Consultation and modelling\/simulation of crystal of hydrogenated castor oil with and without modifiers.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false},"gruppo":[],"class_list":["post-19696","projects","type-projects","status-publish","hentry"],"acf":{"title":"Consultation and modelling\/simulation of crystal of hydrogenated castor oil with and without modifiers","acronym":"P&G 1","project_uuid":"6197c1a0-b567-1032-95fe-0030485a3848","start_date":"14\/04\/2012","end_date":"22\/08\/2012","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