Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

X Bi, L Lin, Z Chen, J Ye - Small Methods, 2024 - Wiley Online Library
Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting
and sensitive analytical technique, has exerted high applicational value in a broad range of …

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

T He, H Huo, CJ Bartel, Z Wang, K Cruse, G Ceder - Science advances, 2023 - science.org
Synthesis prediction is a key accelerator for the rapid design of advanced materials.
However, determining synthesis variables such as the choice of precursor materials is …

Machine learning for analyses and automation of structural characterization of polymer materials

S Lu, A Jayaraman - Progress in Polymer Science, 2024 - Elsevier
Structural characterization of polymer materials is a major step in the process of creating
complex materials design-structural-property relationships. With growing interests in artificial …

Machine-learning rationalization and prediction of solid-state synthesis conditions

H Huo, CJ Bartel, T He, A Trewartha, A Dunn… - Chemistry of …, 2022 - ACS Publications
There currently exist no quantitative methods to determine the appropriate conditions for
solid-state synthesis. This not only hinders the experimental realization of novel materials …

Interpretable machine learning enabled inorganic reaction classification and synthesis condition prediction

C Karpovich, E Pan, Z Jensen, E Olivetti - Chemistry of Materials, 2023 - ACS Publications
Data-driven synthesis planning with machine learning is a key step in the design and
discovery of novel inorganic compounds with desirable properties. Inorganic materials …

Extracting structured seed-mediated gold nanorod growth procedures from scientific text with LLMs

N Walker, S Lee, J Dagdelen, K Cruse, S Gleason… - Digital …, 2023 - pubs.rsc.org
Although gold nanorods have been the subject of much research, the pathways for
controlling their shape and thereby their optical properties remain largely heuristically …