Drawing phase diagrams of random quantum systems by deep learning the wave functions
T Ohtsuki, T Mano - Journal of the Physical Society of Japan, 2020 - journals.jps.jp
Applications of neural networks to condensed matter physics are becoming popular and
beginning to be well accepted. Obtaining and representing the ground and excited state …
beginning to be well accepted. Obtaining and representing the ground and excited state …
Developing new electrocatalysts for oxygen evolution reaction via high throughput experiments and artificial intelligence
The development of non-noble metal electrocatalysts for the Oxygen Evolution Reaction
(OER) is advancing towards the use of multi-element materials. To reveal the complex …
(OER) is advancing towards the use of multi-element materials. To reveal the complex …
Understanding and optimization of hard magnetic compounds from first principles
T Miyake, Y Harashima, T Fukazawa… - Science and Technology …, 2021 - Taylor & Francis
First-principles calculation based on density functional theory is a powerful tool for
understanding and designing magnetic materials. It enables us to quantitatively describe …
understanding and designing magnetic materials. It enables us to quantitatively describe …
Impact of oxidation morphology on reduced graphene oxides upon thermal annealing
Thermal reduction of graphene oxide (GO) is an essential technique to produce low-cost
and higher quality graphene-based materials and composites used today in a plethora of …
and higher quality graphene-based materials and composites used today in a plethora of …
Predicting the Curie temperature of Sm-Co-based alloys via data-driven strategy
G Xu, F Cheng, H Lu, C Hou, X Song - Acta Materialia, 2024 - Elsevier
Calculating the Curie temperature of rare-earth permanent magnetic materials has remained
a big theoretical challenge. In this study, based on a home-built Sm-Co-based alloys …
a big theoretical challenge. In this study, based on a home-built Sm-Co-based alloys …
Predicting the Curie temperature in substitutionally disordered alloys using a first-principles based model
MA Brännvall, R Armiento, B Alling - arXiv preprint arXiv:2412.04920, 2024 - arxiv.org
When exploring new magnetic materials, the effect of alloying plays a crucial role for
numerous properties. By altering the alloy composition, it is possible to tailor, eg, the Curie …
numerous properties. By altering the alloy composition, it is possible to tailor, eg, the Curie …
Linear and nonlinear machine learning correlation of transition metal cluster characteristics
The correlation between the properties of fourth-row transition element small clusters is
studied using linear and nonlinear machine learning (ML) methods. The feature space, or …
studied using linear and nonlinear machine learning (ML) methods. The feature space, or …
Evidence-based data mining method to reveal similarities between materials based on physical mechanisms
Measuring the similarity between materials is essential for estimating their properties and
revealing the associated physical mechanisms. However, current methods for measuring the …
revealing the associated physical mechanisms. However, current methods for measuring the …
Development of hard-magnetic materials by first-principles calculation and materials informatics
T Miyake - JSAP Review, 2023 - jstage.jst.go.jp
Research on permanent magnets [1] has a long history, but has intensified more over the
last decade. The purpose of such research is to create strong magnets, the performance of …
last decade. The purpose of such research is to create strong magnets, the performance of …
Influence of Al on the Microstructure and Hardness of High Manganese Steel
This article is presented on the influence of Aluminium on the microstructure and hardness
of high manganese steel. By calculating the stack fault energy, it has been determined that …
of high manganese steel. By calculating the stack fault energy, it has been determined that …