Franckeite
1 dataset, together covering 0.444–0.7776 µm. Default: Gant et al. 2017: n,k 0.444–0.778 µm.
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Datasets
Use it in Lightsmith
Load this data in Python, simulate in one more line
pip install lightsmith
import lightsmith as ls
# Franckeite: Gant et al. 2017: n,k 0.444–0.778 µm
franckeite = ls.fetch_refractiveindex("other", "franckeite", "Gant", ref="c5c2f188e848453def5970e347399d653df2ffc2")
print(franckeite.range_nm) # (443.974, 777.601) nm
eps = franckeite.eps([633]) # complex permittivity
Downloads the original record once (pinned to the imported snapshot) and caches it.
import json, urllib.request
import lightsmith as ls
url = "https://materials.lightsmith.dev/v1/datasets/ds_6680999c75a7/lightsmith"
franckeite = ls.Tabulated.model_validate(json.load(urllib.request.urlopen(url)))
print(franckeite.source) # citation and provenance travel with the data
Any dataset here, as the library's Tabulated material (franckeite/gant-2017).
# ... load franckeite as above, then:
sim = ls.Simulation(
particles=[ls.Particle(shape=ls.Sphere(radius_nm=50), material=franckeite)],
medium_index=1.0,
wavelengths=ls.WavelengthRange(start_nm=444, stop_nm=777, num=81),
)
res = ls.solve(sim) # exact Mie for a sphere
print(res.q_ext) # extinction efficiency at each wavelength
Same spec runs with DDA, FDTD, BEM or T-matrix: ls.compare(sim, ["mie", "dda"]).
Lightsmith is open source (MIT): Mie, T-matrix, BEM, DDA, FDTD, RCWA, TMM, mode solvers and inverse design behind one spec, on CPU or GPU. GitHub.
About Franckeite
Chemical formula: Pb5Sn3Sb2S14
API: /v1/materials/mat_47dad8e28384 · slug franckeite