scholarly journals Accelerating the discovery of new materials with deep learning

IUCrJ ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Melanie Vollmar
Keyword(s):  
Deep Learning ◽  
New Materials

scholarly journals A general and transferable deep learning framework for predicting phase formation in materials

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Shuo Feng ◽  
Huadong Fu ◽  
Huiyu Zhou ◽  
Yuan Wu ◽  
Zhaoping Lu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Phase Formation ◽  
Periodic Table ◽  
Glass Forming Ability ◽  
High Entropy Alloys ◽  
New Materials ◽  
High Entropy ◽  
Learning Framework ◽  
Glass Forming ◽  
Small Dataset

AbstractMachine learning has been widely exploited in developing new materials. However, challenges still exist: small dataset is common for most tasks; new datasets, special descriptors and specific models need to be built from scratch when facing a new task; knowledge cannot be readily transferred between independent models. In this paper we propose a general and transferable deep learning (GTDL) framework for predicting phase formation in materials. The proposed GTDL framework maps raw data to pseudo-images with some special 2-D structure, e.g., periodic table, automatically extracts features and gains knowledge through convolutional neural network, and then transfers knowledge by sharing features extractors between models. Application of the GTDL framework in case studies on glass-forming ability and high-entropy alloys show that the GTDL framework for glass-forming ability outperformed previous models and can correctly predicted the newly reported amorphous alloy systems; for high-entropy alloys the GTDL framework can discriminate five types phases (BCC, FCC, HCP, amorphous, mixture) with accuracy and recall above 94% in fivefold cross-validation. In addition, periodic table knowledge embedded in data representations and knowledge shared between models is beneficial for tasks with small dataset. This method can be easily applied to new materials development with small dataset by reusing well-trained models for related materials.

Deep Learning in Personalization of Cardiovascular Stents

2019 ◽  
Vol 25 (2) ◽  
pp. 110-120 ◽  
Author(s):  
Yugyung Lee ◽  
Krishna Veerubhotla ◽  
Myung Ho Jeong ◽  
Chi H. Lee
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Treatment Strategies ◽  
Review Article ◽  
New Materials ◽  
Cardiovascular Devices ◽  
Cardiovascular Stent ◽  
Cardiovascular Stents ◽  
Medical Technologies ◽  
Artery Disease

Deep learning (DL) application has demonstrated its enormous potential in accomplishing biomedical tasks, such as vessel segmentation, brain visualization, and speech recognition. This review article has mainly covered recent advances in the principles of DL algorithms, existing DL software, and designing strategies of DL models. Latest progresses in cardiovascular devices, especially DL-based cardiovascular stent used for angioplasty, differential and advanced diagnostic means, and the treatment outcomes involved with coronary artery disease (CAD), are discussed. Also presented is DL-based discovery of new materials and future medical technologies that will facilitate the development of tailored and personalized treatment strategies by identifying and forecasting individual impending risks of cardiovascular diseases.

A new type of defect structure in 124-superconducting oxide revealed by HREM

1991 ◽  
Vol 49 ◽  
pp. 1092-1093
Author(s):  
R. Sharma ◽  
B.L. Ramakrishna ◽  
N.N. Thadhani ◽  
D. Hianes ◽  
Z. Iqbal
Keyword(s):  
Shock Wave ◽  
Defect Structure ◽  
Neutron Radiation ◽  
Weak Links ◽  
Defect Structures ◽  
New Materials ◽  
New Type ◽  
Current Densities ◽  
Processing Techniques ◽  
Microstructural Studies

After materials with superconducting temperatures higher than liquid nitrogen have been prepared, more emphasis has been on increasing the current densities (Jc) of high Tc superconductors than finding new materials with higher transition temperatures. Different processing techniques i.e thin films, shock wave processing, neutron radiation etc. have been applied in order to increase Jc. Microstructural studies of compounds thus prepared have shown either a decrease in gram boundaries that act as weak-links or increase in defect structure that act as flux-pinning centers. We have studied shock wave synthesized Tl-Ba-Cu-O and shock wave processed Y-123 superconductors with somewhat different properties compared to those prepared by solid-state reaction. Here we report the defect structures observed in the shock-processed Y-124 superconductors.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Discussion on “New materials and processes in the field of engineering” at the Preston Section of the Institution

1936 ◽  
Vol 15 (12) ◽  
pp. 686
Author(s):  
Hodgson ◽  
Johnson ◽  
Skipper ◽  
Wilcock ◽  
Bailey ◽  
...  
Keyword(s):  
New Materials

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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