hcp → ω phase transition mechanisms in shocked zirconium: A machine learning based atomic simulation study

We report the presence of large proportions of ω-phase in Ti-Hf-Zr alloys, prepared using the hydride cycle technique. We show that the ω-phase extends across the concentration triangle of Ti-Zr-Hf and report the partitioning of the three metals across the two sites in this structure from neutron and X-ray data. We examine the symmetry of the order parameter governing the β–ω phase transition and show that a two-step model for the phase transition involving site ordering followed by displacement is not likely to be correct. We suggest that an interstitial solid solution of oxygen and octahedral vacancies exists, and that the observation of any ω-phase diffraction pattern of alloys of these metals at ambient temperature and pressure should be viewed as a potential sign of the presence of oxygen in the octahedral interstitial sites.

Download Full-text

Decoupling Mesoscale Functional Response in PLZT across the Ferroelectric–Relaxor Phase Transition with Contact Kelvin Probe Force Microscopy and Machine Learning

ACS Applied Materials & Interfaces ◽

10.1021/acsami.8b15872 ◽

2018 ◽

Vol 10 (49) ◽

pp. 42674-42680 ◽

Cited By ~ 4

Author(s):

Sabine M. Neumayer ◽

Liam Collins ◽

Rama Vasudevan ◽

Christopher Smith ◽

Suhas Somnath ◽

...

Keyword(s):

Machine Learning ◽

Phase Transition ◽

Functional Response ◽

Kelvin Probe Force Microscopy ◽

Kelvin Probe ◽

Ferroelectric Relaxor ◽

Force Microscopy

Download Full-text

An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts

Entropy ◽

10.3390/e21101015 ◽

2019 ◽

Vol 21 (10) ◽

pp. 1015 ◽

Cited By ~ 3

Author(s):

Carles Bretó ◽

Priscila Espinosa ◽

Penélope Hernández ◽

Jose M. Pavía

Keyword(s):

Machine Learning ◽

Gross Domestic Product ◽

Maximum Entropy ◽

Simulation Study ◽

Learning Algorithm ◽

Predictive Ability ◽

Learning Approach ◽

Machine Learning Algorithm ◽

Machine Learning Approach ◽

Maximum Entropy Inference

This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.

Download Full-text

Atomistic Simulation Study of the Order/Disorder (Monoclinic to Hexagonal) Phase Transition of Hydroxyapatite.

ChemInform ◽

10.1002/chin.200525002 ◽

2005 ◽

Vol 36 (25) ◽

Author(s):

Oliver Hochrein ◽

Ruediger Kniep ◽

Dirk Zahn

Keyword(s):

Phase Transition ◽

Simulation Study ◽

Atomistic Simulation ◽

Hexagonal Phase

Download Full-text

Layer dependence of graphene-diamene phase transition in epitaxial and exfoliated few-layer graphene using machine learning

2D Materials ◽

10.1088/2053-1583/ab1b9f ◽

2019 ◽

Vol 6 (3) ◽

pp. 035043 ◽

Cited By ~ 8

Author(s):

Filippo Cellini ◽

Francesco Lavini ◽

Claire Berger ◽

Walt de Heer ◽

Elisa Riedo

Keyword(s):

Machine Learning ◽

Phase Transition ◽

Layer Graphene ◽

Few Layer Graphene

Download Full-text

Estimation of Pregnant Women Possibly Left Behind in Tsunami Predicted Areas of Nagoya City: A Simulation for Rescue Planning

Disaster Medicine and Public Health Preparedness ◽

10.1017/dmp.2019.164 ◽

2020 ◽

pp. 1-8

Author(s):

Ritsuko Hattori ◽

Shoko Miyagawa ◽

Kanetoshi Hattori

Keyword(s):

Machine Learning ◽

Pregnant Women ◽

Simulation Study ◽

Nankai Trough ◽

Learning Model ◽

Center Line ◽

Left Behind ◽

Machine Learning Model ◽

The City

ABSTRACT Objective: In case of an outbreak of Nankai Trough Mega-earthquake, it is predicted that a tsunami would invade Nagoya City within 100 minutes, hitting about one third of the City of Nagoya. If the administrative plan of the city and midwives’ expertise are coordinated, pregnant women’s chances of survival will increase. The authors carried out this simulation study in an attempt to improve consistency of the two efforts. Method: We estimated the number of pregnant women using a machine learning model. The evacuation distance of pregnant women was estimated on the basis of the data of road center line. Results: Through this simulation study, it became clear that preparation for approximately 2600 pregnant women escaping from tsunami predicted area and for about 1200 pregnant women possibly left in the area is needed. Conclusions: Our study suggests that triage point planning is needed in areas where pregnant women are evacuated. The triage makes it possible to transport women to appropriate hospitals.

Download Full-text