Thesis etd-06142018-093453 |
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Thesis type
Tesi di laurea magistrale
URN
etd-06142018-093453
Thesis title
Parameterization of inorganic anions in condensed phase by means of atomistic simulations and machine learning techniques
Department
INGEGNERIA DELL'INFORMAZIONE
Course of study
INGEGNERIA BIOMEDICA
Supervisors
.
relatore Bechini, Alessio
relatore Mancini, Giordano
relatore Mancini, Giordano
Keywords
- Atomistic Simulations
- Force Field
- Machine Learning
Graduation session start date
05/07/2018
Availability
Withheld
Release date
05/07/2088
Abstract (Inglese)
Abstract (Italiano)
Molecular Dynamics (MD) is the prime computational tool for the investigation of basic functional properties of biomolecules in a typical cellular environment, characterized by the presence of ions. The effectiveness of MD investigations depends on the quality of the adopted force field. This thesis work has the purpose to optimize the parameters of nonbonded force field of a chloride ion, Cl-, in soft matter, by means of machine learning techniques, without altering the functional form and the parameters of the force field of the other atoms in the system. The parameters obtained as the main results of this work can be easily integrated into consolidated Molecular Mechanics packages.
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