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10 papers
Pushing the limits of unconstrained machine-learned interatomic potentials
Filippo Bigi, Paolo Pegolo, Arslan Mazitov +2
Machine-learned interatomic potentials (MLIPs) are increasingly used to replace computationally demanding electronic-structure calculations to model matter at the atomic scale. The…
A universal machine learning model for the electronic density of states
Wei Bin How, Pol Febrer, Sanggyu Chong +5
In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbit…
Giant exciton binding energy in bulk CrCl3
Georgy Ermolaev, Tagir Mazitov, Anton Minnekhanov +20
Van der Waals (vdW) materials, with their unique combination of electronic, optical, and magnetic properties, are emerging as promising platforms for exploring excitonic phenomena.…
Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
Filippo Bigi, Joseph W. Abbott, Philip Loche +12
Incorporation of machine learning (ML) techniques into atomic-scale modeling has proven to be an extremely effective strategy to improve the accuracy and reduce the computational c…
Giant optical anisotropy and visible-frequency epsilon-near-zero in hyperbolic van der Waals MoOCl2
Georgy Ermolaev, Adilet Toksumakov, Aleksandr Slavich +14
The realization of extreme optical anisotropy is foundational to nanoscale light manipulation. Van der Waals (vdW) crystal MoOCl2 has emerged as a promising candidate for this ques…
Record Index-Bandgap Trade-off: CdPS3 as a High-Index van der Waals Platform for Ultraviolet-Visible Nanophotonics
M. R. Povolotskiy, A. S. Slavich, G. A. Ermolaev +21
The development of nanophotonics is hindered by a fundamental trade-off between a material's refractive index (n) and its electronic bandgap (Eg), which severely restricts the choi…