Open AccessDOI
Biomaterials by design: Harnessing data for future development
Kun Xue,FuKe Wang,Ady Suwardi,Ming-Yong Han,Peili Teo,Pei Wang,Shijie Wang,Enyi Ye,Zibiao Li,Xian Jun Loh +9 more
- Vol. 12, pp 100165
TLDR
In this paper, the authors discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials.Abstract:
Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality.read more
Citations
More filters
Journal ArticleDOI
Emerging early diagnostic methods for acute kidney injury
Zuoxiu Xiao,Qiong Huang,Yuqi Yang,Min Liu,Qiaohui Chen,Jia Huang,Yuting,Xiang,Xingyu Long,Tianjiao Zhao,Xin Wang,Xiao-Jin Zhu,Shiqi Tu,Yayun,Nan,Kelong Ai +15 more
TL;DR: This review comprehensively summarizes the application of machine learning in AKI prediction algorithms and specific scenarios, and introduces the key role of early biomarkers in the progress of AKI, and comprehensively summarize theApplication of emerging detection technologies for early AKI.
Journal ArticleDOI
A User’s Guide to Machine Learning for Polymeric Biomaterials
TL;DR: The Google Colab notebook as discussed by the authors provides a step-by-step guide to the use of machine learning in biomaterials development, using data from a real biomaterial design challenge based on group's research.
Journal ArticleDOI
Potential of Recycled Silicon and Silicon-Based Thermoelectrics for Power Generation
S. S. F. Duran,Danwei Zhang,Wei Yang Lim,Jing Cao,Hongfei Liu,Qiang Zhu,Chee Kiang Ivan Tan,Jianwei Xu,Xian Jun Loh,Ady Suwardi +9 more
TL;DR: In this paper , the authors summarize the usage of high-temperature thermoelectric generators (TEGs) in applications such as commercial aviation and space voyages, which can be used to guide sustainable recycling of e-waste into TEGs for power harvesting.
Journal ArticleDOI
Bottom-up design of hydrogels for programmable drug release.
TL;DR: In this paper , the authors present a review of physical models of hydrogel release and discuss the interesting potential and challenges for programming release, and potential implications with the advent of machine learning.
Journal ArticleDOI
Machine Learning in Tissue Engineering
TL;DR: A recent review as discussed by the authors highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research, including machine-optimized biomaterial design, predictive modeling of scaffold fabrication, and spatiotemporal analysis of cell and tissue systems.
References
More filters
Journal ArticleDOI
Shape Deviation Generator—A Convolution Framework for Learning and Predicting 3-D Printing Shape Accuracy
TL;DR: This work establishes the shape deviation generator (SDG) as a novel data analytical framework through a convolution formulation to model the 3-D shape formation in the AM process and establishes a new engineering-informed machine-learning framework to facilitate the learning of AM data to establish models for geometric shape accuracy prediction and control.
Journal ArticleDOI
Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers.
Jennifer Bone,Christopher M. Childs,Aditya Menon,Barnabás Póczos,Adam W. Feinberg,Philip R. LeDuc,Newell R. Washburn +6 more
TL;DR: The HML approach offers a promising pathway toward scaling 3D bioprinting by optimizing print fidelity via learned build parameters that reduce the need for iterative testing.
Journal ArticleDOI
Analysis of stress ratio effects on fatigue propagation in a sintered duplex steel by experimentation and artificial neural network approaches
TL;DR: In this paper, the influence of R on the fatigue crack propagation resistance in a PM duplex stainless steel was investigated and an artificial neural network based model was optimised as a new simulation tool.
Journal ArticleDOI
Estimation of welded joint strength using genetic algorithm approach
TL;DR: In this article, a genetic algorithm approach is extended to the estimation of mechanical properties of the joining of brass materials and the results indicated that the changes of the gap between the joint parts and the torch angle have an important effect on the welded joint strength value and the optimum quantity of the shielding gas and the pulse frequencies exist in the tensile strength of welded joints.