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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…
Revealing the Low Temperature Phase of FAPbI using A Machine-Learned Potential
Sangita Dutta, Erik Fransson, Tobias Hainer +4
FAPbI is a material of interest for its potential in solar cell applications, driven by its remarkable optoelectronic properties. However, the low-temperature phase of FAPbI$_3…
A Morphotropic Phase Boundary in MAFAPbI: Linking Structure, Dynamics, and Electronic Properties
Tobias Hainer, Erik Fransson, Sangita Dutta +2
Understanding the phase behavior of mixed-cation halide perovskites is critical for optimizing their structural stability and optoelectronic performance. Here, we map the phase dia…
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…
Octahedral tilt-driven phase transitions in BaZrS chalcogenide perovskite
Prakriti Kayastha, Erik Fransson, Paul Erhart +1
Chalcogenide perovskites are lead-free materials for potential photovoltaic or thermoelectric applications. BaZrS is the most studied member of this family due to its superior…