Geometrical Spin Frustration and Monoclinic-Distortion-Induced Spin Canting in the Double Perovskites Ln2LiFeO6 (Ln = La, Nd, Sm, and Eu) with Unusually High Valence Fe5+

2021 ◽  
Author(s):  
Masato Goto ◽  
Tamio Oguchi ◽  
Yuichi Shimakawa
Keyword(s):  
Double Perovskites ◽  
Spin Frustration ◽  
Spin Canting ◽  
Monoclinic Distortion

A Kagomé layer-based 3D MnII framework showing coexistence of spin-canting, spin-frustration, field-induced metamagnetic and spin-flop transitions

2011 ◽  
Vol 40 (34) ◽  
pp. 8513 ◽  
Author(s):  
En-Cui Yang ◽  
Zhong-Yi Liu ◽  
Ya-Ling Li ◽  
Jing-Yi Wang ◽  
Xiao-Jun Zhao
Keyword(s):  
Spin Frustration ◽  
Spin Canting

In the Quest for a Stable Triplet State in Small Polyaromatic Hydrocarbons : An In-Silico Tool for Rational Design and Prediction

2019 ◽  
Author(s):  
Madhumita Rano ◽  
Sumanta K Ghosh ◽  
Debashree Ghosh
Keyword(s):  
Triplet State ◽  
Small Molecules ◽  
In Silico ◽  
Qualitative Agreement ◽  
Rational Design ◽  
Polyaromatic Hydrocarbons ◽  
Spin Hamiltonian ◽  
Spin Frustration ◽  
Computationally Efficient ◽  
High Level

<div>Combining the roles of spin frustration and geometry of odd and even numbered rings in polyaromatic hydrocarbons (PAHs), we design small molecules that show exceedingly small singlet-triplet gaps and stable triplet ground states. Furthermore, a computationally efficient protocol with a model spin Hamiltonian is shown to be capable of qualitative agreement with respect to high level multireference calculations and therefore, can be used for fast molecular discovery and screening.</div>

scholarly journals Machine learning for perovskite materials design and discovery

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Qiuling Tao ◽  
Pengcheng Xu ◽  
Minjie Li ◽  
Wencong Lu
Keyword(s):  
Machine Learning ◽  
Technological Innovation ◽  
Future Development ◽  
Rational Design ◽  
Driving Forces ◽  
Scientific Progress ◽  
Double Perovskites ◽  
Perovskite Materials ◽  
Development Tendency ◽  
Development Prospects

AbstractThe development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

Machine-learning-assisted prediction of magnetic double perovskites

2019 ◽  
Vol 3 (8) ◽  
Author(s):  
Anita Halder ◽  
Aishwaryo Ghosh ◽  
Tanusri Saha Dasgupta
Keyword(s):  
Machine Learning ◽  
Double Perovskites

scholarly journals Electronic and optical properties of vacancy ordered double perovskites A2BX6 (A = Rb, Cs; B = Sn, Pd, Pt; and X = Cl, Br, I): a first principles study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Faizan ◽  
K. C. Bhamu ◽  
Ghulam Murtaza ◽  
Xin He ◽  
Neeraj Kulhari ◽  
...  
Keyword(s):  
Optical Properties ◽  
Band Gap ◽  
First Principles ◽  
Density Functional ◽  
Perovskite Solar Cells ◽  
Dielectric Constants ◽  
Maximum Efficiency ◽  
Exchange Potential ◽  
Double Perovskites ◽  
Electronic And Optical Properties

AbstractThe highly successful PBE functional and the modified Becke–Johnson exchange potential were used to calculate the structural, electronic, and optical properties of the vacancy-ordered double perovskites A2BX6 (A = Rb, Cs; B = Sn, Pd, Pt; X = Cl, Br, and I) using the density functional theory, a first principles approach. The convex hull approach was used to check the thermodynamic stability of the compounds. The calculated parameters (lattice constants, band gap, and bond lengths) are in tune with the available experimental and theoretical results. The compounds, Rb2PdBr6 and Cs2PtI6, exhibit band gaps within the optimal range of 0.9–1.6 eV, required for the single-junction photovoltaic applications. The photovoltaic efficiency of the studied materials was assessed using the spectroscopic-limited-maximum-efficiency (SLME) metric as well as the optical properties. The ideal band gap, high dielectric constants, and optimum light absorption of these perovskites make them suitable for high performance single and multi-junction perovskite solar cells.

Sign in / Sign up

Export Citation Format

Share Document