Researchers have developed a new machine learning algorithm that excels at interpreting optical spectra, potentially enabling faster and more precise medical diagnoses and sample analysis. Researchers ...

Artificial intelligence is driving a new era in spectroscopy and materials science by enabling faster data interpretation, more accurate property predictions, and even the generation of new materials.

A machine learning model has been developed that makes optical spectroscopy data easier and quicker to interpret. Researchers from Rice University (TX, USA) have developed a new machine learning ...

What will you learn on this course? This course is an introduction to mass spectral interpretation, aimed at presenting the fundamental tools and rules when examining high quality full-scan GC-MS data ...

Workflow of the proposed AI-based approach interpreting X-ray absorption spectroscopy (XAS) data (IMAGE) ...

In this interview, Kevin Broadbelt of Thermo Fisher Scientific discusses the small molecule applications of process Raman spectroscopy. How do cell therapies differ in complexity compared to ...

This webinar will discuss advanced techniques in NMR spectroscopy, providing descriptions of one-dimensional and two-dimensional NMR experiment types and data interpretation techniques, with examples ...

Manufacturing better batteries, faster electronics, and more effective pharmaceuticals depends on the discovery of new materials and the verification of their quality. Artificial intelligence is ...

Combining hyperspectral imaging and AI, this research identifies oxidative stress in red blood cells, paving the way for ...