4.6 Article

Machine learning predictions of band gap and band edge for (GaN)1-x(ZnO)x solid solution using crystal structure information

Related references

Note: Only part of the references are listed.
Article Materials Science, Multidisciplinary

Machine learning assisted discovering of new M2X3-type thermoelectric materials

Du Chen et al.

Summary: A machine learning approach was demonstrated to discover new M2X3-type thermoelectric materials solely based on composition information. By constructing a compound library and using a random forest algorithm with Bayesian optimization, a model was trained to predict crystal structures with a high accuracy of 91% for compounds with rhombohedral structure similar to Bi2Te3.

RARE METALS (2022)

Article Materials Science, Multidisciplinary

Thermal forming properties of a Cr-Mn-Si-Ni alloyed naval steel under different forming conditions by different constitutive models

Jia-Li Pang et al.

Summary: In this study, a series of thermal compression tests were conducted to investigate the effect of different strain rates and temperatures on a Cr-Mn-Si-Ni alloyed naval steel. Strain-compensated Arrhenius-type constitutive (SCAC) and backpropagation artificial neural network (BP-ANN) models with optimized structure were established. The optimized BP-ANN model demonstrated improved predictive performance compared to the SCAC model. Additionally, electron backscatter diffraction analysis was used to study the microstructural evolution behavior of the naval steel during thermoplastic deformation, revealing that dynamic recrystallization was promoted by higher forming temperatures and lower strain rates.

RARE METALS (2022)

Article Materials Science, Multidisciplinary

Machine learning (ML)-assisted optimization doping of KI in MAPbI3solar cells

Sheng Jiang et al.

Summary: Perovskite solar cells have been attracting attention in the PV field for their increasing efficiency. In this study, a machine learning approach was applied to optimize KI doping in MAPbI(3)solar cells, leading to an improved efficiency of 20.91%.

RARE METALS (2021)

Article Polymer Science

Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors

Yun Zhang et al.

Summary: Glass transition temperature, T-g, is an important thermophysical property of polyacrylamides that can be difficult and resource-intensive to determine. A Gaussian process regression model based on quantum chemical descriptors is developed to predict T-g, showing high stability and accuracy for fast and low-cost estimations.

POLYMER CHEMISTRY (2021)

Article Computer Science, Artificial Intelligence

From local explanations to global understanding with explainable AI for trees

Scott M. Lundberg et al.

NATURE MACHINE INTELLIGENCE (2020)

Article Chemistry, Multidisciplinary

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Volker L. Deringer et al.

ADVANCED MATERIALS (2019)

Article Chemistry, Physical

Data-Driven Discovery of Photoactive Quaternary Oxides Using First-Principles Machine Learning

Daniel W. Davies et al.

CHEMISTRY OF MATERIALS (2019)

Article Materials Science, Multidisciplinary

Band gap and band alignment prediction of nitride-based semiconductors using machine learning

Yang Huang et al.

JOURNAL OF MATERIALS CHEMISTRY C (2019)

Article Multidisciplinary Sciences

Universal fragment descriptors for predicting properties of inorganic crystals

Olexandr Isayev et al.

NATURE COMMUNICATIONS (2017)

Article Materials Science, Multidisciplinary

Informatics-aided bandgap engineering for solar materials

Partha Dey et al.

COMPUTATIONAL MATERIALS SCIENCE (2014)

Article Physics, Multidisciplinary

Machine learning of molecular electronic properties in chemical compound space

Gregoire Montavon et al.

NEW JOURNAL OF PHYSICS (2013)

Article Chemistry, Multidisciplinary

Photocatalytic Water Splitting Using Modified GaN:ZnO Solid Solution under Visible Light: Long-Time Operation and Regeneration of Activity

Tomoyuki Ohno et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2012)

Article Computer Science, Artificial Intelligence

LIBSVM: A Library for Support Vector Machines

Chih-Chung Chang et al.

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2011)

Article Chemistry, Physical

Photocatalytic Water Splitting: Recent Progress and Future Challenges

Kazuhiko Maeda et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2010)

Article Computer Science, Interdisciplinary Applications

Stochastic gradient boosting

JH Friedman

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Statistics & Probability

Greedy function approximation: A gradient boosting machine

JH Friedman

ANNALS OF STATISTICS (2001)