BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Physically constrained machine learning: from single-particle Hami
 ltonians to electronic excitations   - Jigyasa Nigam ( Swiss Federal Insti
 tute of Technology Lausanne (EPFL))
DTSTART:20240304T143000Z
DTEND:20240304T150000Z
UID:TALK212803@talks.cam.ac.uk
CONTACT:Eszter Varga-Umbrich
DESCRIPTION:Machine learning techniques often follow the end-to-end approa
 ch in that they estimate outputs of quantum mechanical calculations such a
 s structural energies or dipole moments based on geometric descriptions of
  the underlying structure. \nFollowing a recent paradigm shift of blurring
  the distinction between explicit quantum mechanical and modeling steps\, 
 there has been an interest\, instead\, in machine learning the ingredients
  of electronic structure\, such as the effective single-particle Hamiltoni
 an from which properties of interest may be derived.\nIn this talk\, I wil
 l describe how we can leverage existing techniques in the framework of ato
 m-centered density representations (ACDCs) to model electronic Hamiltonian
 s[1]. I will motivate the merits of this symbiotic integration of fundamen
 tal physical relations with data-driven methods\, not only in terms of the
 ir accuracy and transferability\, but also on their role in the prediction
  of more complex properties such as electronic excitations [2].\n\n[1] J. 
 Nigam\, M. Willatt\, M. Ceriotti\, JCP 156\, 014115\, 2022\n[2] E. Cignoni
 \, D. Suman\, J. Nigam et al. arXiv:2311.00844 (Accepted)
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
END:VEVENT
END:VCALENDAR