Molecular dynamics on machine-learned potential energy surface

Abstract

Za modeliranje materijala iz prvih principa danas se predominantno koristi teorija funkcionala gustoće, koja pruža dobar omjer preciznosti i računalne efikasnosti. Međutim, zbog trenutnih računalnih ograničenja, obično je prezahtjevno modelirati dinamiku sustava s desecima atoma. Jedna od obećavajućih metoda za približavanje simulacija realnim sustavima i povećanje skala na kojima je moguće provoditi dinamiku je interpolacija potencijala dobivenog s teorijom funkcionala gustoće pomoću neuronskih mreža. U ovom diplomskom radu smo simulirali sustav molekula ugljičnog monoksida adsorbiranih na površini paladija ozračenoj femtosekundnim laserskim pulsom. Prikazali smo proces strojnog učenja te implementirali molekularnodinamičke simulacije u modelu Langevinove dinamike s vremenski ovisnim temperaturama.Nowadays, density functional theory is the predominant tool used in first-principles material modeling, providing an adequate ratio between precision and computational efficiency. However, because of current computational limitations, it is usually too demanding to simulate dynamics of systems consisting of tens of atoms. A promising method for reducing the gap between simulations and realistic systems and extending the scales across which it is possible to simulate dynamics is interpolation of potentials calculated with density functional theory via neural networks. In this master’s thesis we have simulated a system of carbon monoxide molecules adsorbed on palladium irradiated with a femtosecond laser pulse. We have shown the process of machine-learning and implemented molecular-dynamics simulations in the Langevin dynamics model with time dependent temperatures