[HTML][HTML] Materials discovery and design using machine learning
Y Liu, T Zhao, W Ju, S Shi - Journal of Materiomics, 2017 - Elsevier
The screening of novel materials with good performance and the modelling of quantitative
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening
We present a machine-learning-augmented chemisorption model that enables fast and
accurate prediction of the surface reactivity of metal alloys within a broad chemical space …
accurate prediction of the surface reactivity of metal alloys within a broad chemical space …
Principal component analysis: A natural approach to data exploration
Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …
areas. This work reports, in an accessible and integrated manner, several theoretical and …
Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends
Mechanical metamaterials have opened an exciting venue for control and manipulation of
architected structures in recent years. Research in the area of mechanical metamaterials …
architected structures in recent years. Research in the area of mechanical metamaterials …
Quantitative structure–property relationship modeling of diverse materials properties
The design and synthesis of materials with useful, novel properties is one of the most active
areas of contemporary science, generating a veritable explosion of scientific activity in areas …
areas of contemporary science, generating a veritable explosion of scientific activity in areas …
Predicting the thermodynamic stability of perovskite oxides using machine learning models
Perovskite materials have become ubiquitous in many technologically relevant applications,
ranging from catalysts in solid oxide fuel cells to light absorbing layers in solar photovoltaics …
ranging from catalysts in solid oxide fuel cells to light absorbing layers in solar photovoltaics …
A deep learning approach for prediction of syngas lower heating value from CFB gasifier in Aspen plus®
Aspen Plus® is one of the practicable software for investigation of the biomass gasification
characteristics. Also, artificial neural networks (ANN) as a deep learning approach are often …
characteristics. Also, artificial neural networks (ANN) as a deep learning approach are often …
Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses
Fe-based metallic glasses (MGs) have been extensively investigated due to their unique
properties, especially the outstanding soft-magnetic properties. However, conventional …
properties, especially the outstanding soft-magnetic properties. However, conventional …
Artificial neural network approach for the steam gasification of palm oil waste using bottom ash and CaO
Abstract The Artificial Neural Network (ANN) modelling is presented for the steam
gasification of palm kernel shell using CaO adsorbent and coal bottom ash as a catalyst. The …
gasification of palm kernel shell using CaO adsorbent and coal bottom ash as a catalyst. The …