An ab initio insight into the structural, physical, thermodynamic and optoelectronic properties of superconducting Heusler-like LiGa2Rh

Mondal, Prianka; Khanom, Shamima; Shahed, Naafis Ahnaf; Hossain, Md. Kamal; Ahmed, Farid

doi:10.1016/j.physc.2022.1354142

Cited by 10 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 , we also display a sample clustering result from the “others” version of conditional SuperDiff conditioned on various compounds. As seen in the plot, we identified two new clusters: , which was generated by conditioning SuperDiff on 33 , and , which was generated by conditioning SuperDiff on 34 . Those and other predicted families will be discussed in more detail below.…”

Section: Resultsmentioning

confidence: 99%

Diffusion models for conditional generation of hypothetical new families of superconductors

Yuan,

Dordevic

2024

Sci Rep

View full text Add to dashboard Cite

Effective computational search holds great potential for aiding the discovery of high-temperature superconductors (HTSs), especially given the lack of systematic methods for their discovery. Recent progress has been made in this area with machine learning, especially with deep generative models, which have been able to outperform traditional manual searches at predicting new superconductors within existing superconductor families but have yet to be able to generate completely new families of superconductors. We address this limitation by implementing conditioning—a method to control the generation process—for our generative model and develop SuperDiff, a denoising diffusion probabilistic model with iterative latent variable refinement conditioning for HTS discovery—the first deep generative model for superconductor discovery with conditioning on reference compounds. With SuperDiff, by being able to control the generation process, we were able to computationally generate completely new families of hypothetical superconductors for the very first time. Given that SuperDiff also has relatively fast training and inference times, it has the potential to be a very powerful tool for accelerating the discovery of new superconductors and enhancing our understanding of them.

show abstract

Section: Resultsmentioning

confidence: 99%

Diffusion models for conditional generation of hypothetical new families of superconductors

Yuan,

Dordevic

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

Nanoscale Synergy: Unveiling Gas Sensing Paradigms Through First Principles Exploration of Vertical Hexagonal Boron Nitride/Graphene Heterostructures

Mondal,

Khanom,

Ahmed

2024

Physica Status Solidi (b)

View full text Add to dashboard Cite

Modern civilization relies on rapid and accurate gas molecule identification at low concentrations for environmental surveillance, civilian security, healthcare evaluation, and inside atmospheric control. Density functional theory simulations are used to study the adsorption of CH4, CO2, PH3, N2O, NH3, O3, and SO2 gas molecules on a vertical hexagonal boron nitride/graphene heterostructure using the CASTEP code. Negative adsorption energy is only detected for NH3, O3, and SO2 gases. Our adsorption energy measurements show that the heterostructure is sensitive to NH3, O3, and SO2 gas molecules but insensitive to other gas molecules, as shown by the positive results. Further electrical and optical properties are only performed on gas‐adsorbed structures. It is found that the pure heterostructure acts like a semiconductor and has a band gap of 1.742 eV, which changes when gas is absorbed. Each of the structures has high absorption coefficients in the UV region, making them promising candidates for optoelectronic devices. The determination of the adsorbed gas type can be achieved by measuring the peak intensity and peak position of reflection or absorption. Heterostructures exhibit superior sensing capabilities for NH3 compared to other gases because of their enhanced adsorption energy, distinctive electrical band gap, and notable optical characteristics.

show abstract

Predictive study of the new double Half-Heusler compounds Hf2FeNiSb2, Nb2Co2GaSb and ScNbCo2Sb2, promising candidates for thermoelectric applications

Mekhtiche,

Matougui,

Houari

et al. 2024

Indian J Phys

View full text Add to dashboard Cite

An ab initio insight into the structural, physical, thermodynamic and optoelectronic properties of superconducting Heusler-like LiGa2Rh

Cited by 10 publications

References 50 publications

Diffusion models for conditional generation of hypothetical new families of superconductors

Diffusion models for conditional generation of hypothetical new families of superconductors

Nanoscale Synergy: Unveiling Gas Sensing Paradigms Through First Principles Exploration of Vertical Hexagonal Boron Nitride/Graphene Heterostructures

Predictive study of the new double Half-Heusler compounds Hf2FeNiSb2, Nb2Co2GaSb and ScNbCo2Sb2, promising candidates for thermoelectric applications

Contact Info

Product

Resources

About