4.8 Article

Physics-Based Feature Makes Machine Learning Cognizing Crystal Properties Simple

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Materials Science, Multidisciplinary

Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning

I Ohkubo et al.

Summary: The closed-loop optimization of epitaxial titanium nitride thin-film growth was achieved using metal-organic molecular beam epitaxy (MO-MBE) technique combined with Bayesian machine learning, reducing the number of growth experiments. Epitaxial TiN thin films grown under the optimized conditions exhibited abrupt superconductor transitions above 5 K, showing a new efficient approach for developing less-studied materials. The combination of thin-film growth technique and Bayesian approach is expected to accelerate the development of automated operation of thin-film growth apparatuses.

MATERIALS TODAY PHYSICS (2021)

Review Chemistry, Physical

Machine learning for perovskite materials design and discovery

Qiuling Tao et al.

Summary: The review explores the applications of machine learning in assisting the discovery of perovskite materials, analyzing the development trends, showcasing the workflow, reviewing its applications in different types of perovskites, and suggesting future development prospects in the field.

NPJ COMPUTATIONAL MATERIALS (2021)

Article Physics, Multidisciplinary

Unsupervised Manifold Clustering of Topological Phononics

Yang Long et al.

PHYSICAL REVIEW LETTERS (2020)

Article Chemistry, Physical

On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations

Ryosuke Jinnouchi et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

Article Chemistry, Physical

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

Zhuoran Qiao et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

Article Multidisciplinary Sciences

Data-driven studies of magnetic two-dimensional materials

Trevor David Rhone et al.

SCIENTIFIC REPORTS (2020)

Article Nanoscience & Nanotechnology

Huge Piezoelectric Response of LaN-based Superlattices

Minglang Hu et al.

ACS APPLIED MATERIALS & INTERFACES (2020)

Article Chemistry, Physical

Creating Machine Learning-Driven Material Recipes Based on Crystal Structure

Keisuke Takahashi et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2019)

Review Physics, Multidisciplinary

Machine learning and the physical sciences

Giuseppe Carleo et al.

REVIEWS OF MODERN PHYSICS (2019)

Article Materials Science, Multidisciplinary

Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon

X. Qian et al.

MATERIALS TODAY PHYSICS (2019)

Article Computer Science, Information Systems

Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection

Yufeng Wang et al.

FUTURE INTERNET (2019)

Article Materials Science, Multidisciplinary

Compositional optimization of hard-magnetic phases with machine-learning models

Johannes J. Moeller et al.

ACTA MATERIALIA (2018)

Article Multidisciplinary Sciences

Learning from data to design functional materials without inversion symmetry

Prasanna V. Balachandran et al.

NATURE COMMUNICATIONS (2017)

Article Chemistry, Physical

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

Luca M. Ghiringhelli et al.

NPJ COMPUTATIONAL MATERIALS (2017)

Article Chemistry, Physical

Data mining-aided materials discovery and optimization

Wencong Lu et al.

JOURNAL OF MATERIOMICS (2017)

Article Multidisciplinary Sciences

Polar metals by geometric design

T. H. Kim et al.

NATURE (2016)

Article Statistics & Probability

A random forest guided tour

Gerard Biau et al.

Article Statistics & Probability

A random forest guided tour

Gerard Biau et al.

Article Physics, Multidisciplinary

Big Data of Materials Science: Critical Role of the Descriptor

Luca M. Ghiringhelli et al.

PHYSICAL REVIEW LETTERS (2015)

Article Materials Science, Multidisciplinary

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

Shyue Ping Ong et al.

COMPUTATIONAL MATERIALS SCIENCE (2013)

Review Computer Science, Artificial Intelligence

Representation Learning: A Review and New Perspectives

Yoshua Bengio et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013)

Article Computer Science, Artificial Intelligence

Extremely randomized trees

P Geurts et al.

MACHINE LEARNING (2006)

Review Physics, Condensed Matter

The Slater-Koster tight-binding method: a computationally efficient and accurate approach

DA Papaconstantopoulos et al.

JOURNAL OF PHYSICS-CONDENSED MATTER (2003)

Article Chemistry, Inorganic & Nuclear

Application of machine-learning methods to solid-state chemistry: ferromagnetism in transition metal alloys

GA Landrum et al.

JOURNAL OF SOLID STATE CHEMISTRY (2003)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Multidisciplinary Sciences

Nonlinear dimensionality reduction by locally linear embedding

ST Roweis et al.

SCIENCE (2000)

Article Multidisciplinary Sciences

A global geometric framework for nonlinear dimensionality reduction

JB Tenenbaum et al.

SCIENCE (2000)