203 citations · 301 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra
Nan Xu, Petter Rosander, Christian Schäfer +7
Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, and solids, as the spectra contain a wealth of information concerning in particular the…
Machine Learning for Polaritonic Chemistry: Accessing chemical kinetics
Christian Schäfer, Jakub Fojt, Eric Lindgren +1
Altering chemical reactivity and material structure in confined optical environments is on the rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusi…
General-purpose machine-learned potential for 16 elemental metals and their alloys
Keke Song, Rui Zhao, Jiahui Liu +25
Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicabil…