Machine learning for metallurgy IV: A neural network potential for Al-Cu-Mg and Al-Cu-Mg-Zn

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Materials for high -temperature environments are actively being investigated for deployment in aerospace and nuclear applications. This study uses computational approaches to unravel the crystallography and thermodynamics of a promising class of refractory ...

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The microstructure evolution associated with the cold forming sequence of an Fe-14Cr-1W-0.3Ti-0.3Y2O3 grade ferritic stainless steel strengthened by dispersion of nano oxides (ODS) was investigated. The material, initially hot extruded at 1100 degrees C an ...

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Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

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Machine learning for metallurgy IV: A neural network potential for Al-Cu-Mg and Al-Cu-Mg-Zn

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Solute-strengthening in metal alloys with short-range order

First-principles thermodynamics of precipitation in aluminum-containing refractory alloys

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Composition and Element Distribution Mapping of γ′ and γ" Phases of Inconel 718 by High-Resolution Scanning Transmission Electron Microscopy and X-ray Energy-Dispersive Spectrometry

Efficient and insightful descriptors for representing molecular and material space

Dislocation Hardening in a New Manufacturing Route of Ferritic Oxide Dispersion-Strengthened Fe-14Cr Cladding Tube

Fast adaptation to rule switching using neuronal surprise

Surface segregation in high-entropy alloys from alchemical machine learning

Robust machine learning for neuroscientific inference

Digital Twins of Stone Masonry Buildings for Damage Assessment

Topics in statistical physics of high-dimensional machine learning

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Hydrogen-induced tunable remanent polarization in a perovskite nickelate

Partial discharge localization in power transformer tanks using machine learning methods

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Random matrix methods for high-dimensional machine learning models

Experimental and ab initio derivation of interface stress in nanomultilayered coatings: Application to immiscible Cu/W system with variable in-plane stress

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis