scholarly journals Magnetic State Generation using Hamiltonian Guided Variational Autoencoder with Spin Structure Stabilization

2021 ◽  
pp. 2004795
Author(s):  
Hee Young Kwon ◽  
Han Gyu Yoon ◽  
Sung Min Park ◽  
Doo Bong Lee ◽  
Jun Woo Choi ◽  
...  
Keyword(s):  
Spin Structure ◽  
Magnetic State ◽  
Variational Autoencoder ◽  
State Generation ◽  
Structure Stabilization

Correlation between Ionic Radius of Substituting Element and Magnetic Properties of Bi1-xAxFeO3-x/2 (A= Ca, Sr, Pb, Ba) Multiferroics

2009 ◽  
Vol 152-153 ◽  
pp. 131-134 ◽  
Author(s):  
V.A. Khomchenko ◽  
Michael Kopcewicz ◽  
Armandina M. Lima Lopes ◽  
Yuri G. Pogorelov ◽  
J.P. Araujo ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Magnetic Properties ◽  
Spin Structure ◽  
Ionic Radius ◽  
Oxygen Vacancies ◽  
Magnetic State ◽  
Weak Ferromagnetism ◽  
Host Lattice ◽  
A Site ◽  
Spiral Spin

Investigation of crystal structure and magnetic properties of the diamagnetically- substituted Bi1-xAxFeO3-x/2 (A= Ca, Sr, Pb, Ba; x= 0.2, 0.3) polycrystalline samples has been carried out. It has been shown that the heterovalent A2+ substitution result in the formation of oxygen vacancies in the host lattice. The solid solutions have been found to possess a rhombohedrally distorted perovskite structure described by the space group R3c. Magnetization measurements have shown that the magnetic state of these compounds is determined by the ionic radius of the substituting elements. A-site substitution with the biggest ionic radius ions has been found to suppress the spiral spin structure of BiFeO3 giving rise to the appearance of weak ferromagnetism.

Interaction of ditertiary phosphines with phenyl- and 1-naphthyldithiocarbamatonickel(II)

1985 ◽  
Vol 50 (6) ◽  
pp. 1383-1390
Author(s):  
Aref A. M. Aly ◽  
Ahmed A. Mohamed ◽  
Mahmoud A. Mousa ◽  
Mohamed El-Shabasy
Keyword(s):  
Thermal Analysis ◽  
Powder Diffraction ◽  
Spin Structure ◽  
Magnetic Measurements ◽  
Mixed Ligand ◽  
Mixed Ligand Complexes ◽  
X Ray ◽  
Square Planar

The synthesis of the following mixed ligand complexes is reported: [Ni(phdtc)2(dpm)2], [Ni(phdtc)2(dpe)2], [Ni(phdtc)2(dpp)3], [Ni(1-naphdtc)2(dpm)2], [Ni(1-naphdtc)2], and [Ni(1-naphdtc)2(dpp)2], where phdtc = PhNHCSS-, 1-naphdtc = 1-NaPhNHCSS-, dpm = Ph2PCH2PPh2, dpe = Ph2P(CH2)2PPh2, and dpp = Ph2P(CH2)3PPh2. The complexes are characterised by microanalysis, IR and UV-Vis spectra, magnetic measurements, conductivity, X-ray powder diffraction, and thermal analysis. All the mixed ligand complexes are diamagnetic, and thus a square-planar or square-pyramidal (low-spin) structure was proposed for the present complexes.

scholarly journals Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Osman Mamun ◽  
Madison Wenzlick ◽  
Arun Sathanur ◽  
Jeffrey Hawk ◽  
Ram Devanathan
Keyword(s):  
Pearson Correlation ◽  
Rupture Life ◽  
Model Performance ◽  
Austenitic Stainless Steels ◽  
Generative Model ◽  
Austenitic Steels ◽  
Gradient Boosting ◽  
Variational Autoencoder ◽  
Feature Importance ◽  
Boosting Algorithm

AbstractThe Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability and dependence on the constant C often result in sub-optimal performance. In this work, we show that the direct rupture life parameterization without intermediate LMP parameterization, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pearson correlation coefficient >0.9 for 9–12% Cr and >0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Finally, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9–12% Cr ferritic–martensitic alloys and austenitic stainless steel datasets.

Sign in / Sign up

Export Citation Format

Share Document