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。Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century
Vineeth Venugopal et al.
INTERNATIONAL JOURNAL OF APPLIED GLASS SCIENCE (2021)
ViscNet: Neural network for predicting the fragility index and the temperature-dependency of viscosity
Daniel R. Cassar
ACTA MATERIALIA (2021)
Machine learning as a tool to design glasses with controlled dissolution for healthcare applications
Taihao Han et al.
ACTA BIOMATERIALIA (2020)
Machine learning on density and elastic property of oxide glasses driven by large dataset
Binghui Deng
JOURNAL OF NON-CRYSTALLINE SOLIDS (2020)
Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures
Edesio Alcobaca et al.
ACTA MATERIALIA (2020)
Revisiting the Makishima-Mackenzie model for predicting the young's modulus of oxide glasses
Ying Shi et al.
ACTA MATERIALIA (2020)
Predicting densities and elastic moduli of SiO2-based glasses by machine learning
Yong-Jie Hu et al.
NPJ COMPUTATIONAL MATERIALS (2020)
Data-driven predictive models for chemical durability of oxide glass under different chemical conditions
Yi Zhang et al.
NPJ MATERIALS DEGRADATION (2020)
Prediction of viscosity behavior in oxide glass materials using cation fingerprints with artificial neural networks
Jaekyun Hwang et al.
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS (2020)
Predicting the Young's Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning
Kai Yang et al.
SCIENTIFIC REPORTS (2019)
Recent advances and applications of machine learning in solid-state materials science
Jonathan Schmidt et al.
NPJ COMPUTATIONAL MATERIALS (2019)
Predicting Young's modulus of oxide glasses with sparse datasets using machine learning
Suresh Bishnoi et al.
JOURNAL OF NON-CRYSTALLINE SOLIDS (2019)
Elasticity, hardness, and fracture toughness of sodium aluminoborosilicate glasses
Kacper Januchta et al.
JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2019)
2018 Table of static dipole polarizabilities of the neutral elements in the periodic table
Peter Schwerdtfeger et al.
MOLECULAR PHYSICS (2019)
Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning
Han Liu et al.
NPJ MATERIALS DEGRADATION (2019)
Matminer: An open source toolkit for materials data mining
Logan Ward et al.
COMPUTATIONAL MATERIALS SCIENCE (2018)
A new model linking elastic properties and ionic conductivity of mixed network former glasses
Weimin Wang et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2018)
Predicting glass transition temperatures using neural networks
Daniel R. Cassar et al.
ACTA MATERIALIA (2018)
Machine learning in materials informatics: recent applications and prospects
Rampi Ramprasad et al.
NPJ COMPUTATIONAL MATERIALS (2017)
Atomic weights of the elements 2013 (IUPAC Technical Report)
Juris Meija et al.
PURE AND APPLIED CHEMISTRY (2016)
A general-purpose machine learning framework for predicting properties of inorganic materials
Logan Ward et al.
NPJ COMPUTATIONAL MATERIALS (2016)
Perspective: Materials informatics and big data: Realization of the fourth paradigm of science in materials science
Ankit Agrawal et al.
APL MATERIALS (2016)
Topological constraints and the Makishima-Mackenzie model
M. Plucinski et al.
JOURNAL OF NON-CRYSTALLINE SOLIDS (2015)
Combinatorial screening for new materials in unconstrained composition space with machine learning
B. Meredig et al.
PHYSICAL REVIEW B (2014)
Covalent radii revisited
Beatriz Cordero et al.
DALTON TRANSACTIONS (2008)
LOF: Identifying density-based local outliers
MM Breunig et al.
SIGMOD RECORD (2000)