Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering. Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries. Self-driving laboratory for accelerated discovery of thin-film materials. Controlling an organic synthesis robot with machine learning to search for new reactivity. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications-rather than absolutes-to overcome the overconfidence in traditional neural networks. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |