We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Application of Machine Learning in Material Synthesis and Property Prediction
Summary
This review examines how machine learning is accelerating the discovery and development of new materials across fields like superconductivity, solar energy, and catalysis. Researchers describe how these computational approaches can dramatically reduce the time and cost of exploring new material properties compared to traditional experiments. The study highlights machine learning as one of the most promising tools for predicting material behavior and screening candidates for advanced applications.
Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.
Sign in to start a discussion.
More Papers Like This
Recent Advances in Polymer Design through Machine Learning: A Short Review
This review examines recent advances in applying machine learning — including supervised learning, unsupervised learning, and artificial neural networks — to polymer informatics, covering property prediction, synthesis optimisation, and polymer classification across diverse applications from medicine to aerospace. The authors highlight how growing datasets and improving ML techniques are enabling more systematic and effective polymer design compared to traditional trial-and-error approaches.
Leveraging Artificial Intelligence for Accelerated Polymer Synthesis and Design
This review examines AI-enabled advances in polymer informatics, focusing on machine learning and deep learning approaches for accelerating the design of application-specific polymeric materials across energy storage, production, and sustainable economy applications including recyclable and biodegradable polymers. The review highlights how AI-powered workflows are shortening the design-to-discovery cycle for next-generation polymer materials.
Applied machine learning as a driver for polymeric biomaterials design
Researchers reviewed how machine learning could accelerate the design of new medical-grade polymers by predicting properties like biodegradability and biocompatibility, bypassing slow trial-and-error lab work. The main obstacle identified is the lack of standardized, publicly available data on medically relevant polymer characteristics needed to train reliable AI models.
Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective
This review evaluates how machine learning and artificial intelligence are being used to predict the toxic effects of nanomaterials, including nanoplastics, on human health and the environment. These computational tools can help screen thousands of materials for potential hazards much faster than traditional lab experiments, though the authors note that better data quality and standardized methods are still needed.
Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges
This review covers machine learning methods applied to predicting and understanding mechanical properties of materials from large datasets. It is an engineering informatics paper and is not related to microplastics or environmental health.