Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …
providing an expressive view of the chemical space and multiscale processes. Their …
[HTML][HTML] A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial …
F Gomes Souza Jr, S Bhansali, K Pal… - Materials, 2024 - mdpi.com
From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment
analysis of nanocomposite literature, distinguishing itself from existing reviews through its …
analysis of nanocomposite literature, distinguishing itself from existing reviews through its …
Remarkable Toughening of Plastic with Monodispersed Nano-CaCO3: From Theoretical Predictions to Experimental Validation
J Qi, Z Shao, Y Sun, Z Wang, Q Chen, J Wang… - Langmuir, 2024 - ACS Publications
The structure–property relationship of poly (vinyl chloride)(PVC)/CaCO3 nanocomposites is
investigated by all-atom molecular dynamics (MD) simulations. MD simulation results …
investigated by all-atom molecular dynamics (MD) simulations. MD simulation results …
Predicting Pair Correlation Functions of Glasses using Machine Learning
Glasses offer a broad range of tunable thermophysical properties that are linked to their
compositions. However, it is challenging to establish a universal composition-property …
compositions. However, it is challenging to establish a universal composition-property …
QRChEM: A Deep Learning Framework for Materials Property Prediction and Design Using QR Codes
H Uthayakumar, RK K, R Jain, R Kumar… - ACS Engineering …, 2023 - ACS Publications
Machine learning (ML) surrogate models are used for the rapid prediction of materials
properties and are promising tools for accelerating new materials design and development …
properties and are promising tools for accelerating new materials design and development …
Predicting the pair correlation functions of silicate and borosilicate glasses using machine learning
Glasses offer a broad range of tunable thermophysical properties that are linked to their
compositions. However, it is challenging to establish a universal composition-property …
compositions. However, it is challenging to establish a universal composition-property …
Developing efficient deep learning model for predicting copolymer properties
K Chakraborty, TK Patra - Physical Chemistry Chemical Physics, 2023 - pubs.rsc.org
Deep learning models are gaining popularity and potency in predicting polymer properties.
These models can be built using pre-existing data and are useful for the rapid prediction of …
These models can be built using pre-existing data and are useful for the rapid prediction of …
Advancements in Nanocomposites: An In-Depth Exploration of Microstructural, Electrical, and Mechanical Dynamics
FG Souza Jr, S Bhansali, K Pal, F da Silveira Maranhão… - 2024 - preprints.org
This research presents a comprehensive bibliometric and sentiment analysis of
nanocomposite literature from 1990 to 2024. Employing cutting-edge computational …
nanocomposite literature from 1990 to 2024. Employing cutting-edge computational …