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Tesi etd-06142018-093453


Thesis type
Tesi di laurea magistrale
Author
PELUSO, MATTEO
URN
etd-06142018-093453
Title
Parameterization of inorganic anions in condensed phase by means of atomistic simulations and machine learning techniques
Struttura
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA BIOMEDICA
Supervisors
relatore Bechini, Alessio
relatore Mancini, Giordano
Parole chiave
  • Atomistic Simulations
  • Force Field
  • Machine Learning
Data inizio appello
05/07/2018;
Consultabilità
Secretata d'ufficio
Data di rilascio
05/07/2088
Riassunto analitico
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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