29/11 -1/12, 2023
The SMLQC 2023 is organized in five thematic sessions reflecting relevant trends of machine learning application to quantum chemistry.
- The first theme is using ML for learning the very core of quantum chemistry – the wavefunction, either via unsupervised or supervised approaches, which can be then used to derive the required molecular and material properties.
- The second thematic session is one of the biggest and most mature – applications of ML as a force field which can in turn be employed for such typical simulations as geometry optimizations and dynamics.
- The third session is dedicated to highlighting recent progress in improving electronic structure methods with ML.
- The fourth theme is more of applied nature and will deal with advances in ML for molecular engineering and materials discovery with desired quantum chemical properties.
- The final, fifth, thematic session will give the stage to highlighting how ML can be used to obtain not just numbers but insights through unsupervised learning and interpretable ML as well as performing analysis of molecular structure through simulating and interpreting spectra.
Mid August: Abstract submission opens
31 October: Early Bird deadline
24 November: Poster abstract submission closes
Chao Zhang, Uppsala University, Sweden
Ignacio F. Galván, Uppsala University, Sweden
Roland Lindh, Uppsala University, Sweden