Data-Driven Studies of Li-Ion-Battery Materials

How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics

(2018) Bing Cao et al.   ACS Nano

Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons

(2018) Jake Graser et al.   CHEMISTRY OF MATERIALS

Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf−In System

(2018) Anton O. Oliynyk et al.   INORGANIC CHEMISTRY

Predicting the Band Gaps of Inorganic Solids by Machine Learning

(2018) Ya Zhuo et al.   Journal of Physical Chemistry Letters

Application of artificial neural networks in design of lithium-ion batteries

(2018) Bin Wu et al.   JOURNAL OF POWER SOURCES

Machine learning directed search for ultraincompressible, superhard materials

(2018) Aria Mansouri Tehrani et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning

(2018) Prasanna V. Balachandran et al.   Nature Communications

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

(2018) Zeeshan Ahmad et al.   ACS Central Science

Spray pyrolysis of conductor- and binder-free porous FeS2 films for high-performance lithium ion batteries

(2018) Shadi Al Khateeb et al.   JOURNAL OF MATERIALS SCIENCE

Identifying an efficient, thermally robust inorganic phosphor host via machine learning

(2018) Ya Zhuo et al.   Nature Communications

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

(2017) Austin D. Sendek et al.   Energy & Environmental Science

Experimental and Simulation Investigations of Porosity Graded Cathodes in Mitigating Battery Degradation of High Voltage Lithium-Ion Batteries

(2017) Lin Liu et al.   JOURNAL OF THE ELECTROCHEMICAL SOCIETY

Machine learning phases of matter

(2017) Juan Carrasquilla et al.   Nature Physics

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats

(2017) Luca M. Ghiringhelli et al.   npj Computational Materials

Classifying Crystal Structures of Binary Compounds AB through Cluster Resolution Feature Selection and Support Vector Machine Analysis

(2016) Anton O. Oliynyk et al.   CHEMISTRY OF MATERIALS

High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

(2016) Anton O. Oliynyk et al.   CHEMISTRY OF MATERIALS

Gd12Co5.3Bi and Gd12Co5Bi, Crystalline Doppelgänger with Low Thermal Conductivities

(2016) Anton O. Oliynyk et al.   INORGANIC CHEMISTRY

How to improve the stability and rate performance of lithium-ion batteries with transition metal oxide anodes

(2016) Guoyong Wang et al.   JOURNAL OF MATERIALS RESEARCH

Data mining our way to the next generation of thermoelectrics

(2016) Taylor D. Sparks et al.   SCRIPTA MATERIALIA

Performance and resource considerations of Li-ion battery electrode materials

(2015) Leila Ghadbeigi et al.   Energy & Environmental Science

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

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

(2015) Datong Liu et al.   MEASUREMENT

Big Data of Materials Science: Critical Role of the Descriptor

(2015) Luca M. Ghiringhelli et al.   PHYSICAL REVIEW LETTERS

Combinatorial screening for new materials in unconstrained composition space with machine learning

(2014) B. Meredig et al.   PHYSICAL REVIEW B

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

(2014) Aaron Gilad Kusne et al.   Scientific Reports

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Detecting Novel Associations in Large Data Sets

(2011) D. N. Reshef et al.   SCIENCE