Bridging laboratory findings and artificial intelligence for the design of TlInTe2 crystals

In our laboratory a specially designed Bridgman technique was utilized to pre- pare single crystals of TlInTe2. The structure of TlInTe2 in powder form was examined by X-ray diffraction, revealing the lattice parameters of a = 8.494 Å and c = 7.181 Å. The optical properties of single-layered crystals of TlInTe2 were studied using spectrophotometric transmittance and reflectance measure- ments in the wavelength range of 200-2500 nm. The dispersion curve of the refractive index showed anomalous dispersion in the absorption region and nor- mal dispersion in the transmitted region. The direct band gap was determined to be 2.08 eV. Micro-Raman measurements were carried out at room temperature using a Dilor XY800 triple monochromator with a CCD camera and a Kr+ laser (λexc = 647.1 nm). The laser power was reduced to 1-8 mW to prevent rapid changes in the Raman spectrum, allowing observation of photoinduced spec- tral dynamics. Micro-Raman spectroscopy provides a powerful non-destructive method to investigate the vibrational properties of TlInTe2, offering valuable insights into its structural properties for specific applications in photonics and optoelectronic devices. By careful analysis and comparison with theoretical mod- els and other materials in the same class, we gain a deep understanding of TlInTe2's potential and limitations. Furthermore, this study integrates machine learning (ML) techniques to improve understanding and prediction of TlInTe2's. Optical properties. Using synthetic data, Random Forest models were trained to predict optical parameters such as refractive index and absorption coeffi- cient with near-perfect accuracy. These ML models significantly reduce the need for extensive experimental measurements and offer a powerful tool for optimiz- ing the performance of the material in optoelectronic applications. The findings underscore the growing importance of combining ML with materials science to accelerate discoveries and advances in semiconductors such as TlInTe2.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-05-21
DOI
https://doi.org/10.1038/s41598-026-44965-9
Primary Topic
Solid-state spectroscopy and crystallography
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Bridging laboratory findings and artificial intelligence for the design of TlInTe2 crystals

Mohamed M. Fangary, F. Gami, Muhammad A. O. Ahmed, Hessah Alotaibi
Scientific Reports
Solid-state spectroscopy and crystallography
article

Bridging laboratory findings and artificial intelligence for the design of TlInTe2 crystals

Mohamed M. Fangary, F. Gami, Muhammad A. O. Ahmed, Hessah Alotaibi
article en

Abstract

In our laboratory a specially designed Bridgman technique was utilized to pre- pare single crystals of TlInTe2. The structure of TlInTe2 in powder form was examined by X-ray diffraction, revealing the lattice parameters of a = 8.494 Å and c = 7.181 Å. The optical properties of single-layered crystals of TlInTe2 were studied using spectrophotometric transmittance and reflectance measure- ments in the wavelength range of 200-2500 nm. The dispersion curve of the refractive index showed anomalous dispersion in the absorption region and nor- mal dispersion in the transmitted region. The direct band gap was determined to be 2.08 eV. Micro-Raman measurements were carried out at room temperature using a Dilor XY800 triple monochromator with a CCD camera and a Kr+ laser (λexc = 647.1 nm). The laser power was reduced to 1-8 mW to prevent rapid changes in the Raman spectrum, allowing observation of photoinduced spec- tral dynamics. Micro-Raman spectroscopy provides a powerful non-destructive method to investigate the vibrational properties of TlInTe2, offering valuable insights into its structural properties for specific applications in photonics and optoelectronic devices. By careful analysis and comparison with theoretical mod- els and other materials in the same class, we gain a deep understanding of TlInTe2's potential and limitations. Furthermore, this study integrates machine learning (ML) techniques to improve understanding and prediction of TlInTe2's. Optical properties. Using synthetic data, Random Forest models were trained to predict optical parameters such as refractive index and absorption coeffi- cient with near-perfect accuracy. These ML models significantly reduce the need for extensive experimental measurements and offer a powerful tool for optimiz- ing the performance of the material in optoelectronic applications. The findings underscore the growing importance of combining ML with materials science to accelerate discoveries and advances in semiconductors such as TlInTe2.

Scientific ReportsVol. 16(1)
South Valley University (EG), Shaqra University (SA), Luxun Academy of Fine Arts (CN), Lux Research (United States) (US)
Science and Technology Development Fund
Life in Land
Openalex Percentile: Top 12%
Solid-state spectroscopy and crystallography
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.