
Andreas Alvermann, Achim Basermann, Hans-Joachim Bungartz +30
We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications - Algorithmic Extensions and Optimizations) and ESSEX II…
Genarris: Random Generation of Molecular Crystal Structures and Fast Screening with a Harris Approximation
Xiayue Li, Farren S. Curtis, Timothy Rose +5
We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast e…