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SUMMARY:"Machine learning in chemistry and beyond" (ChE-650) seminar by Se
 reina Riniker (ETH Zurich)
DTSTART:20211123T151500
DTEND:20211123T161500
DTSTAMP:20260510T111813Z
UID:52b38e2f62bfe6c9186506e76968a36114be0d617f8890c11ce6f1e9
CATEGORIES:Conferences - Seminars
DESCRIPTION:Sereina Riniker is currently Associate Professor of Computati
 onal Chemistry at the Department of Chemistry and Applied Biosciences of 
 ETH Zurich. \nUsing machine learning for molecular dynamics simulations\n
 \nFrom simple clustering techniques to sophisticated neural networks\, the
  use of machine learning has become a valuable tool in many fields of chem
 istry in the past decades. Here\, we describe different ways in which we e
 xplore the combination of machine learning (ML) and molecular dynamics (MD
 ) simulations. One topic focuses on how the information in MD simulations 
 can be encoded as input to train ML models for the quantitative understand
 ing of molecular systems. Molecular dynamics fingerprints (MDFP) represent
  an orthogonal description of molecules compared to topological fingerprin
 ts. The concept of the MDFPs is highly versatile\, depending on the proper
 ty to be predicted\, different systems can be simulated and different prop
 erties can be extracted from the MD simulations. The second topic address
 es the utilization of ML to improve the set-up\, interpretation\, as well 
 as accuracy of MD simulations. In classical MD simulations\, the physical 
 interactions between atoms are described with an empirical force field. Th
 is involves a large number of parameters for each molecule\, which are fit
 ted to quantum-mechanical (QM) or available experimental data. There is a 
 need for more accurate and general force fields. In this context\, we demo
 nstrate how ML approaches can aid in force-field development\, e.g. for th
 e improved generation of partial charges for organic molecules. In the thi
 rd part\, we explore the use of ML for increasing the speed and accuracy o
 f QM/MM MD simulations.\n 
LOCATION:https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUW
 JyQT09
STATUS:CONFIRMED
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