In this project CRS4 commissioned to external to perform a ‘in silico’ cocrystal screening. Aim of the project is the prediction of the stability of cocrystals. Very often chemical industry is interest to combine a active pharmaceutical ingredient with another substance to modify the properties of the final drug. In this project the stability of a specific API with 20 other possible coformers was estimated.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false},"gruppo":[],"class_list":["post-19688","projects","type-projects","status-publish","hentry"],"acf":{"title":"Consultation and modelling of 11 cocrystals and prediction of the likeliness of their stability","acronym":"COCRYS 2","project_uuid":"61987eb0-b567-1032-95fe-0030485a3848","start_date":"12\/03\/2013","end_date":"02\/04\/2013","funder":"","partners":"","team_members":[],"proj_website":"","project_status":"Completed","project_coordinator":[{"ID":10237,"post_author":"4","post_date":"2025-06-30 17:23:18","post_date_gmt":"2025-06-30 15:23:18","post_content":"Dr. Detlef Walter Maria Hofmann is a computational chemist specializing in crystal structure prediction, data mining in crystallography, and machine learning applications in chemistry. His research focuses on developing and applying computational methods, including machine learning algorithms, to predict and understand crystal structures and their properties. He has made significant contributions to the field of crystal structure prediction, participating in and analyzing results from multiple blind tests. His work involves developing force fields, analyzing intermolecular interactions, and predicting phase diagrams using data mining techniques.Dr. Hofmann's expertise extends to applying machine learning techniques like clustering, anomaly detection, and supervised learning to crystallographic data. He explores the use of machine learning on experimental crystal structures to parameterize models of Gibbs energy in computational crystallography. This interdisciplinary approach combines advanced computational methods with data-driven insights to address challenging problems in crystallography.His academic background includes a Dr. rer. nat. in theoretical chemistry from Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg. He has held research and teaching positions at several universities in Germany, including Humboldt-Universit\u00e4t zu Berlin, Goethe University Frankfurt, Friedrich-Alexander-University Erlangen-N\u00fcrnberg, and Freie Universit\u00e4t Berlin. He has also worked at the Fraunhofer-Institut f\u00fcr Algorithmen und Wissenschaftliches Rechnen SCAI and currently works at CRS4 in Pula, Italy. His educational experience also includes studies at the University of Naples Federico II and Sapienza University of Rome. Dr. Hofmann's publications reflect his active engagement in the field, with numerous peer-reviewed articles and contributions to book chapters on topics ranging from crystal structure prediction methods to data mining in crystallography. His work is frequently published in leading journals such as Acta Crystallographica, Journal of Chemical Physics, and Crystal Growth & Design. He is also associated with the FlexCryst project, which focuses on developing a general force field by machine learning on experimental crystal structures.","post_title":"Detlef W. M. Hofmann","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"detlef-w-m-hofmann","to_ping":"","pinged":"","post_modified":"2026-04-09 20:11:30","post_modified_gmt":"2026-04-09 18:11:30","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.crs4.it\/people\/detlef-w-m-hofmann\/","menu_order":0,"post_type":"people","post_mime_type":"","comment_count":"0","filter":"raw"}]},"yoast_head":"\n