activity
20182026
most citedSE(3)-equivariant prediction of molecular wavefunctions and electronic densities

25 citations · 36 across the 6 of their papers we have counts for

collaborators

7 papers

physics.chem-ph2026

Accurate and Transferable Intermolecular Potential Based on Machine-Learned Molecular Electron Density

Dahvyd Wing, Mihail Bogojeski, Szabolcs Goger +2

Machine-learned force fields (MLFFs) contain many learnable parameters and therefore require large training datasets. This poses a challenge for developing highly accurate, general…

physics.chem-ph2026

Enhancing molecular dynamics with equivariant machine-learned densities

Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto +3

Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving elect…

physics.chem-ph2024

A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery

Parastoo Semnani, Mihail Bogojeski, Florian Bley +7

The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aid…

physics.chem-ph2021★ 25 cited

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities

Oliver T. Unke, Mihail Bogojeski, Michael Gastegger +3

Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Inst…

cs.LG2020

Learning more expressive joint distributions in multimodal variational methods

Sasho Nedelkoski, Mihail Bogojeski, Odej Kao

Data often are formed of multiple modalities, which jointly describe the observed phenomena. Modeling the joint distribution of multimodal data requires larger expressive power to…

cs.LG2020

Forecasting Industrial Aging Processes with Machine Learning Methods

Mihail Bogojeski, Simeon Sauer, Franziska Horn +1

Accurately predicting industrial aging processes makes it possible to schedule maintenance events further in advance, ensuring a cost-efficient and reliable operation of the plant.…

physics.comp-ph2018★ 11 cited

Efficient prediction of 3D electron densities using machine learning

Mihail Bogojeski, Felix Brockherde, Leslie Vogt-Maranto +4

The Kohn-Sham scheme of density functional theory is one of the most widely used methods to solve electronic structure problems for a vast variety of atomistic systems across diffe…