Topic: Machine Learning in the Discovery and Design of Bio-functional Materials

In the rapidly advancing era of modern technology, machine learning is revolutionizing various fields, including the discovery and design of bio-functional materials. These materials, such as biocompatible, biodegradable, bioactive, biosensor, and biomimetic materials, possess unique physical, chemical, and biological properties that find wide applications in human healthcare, environmental science, energy, and beyond. However, traditional methods of materials research are often time-consuming and labor-intensive, limiting the comprehensive exploration of material properties and potentials.

This Special Issue aims to showcase and explore how machine learning technologies can drive innovation and advancement in bio-functional materials. By integrating big data, advanced algorithms, and high-throughput experimental validation, machine learning can significantly enhance the efficiency of material design, accelerate the discovery of new materials, and optimize the performance of existing ones, which greatly changed the paradigm of the research and development of new functional biomaterials.

The Special Issue will cover, but is not limited to, the following topics:
● Data-driven Design and Optimization of Bio-functional Materials Using Machine Learning;
● High-throughput Preparation, Screening and Characterization with Multiscale, Multidimensional and Multifield Collaborative Technologies for Bio-functional Materials;
● Simulation of Physical and Chemical Properties of Bio-functional Materials with Explainable Machine Learning;
● Rational Design of New Bio-functional Materials with Machine Learning-Assisted Structure-Property Relationship;
● Interdisciplinary Approaches: Integrating Machine Learning with Chemistry, Biology, and Physics;
● Analysis and Prediction of High-quality Datasets for Bio-functional Materials with Machine Learning;
● Exploration of Advanced Algorithms and Models in Bio-functional Materials and Biomedical Engineering.

15 Mar 2025