top of page
  • Katherine Hollingsworth

Webinar: Δ-Machine Learning beyond DFT: from phase transitions to quantum paraelectricity & CO...

UGM Plenary Speaker Spotlight

Tuesday, we host Dr. Carla Verdi; Faculty at University of Vienna, Austria and Dr. Georg Kresse; Professor at University of Vienna, Austria

Δ-Machine Learning beyond DFT: from phase transitions to quantum paraelectricity & CO adsorption


Machine-learned force fields (MLFFs) enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. Two major challenges, however, are i) the accurate description of anharmonic interactions, which are crucial for predicting key thermodynamic properties such as the phase transitions and lattice thermal conductivity in solids, and ii) that MLFFs are generally trained from density-functional theory (DFT) data and thus suffer the same limitations as DFT.

This talk will discuss the on-the-fly learning technique implemented in VASP, based on molecular dynamics and Bayesian inference, and a Δ-machine learning approach that allows to generate MLFFs with beyond-DFT accuracy at an affordable computational cost. Specifically, we train MLFFs based on the random phase approximation (RPA).

Three applications will be discussed. First, for the paradigmatic example of zirconia, an important transition metal oxide, we will show that our MLFF correctly captures the temperature-induced phase transitions below the melting point. We also calculate the heat transport on the basis of Green-Kubo theory, accounting for anharmonic effects to all orders. Second, we will focus on strontium titanate, a prototypical perovskite oxide with strongly anharmonic lattice dynamics and a building block for a variety of technologies. We employ MLFFs in combination with the stochastic self-consistent harmonic approximation method in order to investigate the cubic to tetragonal transition and the quantum paraelectric behavior at low temperature, which is accurately described by the RPA. Third, we investigate carbon monoxide (CO) adsorption on transition metal surfaces, an important problem in surface science and catalysis where DFT is often inaccurate. We show that our RPA-derived MLFF is capable to accurately predict the Rh(111) surface energy, CO adsorption site preference, as well as adsorption energies at different coverages. Taken together, the results demonstrate the feasibility of many-body calculations of finite-temperature properties of materials.


Dr. Carla Verdi

Carla Verdi received her doctorate in Materials from the University of Oxford in 2017, where she remained as a post-doctoral researcher until 2018. In 2019 she took up her current University Assistant position at the University of Vienna. Her main research interests are in the field of computational materials physics, where she specializes in the first-principles theory of the electron-phonon coupling and polaron physics, and in the application of machine-learning models for finite-temperature simulations of solids.

Professor Georg Kresse

Georg Kresse completed his doctoral thesis at the Institute for Theoretical Physics of the Vienna University of Technology in 1993 under the supervision of Jürgen Hafner. He then worked as a scientific assistant in Vienna and held a postdoctoral position at Staffordshire University with Mike Gillan. After his habilitation at the Vienna University of Technology in 2001, he was offered a full professorship from Oxford University as well as from the University of Vienna. He accepted the chair for Computational Quantum Mechanics in Vienna in 2007. Since 2011 Kresse is a full member of the Austrian Academy of Sciences and since 2012 of the International Academy of Quantum Molecular Sciences. He is the recipient of several awards, including the 2003 "START Grant" of the Austrian Science Fund (FWF), the "Hellmann Preis" of the Internationale Working group for Theoretical Chemistry, and the 2016 Kardinal-Innitzer-Preis.

His main scientific focus lies in the fields of Theoretical Solid State Physics, Surface Sciences and Computational Materials Physics. His work on ab initio density functional theory for solids, liquid and amorphous systems and surfaces has contributed significantly to basic and applied research and has shaped the application of density functional theory worldwide. Kresse is the main author of the computer code "VASP" (Vienna ab initio simulation package) which his research group develops. VASP is the internationally most widely used program for quantum mechanical simulations of condensed matter. The publications on which this code is based received between 30.000 and 50.000 citations each and are among the 100 most cited research articles worldwide.

Until mid 2019, Kresse directed the Special Research Area "Vienna Computational Materials Laboratory" funded by the Austrian Science Fund. The main goal of this large collaborative project was the precise description of electron interactions in solids and real materials. His current research focus is on the accurate prediction of mechanical, electronic and optical properties in condensed matter beyond simple mean field methods, a research area to which he has already made significant contributions. Georg Kresse is the author of about 400 research articles and has an h-index of over 110.

This year's plenary talks are open to everyone.

Other internationally renowned speakers at the User Group Meeting include:

  • Jeff Grossman (Massachusetts Institute of Technology, USA)

  • Jörg Behler (University of Göttingen, Germany)

  • Jozef Bicerano (Bicerano & Associates Consulting, LLC, USA)

Register Now

Webinar Sessions

Wednesday, October 12*: 7:00 am PDT / 10:00 am EDT USA 3:00 pm GMT England 4:00 pm Europe CEST 7:30 pm India (IST)

This lecture will include a live Question and Answer session directly following the presentation.

*Recording and Slides

Registrations will also include a link to the recording and slides after the sessions end.



bottom of page