Cargando…

An Ecosystem for Digital Reticular Chemistry

[Image: see text] The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of...

Descripción completa

Detalles Bibliográficos
Autores principales: Jablonka, Kevin Maik, Rosen, Andrew S., Krishnapriyan, Aditi S., Smit, Berend
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141625/
https://www.ncbi.nlm.nih.gov/pubmed/37122448
http://dx.doi.org/10.1021/acscentsci.2c01177
Descripción
Sumario:[Image: see text] The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of machine learning to reticular materials, progress in the field has stagnated, possibly because digital chemistry is more an art than a science and its limited accessibility to inexperienced researchers. To address this issue, we present mofdscribe, a software ecosystem tailored to novice and seasoned digital chemists that streamlines the ideation, modeling, and publication process. Though optimized for reticular chemistry, our tools are versatile and can be used in nonreticular materials research. We believe that mofdscribe will enable a more reliable, efficient, and comparable field of digital chemistry.