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.| File | Dimensione | Formato | |
|---|---|---|---|
|
104_MYTHOS.pdf
accesso aperto
Tipo:
Versione (PDF) editoriale / Version Of Record
Licenza:
Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione
7.7 MB
Formato
Adobe PDF
|
7.7 MB | Adobe PDF | Visualizza/Apri |
|
si_104_MYTHOS.pdf
accesso aperto
Tipo:
File Supplementare
Licenza:
Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione
4.71 MB
Formato
Adobe PDF
|
4.71 MB | Adobe PDF | Visualizza/Apri |
|
si_104_MYTHOS.zip
accesso aperto
Tipo:
File Supplementare
Licenza:
Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione
921.57 kB
Formato
Zip File
|
921.57 kB | Zip File | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


