An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would …
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural to wonder what lessons can be learned from other fields undergoing similar developments …
There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models …
C Ling - npj Computational Materials, 2022 - nature.com
Batteries are of paramount importance for the energy storage, consumption, and transportation in the current and future society. Recently machine learning (ML) has …
A bottleneck in efficiently connecting new materials discoveries to established literature has arisen due to an increase in publications. This problem may be addressed by using named …
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials …
A large amount of materials science knowledge is generated and stored as text published in peer-reviewed scientific literature. While recent developments in natural language …
Y Liu, X Tan, J Liang, H Han, P Xiang… - Advanced Functional …, 2023 - Wiley Online Library
Data‐driven epoch, the development of machine learning (ML) in materials and device design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …
We propose strategies that couple natural language processing with deep learning to enhance machine capability for corrosion-resistant alloy design. First, accuracy of machine …