1 citations · 2 across the 2 of their papers we have counts for
4 papers
qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations
Zheyong Fan, Benrui Tang, Esmée Berger +13
Although electrostatics can be incorporated into machine-learned interatomic potentials, existing approaches are computationally very demanding, limiting large-scale, long-time sim…
Thermal Stabilization of Defect Charge States and Finite-Temperature Charge Transition Levels
Tobias Hainer, Ethan Berger, Esmée Berger +3
Point defects introduce localized electronic states that critically affect carrier trapping, recombination, and transport in functional materials. The associated charge transition…
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…