Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

Adaptive characterization of microstructure dataset using a two stage machine learning approach

(2020) Arun Baskaran et al.   COMPUTATIONAL MATERIALS SCIENCE

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

(2020) Elizabeth Kautz et al.   MATERIALS CHARACTERIZATION

High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel

(2019) Brian L. DeCost et al.   MICROSCOPY AND MICROANALYSIS

A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

(2019) Elizabeth J. Kautz et al.   COMPUTATIONAL MATERIALS SCIENCE

Nanoscale Spatially Resolved Mapping of Uranium Enrichment

(2019) Elizabeth Kautz et al.   Scientific Reports

Grain boundary engineering to control the discontinuous precipitation in multicomponent U10Mo alloy

(2018) Arun Devaraj et al.   ACTA MATERIALIA

Use of neural networks to analyze pulse shape data in low-background detectors

(2018) E. K. Mace et al.   JOURNAL OF RADIOANALYTICAL AND NUCLEAR CHEMISTRY

Phase transformation of metastable discontinuous precipitation products to equilibrium phases in U10Mo alloys

(2018) Arun Devaraj et al.   SCRIPTA MATERIALIA

Microstructural Materials Design via Deep Adversarial Learning Methodology

(2018) Zijiang Yang et al.   JOURNAL OF MECHANICAL DESIGN

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Kinetics of cellular transformation and competing precipitation mechanisms during sub-eutectoid annealing of U10Mo alloys

(2017) S. Jana et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Phase transformation kinetics in rolled U-10 wt. % Mo foil: Effect of post-rolling heat treatment and prior γ-UMo grain size

(2017) Saumyadeep Jana et al.   JOURNAL OF NUCLEAR MATERIALS

Energy landscapes for machine learning

(2017) Andrew J. Ballard et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Image driven machine learning methods for microstructure recognition

(2016) Aritra Chowdhury et al.   COMPUTATIONAL MATERIALS SCIENCE

Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

(2016) Brian L. DeCost et al.   JOM

A computer vision approach for automated analysis and classification of microstructural image data

(2015) Brian L. DeCost et al.   COMPUTATIONAL MATERIALS SCIENCE

Big–deep–smart data in imaging for guiding materials design

(2015) Sergei V. Kalinin et al.   NATURE MATERIALS

Human-level concept learning through probabilistic program induction

(2015) B. M. Lake et al.   SCIENCE

Sliced and Radon Wasserstein Barycenters of Measures

(2014) Nicolas Bonneel et al.   JOURNAL OF MATHEMATICAL IMAGING AND VISION

IRRADIATION PERFORMANCE OF U-Mo MONOLITHIC FUEL

(2014) M.K. MEYER et al.   Nuclear Engineering and Technology

REVIEW OF 15 YEARS OF HIGH-DENSITY LOW-ENRICHED UMo DISPERSION FUEL DEVELOPMENT FOR RESEARCH REACTORS IN EUROPE

(2014) S. VAN DEN BERGHE et al.   Nuclear Engineering and Technology

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports