BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Vrije Universiteit Amsterdam//NONSGML v1.0//EN NAME:PhD defense D.D. Baum METHOD:PUBLISH BEGIN:VEVENT DTSTART:20261116T114500 DTEND:20261116T131500 DTSTAMP:20261116T114500 UID:phd-defense-d-d-baum@8F96275E-9F55-4B3F-A143-836282E12573 CREATED:20260924T234503 LOCATION:Main building VU, 1105, Auditorium, De Boelelaan, 1081 HV, Amsterdam SUMMARY:PhD defense D.D. Baum X-ALT-DESC;FMTTYPE=text/html:

Machine Learning for the Acceleration of Quantum Chemical Simulations

AI and Machine Learning can accelerate quantum chemical simulations even on a budget

For many applications, it is not necessary to use very large and complex AI models. My research shows that relativ ely simple models for specific quantum chemical problems can already yield good results.

My work is at the intersection of computational chemistry, quantum chemistry and artificial intelligenc e. Quantum chemical simulations can be used to investigate the proper ties of molecules without the need for a laboratory experiment for ev ery question. Such simulations can support research, but accurate cal culations often require a lot of computing time and computer power.

Therefore, I focused on the question of how artificial intellige nce can be used to perform certain quantum chemical calculations more efficiently. The goal was to obtain results of similar quality with less computational work.

My research shows that the most comple x AI method is not automatically the best choice. Which approach is m ost suitable depends on the problem, the available data and the desir ed accuracy. For some quantum chemical applications, simple models ma y be sufficient. For other applications, a combination of artificial intelligence and existing scientific models works better.

More information on the thesis to follow.

DESCRIPTION: Machine Learning for the Acceleration of Quantum Chemical Simulations

AI and Machine Learning can accelerate quantum chemi cal simulations even on a budget

For many applications, i t is not necessary to use very large and complex AI models. My resear ch shows that relatively simple models for specific quantum chemical problems can already yield good results.My work is at the in tersection of computational chemistry, quantum chemistry and artifici al intelligence. Quantum chemical simulations can be used to investig ate the properties of molecules without the need for a laboratory exp eriment for every question. Such simulations can support research, bu t accurate calculations often require a lot of computing time and com puter power.Therefore, I focused on the question of how artificial in telligence can be used to perform certain quantum chemical calculatio ns more efficiently. The goal was to obtain results of similar qualit y with less computational work.My research shows that the most comple x AI method is not automatically the best choice. Which approach is m ost suitable depends on the problem, the available data and the desir ed accuracy. For some quantum chemical applications, simple models ma y be sufficient. For other applications, a combination of artificial intelligence and existing scientific models works better.More informa tion on the thesis to follow. END:VEVENT END:VCALENDAR