Εξειδίκευση τύπου :	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	MOFSynth-ADV: An Open-Source Engine for Synthesizability Evaluation of Metal-Organic Frameworks
Δημιουργός/Συγγραφέας:	Livas, Charalampos G [EL] Κλώντζας, Εμμανουήλ[EN] Klontzas, Emmanuel Trikalitis, Pantelis N Froudakis, George E
Ημερομηνία:	2026-04-29
Γλώσσα:	Αγγλικά
ISSN:	1549-9596 1549-960X
DOI:	10.1021/acs.jcim.6c00880
Άλλο:	42055774
Περίληψη:	We present MOFSynth-ADV, an advanced iteration of the MOFSynth tool designed to evaluate the synthetic feasibility of Metal-Organic Frameworks (MOFs). By integrating the Atomic Simulation Environment (ASE) to leverage extended tight-binding (xTB) and machine learning interatomic potential (MLIP) calculations, MOFSynth-ADV achieves geometry optimization and energy evaluations with significantly higher accuracy than classical force field approaches. Crucially, this update eliminates reliance on proprietary software, delivering a fully open-source solution optimized for high-throughput screening. Performance benchmarks reveal that the xTB module reduces unconverged cases by 73% while decreasing computational time by 13%, with MLIPs (specifically MACE-OFF) yielding comparable improvements. Validated against a benchmark set of experimentally reported MOFs, MOFSynth-ADV demonstrates superior structural and energetic prediction quality, effectively capturing the subtle chemical interactions essential for accurate synthesis prediction. Fast, modular, and easily deployable, this upgraded framework establishes a robust foundation for future machine learning and quantum-classical hybrid workflows in MOF discovery.
Τίτλος πηγής δημοσίευσης:	Journal of chemical information and modeling
Θεματική Κατηγορία:	[EL] Φυσική και θεωρητική χημεία[EN] Physical and theoretical chemistry [EL] Πληροφορική[EN] Information technology [EL] Ανόργανη χημεία[EN] Inorganic chemistry
Λέξεις-Κλειδιά:	Machine learning Materials Mathematical methods Metal organic frameworks Optimization
Χρηματοδότης:	European Union Hellenic Foundation for Research and Innovation (HFRI)
Χρηματοδοτικό πρόγραμμα:	MOST-H2 Toyota Motor Europe NV/SA Fourth Call for HFRI PhD Fellowships
Αναγνωριστικό χρηματοδοτικού προγράμματος:	101058547 9459
Κάτοχος πνευματικών δικαιωμάτων:	© 2026 The Authors. Published by American Chemical Society.
Όροι και προϋποθέσεις δικαιωμάτων:	This publication is licensed under CC-BY-NC-ND 4.0.
Ηλεκτρονική διεύθυνση στον εκδότη (link):	https://doi.org/10.1021/acs.jcim.6c00880
Εμφανίζεται στις συλλογές:	Ινστιτούτο Θεωρητικής και Φυσικής Χημείας (ΙΘΦΧ) - Επιστημονικό έργο