Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte
Abstract Machine learning (ML) exhibits substantial potential for predicting the properties of
solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML …
solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML …
Navigating the landscape of enzyme design: from molecular simulations to machine learning
J Zhou, M Huang - Chemical Society Reviews, 2024 - pubs.rsc.org
Global environmental issues and sustainable development call for new technologies for fine
chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the …
chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the …
Data generation for machine learning interatomic potentials and beyond
The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …
machine learning models for predicting molecular properties and behavior. Recent strides in …
Stimuli-responsive chiral cellulose nanocrystals based self-assemblies for security measures to prevent counterfeiting: a review
S Singh, S Bhardwaj, N Choudhary… - … Applied Materials & …, 2024 - ACS Publications
The proliferation of misleading information and counterfeit products in conjunction with
technical progress presents substantial worldwide issues. To address the issue of …
technical progress presents substantial worldwide issues. To address the issue of …
[HTML][HTML] Deep learning for low-data drug discovery: hurdles and opportunities
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to
protein structure prediction and synthesis planning. However, it is often challenged by the …
protein structure prediction and synthesis planning. However, it is often challenged by the …
Integrated molecular modeling and machine learning for drug design
Modern therapeutic development often involves several stages that are interconnected, and
multiple iterations are usually required to bring a new drug to the market. Computational …
multiple iterations are usually required to bring a new drug to the market. Computational …
Integrated transfer learning and multitask learning strategies to construct graph neural network models for predicting bioaccumulation parameters of chemicals
Z Xiao, M Zhu, J Chen, Z You - Environmental Science & …, 2024 - ACS Publications
Accurate prediction of parameters related to the environmental exposure of chemicals is
crucial for the sound management of chemicals. However, the lack of large data sets for …
crucial for the sound management of chemicals. However, the lack of large data sets for …
Transformer technology in molecular science
A transformer is the foundational architecture behind large language models designed to
handle sequential data by using mechanisms of self‐attention to weigh the importance of …
handle sequential data by using mechanisms of self‐attention to weigh the importance of …
Machine learning applications for electrospun nanofibers: a review
Electrospun nanofibers have gained prominence as a versatile material, with applications
spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles …
spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles …
Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest
The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy
metal pollution, posing grave ecological and human health risks. Developing an accurate …
metal pollution, posing grave ecological and human health risks. Developing an accurate …