AIMC Topic: Materials Science
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Machine Learning-Driven Biomaterials Evolution.
Advanced materials (Deerfield Beach, Fla.)
Oct 7, 2021
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 materials with time, biomaterials research has been hampered by t...
Fighting viruses with materials science: Prospects for antivirus surfaces, drug delivery systems and artificial intelligence.
Dental materials : official publication of the Academy of Dental Materials
Jan 10, 2021
OBJECTIVE: Viruses on environmental surfaces, in saliva and other body fluids represent risk of contamination for general population and healthcare professionals. The development of vaccines and medicines is costly and time consuming. Thus, the devel...
Near-hysteresis-free soft tactile electronic skins for wearables and reliable machine learning.
Proceedings of the National Academy of Sciences of the United States of America
Sep 28, 2020
Electronic skins are essential for real-time health monitoring and tactile perception in robots. Although the use of soft elastomers and microstructures have improved the sensitivity and pressure-sensing range of tactile sensors, the intrinsic viscoe...
Unsupervised word embeddings capture latent knowledge from materials science literature.
Nature
Jul 3, 2019
The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the ma...
Bio-inspired Design and Additive Manufacturing of Soft Materials, Machines, Robots, and Haptic Interfaces.
Angewandte Chemie (International ed. in English)
Jun 18, 2019
Soft materials possess several distinctive characteristics, such as controllable deformation, infinite degrees of freedom, and self-assembly, which make them promising candidates for building soft machines, robots, and haptic interfaces. In this Revi...
Physically informed artificial neural networks for atomistic modeling of materials.
Nature communications
May 28, 2019
Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the energy and Newtonian forces on atoms. Traditional interatomic potentials are based on physical intuition but contain few adjustable paramete...
Redesigning the Materials and Catalysts Database Construction Process Using Ontologies.
Journal of chemical information and modeling
Aug 17, 2018
Materials and catalyst informatics are emerging fields that are a result of shifts in terms of how materials and catalysts are discovered in the fields of materials science and catalysis. However, these fields are not reaching their full potential du...
Droplets As Liquid Robots.
Artificial life
Oct 6, 2017
Liquid droplets are very simple objects present in our everyday life. They are extremely important for many natural phenomena as well as for a broad variety of industrial processes. The conventional research areas in which the droplets are studied in...
Editorial: Machine Learning in Materials Science.
Journal of chemical information and modeling
May 27, 2024
Interatomic force from neural network based variational quantum Monte Carlo.
The Journal of chemical physics
Oct 28, 2022
Accurate ab initio calculations are of fundamental importance in physics, chemistry, biology, and materials science, which have witnessed rapid development in the last couple of years with the help of machine learning computational techniques such as...
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