BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Fostering accuracy in modelling materials and molecular complexes 
 with quantum Monte Carlo - Andrea Zen\, Universita' di Napoli Federico II
DTSTART:20220720T103000Z
DTEND:20220720T110000Z
UID:TALK176507@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:Computer simulations are becoming useful in providing insight 
 in the physical and chemical processes taking places in nature. Simulation
 s yield molecular level understanding\, which is often complementary infor
 mation to the understanding provided by experimental investigations. Yet\,
  they are only useful when they can accurately model the physical system. 
 High accuracy is often obtained by resorting to first principles\, and by 
 modelling the quantum mechanics features of the system of interest at the 
 atomic level.\nThriving nanotechnologies and exciting experiments pose big
  challenges to computational approaches. On the one hand\, the systems to 
 be simulated are large and computationally expensive\, and their physical 
 and thermal properties require sampling of a large phase space (using mole
 cular dynamics or other techniques). On the other hand\, the high accuracy
  required to evaluate inter-atomic interactions often means using very acc
 urate and expensive approaches to solve the Schrödinger equation. \nHighe
 r accuracy can be fostered by employing diffusion quantum Monte Carlo (QMC
 )\, which is one of the most accurate approaches available to assess the g
 round state electronic states and their properties in molecular systems\, 
 solids and surfaces. Pairing QMC with computationally cheaper approaches a
 nd machine learning techniques allows to bring this accuracy to large syst
 ems and to investigate many complex phenomena.\nWe see here the applicatio
 n to the phases of bulk ice\, the phases of nano-confined water\, and the 
 study of molecular complexes.\nMoreover\, we discuss recent methodological
  improvements in QMC\, and the present and future challenges for QMC appro
 aches.
LOCATION:Cambridge University Engineering Department\, Lecture Theater 2
END:VEVENT
END:VCALENDAR
