19 citations · 19 across the 1 of their papers we have counts for
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NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
Ting Liang, Ke Xu, Eric Lindgren +16
While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting the…
Predicting neutron experiments from first principles: A workflow powered by machine learning
Eric Lindgren, Adam J. Jackson, Erik Fransson +6
Machine learning has emerged as a powerful tool in materials discovery, enabling the rapid design of novel materials with tailored properties for countless applications, including…
Dynasor 2: From Simulation to Experiment Through Correlation Functions
Esmée Berger, Erik Fransson, Fredrik Eriksson +4
Correlation functions, such as static and dynamic structure factors, offer a versatile approach to analyzing atomic-scale structure and dynamics. By having access to the full dynam…
Probing Glass Formation in Perylene Derivatives via Atomic Scale Simulations and Bayesian Regression
Eric Lindgren, Jan Swensson, Christian Müller +1
While the structural dynamics of chromophores are of interest for a range of applications, it is experimentally very challenging to resolve the underlying microscopic mechanisms. G…