Machine-learning-assisted de novo design of organic molecules and polymers: opportunities and challenges
Organic molecules and polymers have a broad range of applications in biomedical,
chemical, and materials science fields. Traditional design approaches for organic molecules …
chemical, and materials science fields. Traditional design approaches for organic molecules …
Machine-learning-based predictions of polymer and postconsumer recycled polymer properties: a comprehensive review
There has been a tremendous increase in demand for virgin and postconsumer recycled
(PCR) polymers due to their wide range of chemical and physical characteristics. Despite …
(PCR) polymers due to their wide range of chemical and physical characteristics. Despite …
Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning
Polyimides have been widely used in modern industries because of their excellent
mechanical and thermal properties, eg, high-temperature fuel cells, displays, and aerospace …
mechanical and thermal properties, eg, high-temperature fuel cells, displays, and aerospace …
Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm
The use of machine learning in computational molecular design has great potential to
accelerate the discovery of innovative materials. However, its practical benefits still remain …
accelerate the discovery of innovative materials. However, its practical benefits still remain …
Data-driven methods for accelerating polymer design
TK Patra - ACS Polymers Au, 2021 - ACS Publications
Optimal design of polymers is a challenging task due to their enormous chemical and
configurational space. Recent advances in computations, machine learning, and increasing …
configurational space. Recent advances in computations, machine learning, and increasing …
A polymer dataset for accelerated property prediction and design
Emerging computation-and data-driven approaches are particularly useful for rationally
designing materials with targeted properties. Generally, these approaches rely on identifying …
designing materials with targeted properties. Generally, these approaches rely on identifying …
Machine-learning-assisted low dielectric constant polymer discovery
Machine learning (ML) has excellent potential for molecular property prediction and new
molecule discovery. However, real-world synthesis is the most vital part of determining a …
molecule discovery. However, real-world synthesis is the most vital part of determining a …
Opportunities and challenges for machine learning in materials science
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …
the discovery of novel materials and the improvement of molecular simulations, with likely …
Accelerating materials discovery using machine learning
Y Juan, Y Dai, Y Yang, J Zhang - Journal of Materials Science & …, 2021 - Elsevier
The discovery of new materials is one of the driving forces to promote the development of
modern society and technology innovation, the traditional materials research mainly …
modern society and technology innovation, the traditional materials research mainly …
Advances of machine learning in materials science: Ideas and techniques
In this big data era, the use of large dataset in conjunction with machine learning (ML) has
been increasingly popular in both industry and academia. In recent times, the field of …
been increasingly popular in both industry and academia. In recent times, the field of …