Improving prediction accuracy of high-performance materials via modified machine learning strategy

Improving phase prediction accuracy for high entropy alloys with Machine learning

(2021) Sandesh Risal et al.   COMPUTATIONAL MATERIALS SCIENCE

Machine learning assisted composition effective design for precipitation strengthened copper alloys

(2021) Hongtao Zhang et al.   ACTA MATERIALIA

Discovery of aluminum alloys with ultra-strength and high-toughness via a property-oriented design strategy

(2021) Lei Jiang et al.   JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY

Nanocomposites and polyethylene blends: two potentially synergistic strategies for HVDC insulation materials with ultra-low electrical conductivity

(2020) Fritjof Nilsson et al.   COMPOSITES PART B-ENGINEERING

On-the-fly closed-loop materials discovery via Bayesian active learning

(2020) A. Gilad Kusne et al.   Nature Communications

Microstructure and properties of a novel ultra-high strength, high elasticity and high plasticity Cu–20Ni–20Mn-0.3Nb-0.3Cr-0.1Zr alloy

(2020) Bin Liu et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Machine learning assisted design of high entropy alloys with desired property

(2019) Cheng Wen et al.   ACTA MATERIALIA

Materials informatics: From the atomic-level to the continuum

(2019) J.M. Rickman et al.   ACTA MATERIALIA

Machine-learning phase prediction of high-entropy alloys

(2019) Wenjiang Huang et al.   ACTA MATERIALIA

High-temperature bulk metallic glasses developed by combinatorial methods

(2019) Ming-Xing Li et al.   NATURE

Materials informatics for the screening of multi-principal elements and high-entropy alloys

(2019) J. M. Rickman et al.   Nature Communications

Data‐Driven Materials Science: Status, Challenges, and Perspectives

(2019) Lauri Himanen et al.   Advanced Science

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation

(2019) Zheng Xiong et al.   COMPUTATIONAL MATERIALS SCIENCE

Accelerated Discovery of Large Electrostrains in BaTiO3 -Based Piezoelectrics Using Active Learning

(2018) Ruihao Yuan et al.   ADVANCED MATERIALS

Machine learning modeling of superconducting critical temperature

(2018) Valentin Stanev et al.   npj Computational Materials

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Designing High Dielectric Permittivity Material in Barium Titanate

(2017) Jinghui Gao et al.   Journal of Physical Chemistry C

Feature selection methods for big data bioinformatics: A survey from the search perspective

(2016) Lipo Wang et al.   METHODS

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

Investigation of transmittance and thermal conductivity properties of silica gels for application as transparent heat insulation materials

(2015) Friederike Klenert et al.   JOURNAL OF SOL-GEL SCIENCE AND TECHNOLOGY

Large Strain in Relaxor/Ferroelectric Composite Lead-Free Piezoceramics

(2015) Haibo Zhang et al.   Advanced Electronic Materials

Relaxor-based ferroelectric single crystals: Growth, domain engineering, characterization and applications

(2014) Enwei Sun et al.   PROGRESS IN MATERIALS SCIENCE