We introduce a new computational approach for predicting organic crystalline structures on flat surfaces, an essential step in designing and optimizing thin-film systems for electronic devices. Based on molecular mechanics and molecular dynamics simulations, and implemented in a user-friendly Python program, this method enables a sequential layer-by-layer analysis of crystalline formation, thus allowing to identify surface-induced polymorphs (SIPs) and to study the transition between surface and bulk structures. A validation against six diverse test cases demonstrated a good match with experimental crystalline parameters and arrangements, underscoring the reliability of the method in identifying the most relevant polymorphs for a given molecule.

Lorini, E., Walzer, K., Pfeiffer, M., Muccioli, L. (2025). MYTHOS: A Python Interface for Surface Crystal Structure Prediction of Organic Semiconductors. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 65(14), 7619-7631 [10.1021/acs.jcim.5c00669].

MYTHOS: A Python Interface for Surface Crystal Structure Prediction of Organic Semiconductors

Lorini E.
;
Muccioli L.
2025

Abstract

We introduce a new computational approach for predicting organic crystalline structures on flat surfaces, an essential step in designing and optimizing thin-film systems for electronic devices. Based on molecular mechanics and molecular dynamics simulations, and implemented in a user-friendly Python program, this method enables a sequential layer-by-layer analysis of crystalline formation, thus allowing to identify surface-induced polymorphs (SIPs) and to study the transition between surface and bulk structures. A validation against six diverse test cases demonstrated a good match with experimental crystalline parameters and arrangements, underscoring the reliability of the method in identifying the most relevant polymorphs for a given molecule.
2025
Lorini, E., Walzer, K., Pfeiffer, M., Muccioli, L. (2025). MYTHOS: A Python Interface for Surface Crystal Structure Prediction of Organic Semiconductors. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 65(14), 7619-7631 [10.1021/acs.jcim.5c00669].
Lorini, E.; Walzer, K.; Pfeiffer, M.; Muccioli, L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/1020672
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