Blar i SINTEF Industri på emneord "Machine learning"
Viser treff 1-6 av 6
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Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis
(Peer reviewed; Journal article, 2021)Low lattice thermal conductivity is essential for high thermoelectric performance of a material. Lattice thermal conductivity is often computed using density functional theory (DFT), typically at a high computational cost. ... -
Magnesiothermic Reduction of Silica: A Machine Learning Study
(Journal article; Peer reviewed, 2023)undamental studies have been carried out experimentally and theoretically on the magnesiothermic reduction of silica with different Mg/SiO2 molar ratios (1–4) in the temperature range of 1073 to 1373 K with different ... -
On the value of popular crystallographic databases for machine learning prediction of space groups
(Peer reviewed; Journal article, 2022)Predicting crystal structure information is a challenging problem in materials science that clearly benefits from artificial intelligence approaches. The leading strategies in machine learning are notoriously data-hungry ... -
Reduced well path parameterization for optimization problems through machine learning
(Peer reviewed; Journal article, 2021)In this work we apply a recently developed machine learning routine for automatic well planning to simplify well parameterization in reservoir simulation models. This reduced-order parameterization is shown to be beneficial ... -
The evolution of precipitate crystal structures in an Al-Mg-Si(-Cu) alloy studied by a combined HAADF-STEM and SPED approach
(Journal article; Peer reviewed, 2018)This work presents a detailed investigation into the effect of a low Cu addition (0.01 at.%) on precipitation in an Al-0.80Mg-0.85Si alloy during ageing. The precipitate crystal structures were assessed by scanning ... -
Towards high-throughput microstructure simulation in compositionally complex alloys via machine learning
(Peer reviewed; Journal article, 2021)The coupling of computational thermodynamics and kinetics has been the central research theme in Integrated Computational Material Engineering (ICME). Two major bottlenecks in implementing this coupling and performing ...