[HTML][HTML] Biomimetic natural biomaterials for tissue engineering and regenerative medicine: new biosynthesis methods, recent advances, and emerging applications

S Liu, JM Yu, YC Gan, XZ Qiu, ZC Gao, H Wang… - Military Medical …, 2023 - Springer
Biomimetic materials have emerged as attractive and competitive alternatives for tissue
engineering (TE) and regenerative medicine. In contrast to conventional biomaterials or …

Emerging materials intelligence ecosystems propelled by machine learning

R Batra, L Song, R Ramprasad - Nature Reviews Materials, 2021 - nature.com
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …

Machine learning: new ideas and tools in environmental science and engineering

S Zhong, K Zhang, M Bagheri, JG Burken… - Environmental …, 2021 - ACS Publications
The rapid increase in both the quantity and complexity of data that are being generated daily
in the field of environmental science and engineering (ESE) demands accompanied …

Polymer informatics: Current status and critical next steps

L Chen, G Pilania, R Batra, TD Huan, C Kim… - Materials Science and …, 2021 - Elsevier
Artificial intelligence (AI) based approaches are beginning to impact several domains of
human life, science and technology. Polymer informatics is one such domain where AI and …

Machine learning‐driven biomaterials evolution

A Suwardi, FK Wang, K Xue, MY Han, P Teo… - Advanced …, 2022 - Wiley Online Library
Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to
achieve desired biological responses. While there is constant evolution and innovation in …

Machine learning in materials science: From explainable predictions to autonomous design

G Pilania - Computational Materials Science, 2021 - Elsevier
The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

Benchmarking machine learning models for polymer informatics: an example of glass transition temperature

L Tao, V Varshney, Y Li - Journal of Chemical Information and …, 2021 - ACS Publications
In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the
glass transition temperature T g and other properties of polymers has attracted extensive …

Computational design and manufacturing of sustainable materials through first-principles and materiomics

SC Shen, E Khare, NA Lee, MK Saad… - Chemical …, 2023 - ACS Publications
Engineered materials are ubiquitous throughout society and are critical to the development
of modern technology, yet many current material systems are inexorably tied to widespread …

Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

M Reis, F Gusev, NG Taylor, SH Chung… - Journal of the …, 2021 - ACS Publications
Modern polymer science suffers from the curse of multidimensionality. The large chemical
space imposed by including combinations of monomers into a statistical copolymer …

New opportunity: machine learning for polymer materials design and discovery

P Xu, H Chen, M Li, W Lu - Advanced Theory and Simulations, 2022 - Wiley Online Library
Under the guidance of the material genome initiative (MGI), the use of data‐driven methods
to discover new materials has become an innovation of materials science. The polymer …