Development of Vickers hardness prediction models via microstructural analysis and machine learning

Accelerated Data-Driven Accurate Positioning of the Band Edges of MXenes

(2019) Avanish Mishra et al.   Journal of Physical Chemistry Letters

Coupling the High-Throughput Property Map to Machine Learning for Predicting Lattice Thermal Conductivity

(2019) Rinkle Juneja et al.   CHEMISTRY OF MATERIALS

Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene

(2018) Arunkumar Chitteth Rajan et al.   CHEMISTRY OF MATERIALS

Machine learning for phase selection in multi-principal element alloys

(2018) Nusrat Islam et al.   COMPUTATIONAL MATERIALS SCIENCE

An materials informatics approach to Ni-based single crystal superalloys lattice misfit prediction

(2018) Xue Jiang et al.   COMPUTATIONAL MATERIALS SCIENCE

New methods for automatic quantification of microstructural features using digital image processing

(2018) Andrew Campbell et al.   MATERIALS & DESIGN

A strategy to apply machine learning to small datasets in materials science

(2018) Ying Zhang et al.   npj Computational Materials

Effects of aluminum and molybdenum content on the microstructure and properties of multi-component γ′-strengthened cobalt-base superalloys

(2018) Yi Zhang et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Analytics for microstructure datasets produced by phase-field simulations

(2016) Philipp Steinmetz et al.   ACTA MATERIALIA

Extraction of Flow Behavior and Hall-Petch Parameters Using a Nanoindentation Multiple Sharp Tip Approach 

(2016) Alexander Leitner et al.   ADVANCED ENGINEERING MATERIALS

Image driven machine learning methods for microstructure recognition

(2016) Aritra Chowdhury et al.   COMPUTATIONAL MATERIALS SCIENCE

Effect of heat treatments on microstructure evolution and mechanical properties of blended nickel-based superalloys powders fabricated by laser powder deposition

(2016) Y. Tian et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

Accelerated search for materials with targeted properties by adaptive design

(2016) Dezhen Xue et al.   Nature Communications

Materials Data Science: Current Status and Future Outlook

(2015) Surya R. Kalidindi et al.   Annual Review of Materials Research

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

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

A Machine Learning-Based Design Representation Method for Designing Heterogeneous Microstructures

(2015) Hongyi Xu et al.   JOURNAL OF MECHANICAL DESIGN

Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening

(2015) Xianfeng Ma et al.   Journal of Physical Chemistry Letters

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Elemental partitioning and mechanical properties of Ti- and Ta-containing Co–Al–W-base superalloys studied by atom probe tomography and nanoindentation

(2014) Ivan Povstugar et al.   ACTA MATERIALIA

A Descriptor-Based Design Methodology for Developing Heterogeneous Microstructural Materials System

(2014) Hongyi Xu et al.   JOURNAL OF MECHANICAL DESIGN

Machine Learning Based Predictive Modeling of Machining Induced Microhardness and Grain Size in Ti–6Al–4V Alloy

(2014) Yiğit M. Arisoy et al.   MATERIALS AND MANUFACTURING PROCESSES

The influence of Al:Nb ratio on the microstructure and mechanical response of quaternary Ni–Cr–Al–Nb alloys

(2014) P.M. Mignanelli et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

Alloying effects in polycrystalline γ′ strengthened Co–Al–W base alloys

(2013) H.-Y. Yan et al.   INTERMETALLICS

Understanding and visualizing microstructure and microstructure variance as a stochastic process

(2011) Stephen R. Niezgoda et al.   ACTA MATERIALIA

Microstructural factors affecting hardness property of dual two-phase intermetallic alloys based on Ni3Al–Ni3V pseudo-binary alloy system

(2009) K. Kawahara et al.   INTERMETALLICS

Image Based Analysis of Complex Microstructures of Engineering Materials

(2008) Tomasz Wejrzanowski et al.   International Journal of Applied Mathematics and Computer Science

Effects of a tungsten addition on the morphological evolution, spatial correlations and temporal evolution of a model Ni–Al–Cr superalloy

(2007) Chantal K. Sudbrack et al.   ACTA MATERIALIA