Projekt

Zurück zur Übersicht

Quantum Metadynamics

Titel Englisch Quantum Metadynamics
Gesuchsteller/in Parrinello Michele
Nummer 169429
Förderungsinstrument Projektförderung (Abt. I-III)
Forschungseinrichtung Istituto di scienze computazionali Facoltà di scienze informatiche Università della Svizzera italiana
Hochschule Università della Svizzera italiana - USI
Hauptdisziplin Physikalische Chemie
Beginn/Ende 01.01.2017 - 31.12.2019
Bewilligter Betrag 319'800.00
Alle Daten anzeigen

Alle Disziplinen (2)

Disziplin
Physikalische Chemie
Physik der kondensierten Materie

Keywords (6)

Quantum annealing; Quantum dynamics; Enhanced sampling; Matadynamics; Protein; Path integral

Lay Summary (Italienisch)

Lead
Le fasi iniziali che portano allo sviluppo di nuove sostanze, come ad esempio la ricerca di materiali con le proprieta' desiderate o lo sviluppo di nuovi principi attivi per i farmaci, consistono spesso in simulazioni esplorative fatte tramite un computer. Molti fenomeni di interesse attuale avvengono su scale di tempo che non sono accessibili tramite un semplice approccio di “forza bruta” per simulare il mondo microscopico e lo sviluppo di algoritmi di “esplorazione accelerata” e' oggi un campo di ricerca molto attivo.
Lay summary
Il nostro primo obiettivo e' l'adattamento di una tecnica di esplorazione accelerata chiamata Metadinamica e sviluppata all'interno del nostro gruppo. La Metadinamica verra' adattata per trattare sistemi in cui gli effetti quantistici non sono trascurabili e che dunque richiedono tecniche di simulazione quantistica per essere studiati. Gli effetti quantistici verranno anche utilizzati in maniera controllata per ottenere un nuovo algoritmo di esplorazione accelerata in grado di funzionare sia per sistemi classici che per sistemi quantistici.
Questo lavoro aumentera' l'arsenale disponibile per lo studio di reazioni chimiche a basse temperature e la ricerca delle conformazioni che possono assumere svariate proteine aiutando, per esempio,  a ottenere informazioni utili per megliorare la comprensione dei meccanismi biologici all'origine della vita e di diverse patologie.

Direktlink auf Lay Summary Letzte Aktualisierung: 23.11.2016

Verantw. Gesuchsteller/in und weitere Gesuchstellende

Mitarbeitende

Verbundene Projekte

Nummer Titel Start Förderungsinstrument
141828 Materials’ Revolution: Computational Design and Discovery of Novel Materials (MARVEL) 01.05.2014 Resource not found: 'c3456569-6e72-4910-9065-d5a0ca99b2cf'

Abstract

Many physical phenomena take place on a time scale that far exceeds what can be reached in atomistic simulations, strongly limiting the scope of an otherwise very powerful method [1-4]. The vast literature on the subject is evidence of the relevance of this problem [5]. In the last decade our group has been actively involved in developing methods that speed up sampling and calculate rates of transition between metastable states separated by large barriers. In particular we have introduced a method called Metadynamics [6, 7] and more recently a variationally enhanced sampling [8] (VES) approach that have proven to be extremely effective. When properly engineered both methods allow also the calculation of rates [9,10]. Thus far this effort has been mostly focused on classical systems. One of the aims of this project is to extend these sampling methods to quantum systems and calculate quantum rates. We shall use the Feynman’s Path Integral formulation [11, 12] of quantum statistical systems to map the quantum system into an isomorphic classical system. Neglecting quantum exchanges, each particle is mapped into a ring polymer of P beads. As P ? 8 the exact quantum limit is reached while the P = 1 case corresponds to the purely classical case [13, 14]. While in principle a quantum system is expected to surmount high barriers thanks to effects like zero point motion and tunneling, still in the presence of high barriers sampling remains severely hindered [15]. The few methods that have been proposed [16-22] for quantum systems presume conditions and or knowledge of the system that in complex situations are not available. Here we propose to develop a number of specifically designed methods to sample quantum systems that can be applied blindly to very generic systems. In preliminary investigations [15, 23] we have shown that this is possible. On the one hand we have been able to enhance sampling in quantum systems on the other we have shown that sampling quantum systems can be profitably used to sample classical systems. We plan to extend this approach in various directions including the introduction of Bose exchange processes [24]. The resulting software will be made available to the community. We want also to calculate rates for quantum transitions. We will follow two approaches: the first one at variance with previous efforts [25, 26] does not contain any approximation, the second one will be more approximated but computationally more expedite and capable of overcoming barriers where other methods fail.The project duration is scheduled for 3 years and it will involve one post-doctoral researcher and a doctoral student with a total working load of 180%.[1] R. A. Copeland, D. L. Pompliano and T. D. Meek, Nat. Rev. 5, 730 (2006).[2] S. Nunez, J. Venhorst and C. G. Kruse, Drug discovery today 17, 10 (2012).[3] A. C. Pan, D. W. Borhani, R. O. Dror and D. E. Shaw, Drug discovery today 18, 667 (2013).[4] R. J. Davey, S. L. M. Schroeder, and J. H. ter Horst, Angewandte Chemie 52, 2166 (2013).[5] A. Barducci, M. Bonomi and M. Parrinello, WIREs: Comput. Mol. Sci. 1, 826 (2011).[6] A. Barducci, G. Bussi, and M. Parrinello, Phys. Rev. Lett. 100, 020603 (2008).[7] O. Valsson, P. Tiwary and M. Parrinello Annu. Rev. Phys. Chem. 67 (2016), doi: 10.1146/annurev-physchem-040215-112229.[8] O. Valsson and M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014).[9] P. Tiwary and M. Parrinello, Phys. Rev. Lett. 111, 230602 (2013).[10] James McCarty, Omar Valsson, Pratyush Tiwary and Michele Parrinello, Phys. Rev. Lett. 115, 070601 (2015).[11] R. P. Feynman and A. R. Hibbs, Quantum Mechanics and Path Integrals, McGraw-Hill Companies; First Edition (1965).[12] M. Parrinello and A. Rahman, in Monte Carlo Methods in Quantum Problems, edited by M. H. Kalos, NATO ASI Series, Series C: Mathematical and Physycal Sciences, 125 (1982).[13] D. Chandler, P. G. Wolynes, J. Chem. Phys. 74, 4078 (1981).[14] M. Parrinello and A. Rahman, J. Chem. Phys. 80, 860 (1984).[15] R. Quhe, M. Nava, P. Tiwary and M. Parrinello, J. Chem. Theory Comput. 11, 1383 (2015).[16] P. Liu and B. J. Berne, J. Chem. Phys. 118, 2999 (2003).[17] G. Santoro and E. Tosatti, J. Phys. A: Math. Gen. 39, R393 (2006).[18] M. J. Gillan, Phys. Rev. Lett. 58, 563 (1987).[19] G. A. Voth, D. Chandler and W. H. Miller, J. Chem. Phys. 91, 7749 (1989).[20] I. R. Craig and D. E. Manolopoulus, J. Chem. Phys. 122, 084106 (2005).[21] J. Vanicek, W. H. Miller, J. F. Castillo and F. J. Aoiz, J. Chem. Phys. 123, 054108 (2005).[22] J. Kretchmer and T.F. Miller, J. Chem. Phys. 138, 134109 (2013).[23] M. Nava, R. Quhe, F. Palazzesi, P. Tiwary and M. Parrinello, J. Chem. Theory Comput. 11, 5114 (2015).[24] D. M. Ceperley, Rev. Mod. Phys. 67, 279 (1995).[25] S. Habershon, B. J. Braams and D. E. Manolopoulos, J. Chem. Phys. 127, 174108 (2007).[26] S. Jang, A. V. Sinitskiy and G. A. Voth, J. Chem. Phys. 140, 154103 (2014).
-