Materials discovery through machine learning formation energy
GGC Peterson, J Brgoch - Journal of Physics: Energy, 2021 - iopscience.iop.org
The budding field of materials informatics has coincided with a shift towards artificial
intelligence to discover new solid-state compounds. The steady expansion of repositories for …
intelligence to discover new solid-state compounds. The steady expansion of repositories for …
Caloric devices: A review on numerical modeling and optimization strategies
The discovery of giant caloric effects and further investigation on device systems that can
compete with current heat management technologies in terms of efficiency have led to a …
compete with current heat management technologies in terms of efficiency have led to a …
Comparative data-driven enhanced geothermal systems forecasting models: A case study of Qiabuqia field in China
Geothermal energy is gaining global attractiveness owing to its abundance and sustainable
nature. An in-depth understanding of potential geothermal production provides the energy …
nature. An in-depth understanding of potential geothermal production provides the energy …
Advanced Magnetocaloric Materials for Energy Conversion: Recent Progress, Opportunities, and Perspective
Solid‐state caloric effects as intrinsic thermal responses to different physical external stimuli
(magnetic‐, uniaxial stress‐, pressure‐, and electric‐fields) can achieve a higher energy …
(magnetic‐, uniaxial stress‐, pressure‐, and electric‐fields) can achieve a higher energy …
A numerical modelling of a multi-layer LaFeCoSi Active magnetic regenerator by using Artificial Neural Networks
A Maiorino, MG Del Duca, U Tomc, J Tušek… - Applied Thermal …, 2021 - Elsevier
One of the main problems in the framework of magnetic refrigeration regards low adiabatic
temperature changes that occur in the magnetocaloric materials, which limits the …
temperature changes that occur in the magnetocaloric materials, which limits the …
Examining the role of passive design indicators in energy burden reduction: Insights from a machine learning and deep learning approach
Passive design characteristics (PDC) play a pivotal role in reducing the energy burden on
households without imposing additional financial constraints on project stakeholders …
households without imposing additional financial constraints on project stakeholders …
System-level multi-objective optimization of a magnetic air conditioner through coupling of artificial neural networks and genetic algorithms
This work advances an integrated approach to design the components of a magnetocaloric
air conditioner capable of producing a cooling capacity of 9000 BTU h− 1 (2637 W) for …
air conditioner capable of producing a cooling capacity of 9000 BTU h− 1 (2637 W) for …
Influence of the interfacial thermal resistance of a gadolinium/copper bimetal composite on solid-state magnetic refrigeration
B Lu, Y Huang, J Huang, Z Ma, J Wang, J He - International Journal of …, 2023 - Elsevier
The low heat transfer efficiency caused by a magnetocaloric material (MCM) with low
thermal conductivity is the bottleneck that limits the performance of magnetic refrigeration …
thermal conductivity is the bottleneck that limits the performance of magnetic refrigeration …
Measurement and verification of energy performance for chiller system retrofit with k nearest neighbour regression
There is no generic performance indicator for chiller systems running in different cooling
load profiles. This study applies the k nearest neighbour (kNN) regression to analyse the pre …
load profiles. This study applies the k nearest neighbour (kNN) regression to analyse the pre …
Accelerated design of MTX alloys with targeted magnetostructural properties through interpretable machine learning
Data-driven machine learning (ML) models are developed to rapidly predict the
magnetostructural transition temperature (T t) and thermal hysteresis in the vast search …
magnetostructural transition temperature (T t) and thermal hysteresis in the vast search …