From DFT to machine learning: recent approaches to materials science–a review
Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …
and complexity of generated data. This massive amount of raw data needs to be stored and …
Machine learning and big data provide crucial insight for future biomaterials discovery and research
J Kerner, A Dogan, H von Recum - Acta Biomaterialia, 2021 - Elsevier
Abstract Machine learning have been widely adopted in a variety of fields including
engineering, science, and medicine revolutionizing how data is collected, used, and stored …
engineering, science, and medicine revolutionizing how data is collected, used, and stored …
Sustainable materials acceleration platform reveals stable and efficient wide-bandgap metal halide perovskite alloys
The vast chemical space of emerging semiconductors, like metal halide perovskites, and
their varied requirements for semiconductor applications have rendered trial-and-error …
their varied requirements for semiconductor applications have rendered trial-and-error …
DFT and QSAR studies of ethylene polymerization by zirconocene catalysts
R Parveen, TR Cundari, JM Younker, G Rodriguez… - ACS …, 2019 - ACS Publications
A computational study of olefin polymerization has been performed on 51 zirconocene
catalysts. The catalysts can be categorized into three classes according to the ligand …
catalysts. The catalysts can be categorized into three classes according to the ligand …
Exploring structure-activity relationships for polymer biodegradability by microorganisms
Research on the environmental biodegradation or microbial biodegradation of polymers has
substantially increased recently due to growing demand for biodegradable polymers for …
substantially increased recently due to growing demand for biodegradable polymers for …
Reproducibility, sharing and progress in nanomaterial databases
Publicly accessible databases are core resources for data-rich research, consolidating field-
specific knowledge and highlighting best practices and challenges. Further effective growth …
specific knowledge and highlighting best practices and challenges. Further effective growth …
Toward a digital polymer reaction engineering
S Lazzari, A Lischewski, Y Orlov, P Deglmann… - Advances in Chemical …, 2020 - Elsevier
What is digitalization, and why do we need it? What does digitalization mean for research
and development in polymer reaction engineering (PRE)? In this chapter, we address these …
and development in polymer reaction engineering (PRE)? In this chapter, we address these …
How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
F Cravero, SA Schustik, MJ Martínez… - … and Technology of …, 2022 - Taylor & Francis
Polymer informatics is an emerging discipline that has benefited from the strong
development that data science has experienced over the last decade. Machine learning …
development that data science has experienced over the last decade. Machine learning …
Computational modeling of silicate glasses: a quantitative structure-property relationship perspective
A Pedone, MC Menziani - … of Disordered Materials: From Network Glasses …, 2015 - Springer
This article reviews the present state of Quantitative Structure-Property Relationships
(QSPR) in glass design and gives an outlook into future developments. First an overview is …
(QSPR) in glass design and gives an outlook into future developments. First an overview is …
Using 3D-QSAR to predict the separation efficiencies of flotation collectors: Implications for rational design of non-polar side chains
X Yang, B Albijanic, Y Zhou, Y Zhou, X Zhu - Minerals Engineering, 2018 - Elsevier
Three-dimensional quantitative structure-activity relationship (3D-QSAR) methods were
innovatively introduced into the structure-performance study of flotation collectors using …
innovatively introduced into the structure-performance study of flotation collectors using …