Workshops

Learning quantum potential energy surfaces: multiple descriptors, many body expansions, baselines

Collective Variables in Quantum Mechanics and Machine Learning

An electronically coarse grained model including all quantum mechanical fluctuations necessary for long-range forces describes water’s properties from ice to the supercritical regime

Towards controllable van der Waals interactions: the importance of collective charge dynamics at the nanoscale

Hierarchical Tensor Approximation, DMRG and Combination with a Multi-Reference Coupled Cluster Method

Combining model reduction methods and data assimilation tools for the approximation of the solution to parameter dependent PDE’s : Application to quantum mechanics

Extracting Chemical Information from Quantum Many-Particle Physics

Predicting Properties and Electronic Structure of Inorganic Materials with Machine Learning

DFT and ML: The role of collective variables

Toward accurate, efficient, and reliable interatomic potentials

Effect of Crystal Packing on the Electronic Properties of Molecular Crystals

Grain Boundary Physics, Machine Learning, and the "SOAP" Formalism

Reducing complexity by implicit solvation models.

Density functional perturbation theory for large systems

Inference-Boosted First Principles Molecular Dynamics

Combination of physics-based and data-driven methods to parametrize polarizable intermolecular potentials across small organic molecules

Virtual Screening for High Carrier Mobility in Organic Semiconductors

by Stefan Chmiela, Grégoire Ferré and Jan Hermann

The density-to-pair-density map in density functional theory

Ensemble-Average Representation of Pt clusters in Conditions of Catalysis, accessed through GPU Accelerated Deep Neural Network Fitting Global Optimization