MARVEL Machine Learning Incubator

Research Project

01.01.2018

- 31.05.2023

The aim of this project is to design and discover novel materials as well as their chemical properties based on data obtained from quantum-mechanical simulations. This data is used to develop two types of models: First, we consider deep learning models to predict the properties of individual molecules as surrogates for quantum mechanical simulations. Second, we develop deep latent variable models to generate novel materials on the basis of a set of user-defined properties.

Publications

Nesterov, Vitali et al. (2022) ‘Learning Invariances with Generalised Input-Convex Neural Networks’. Available at: https://doi.org/10.48550/arxiv.2204.07009.

URLs

Nesterov, Vitali, Wieser, Mario and Roth, Volker (2020) ‘3DMolNet: A Generative Network for Molecular Structures’, Arxiv [Preprint]. Cornell University. Available at: https://doi.org/10.48550/arxiv.2010.06477.

URLs

Wieser, Mario et al. (2018) ‘Learning Sparse Latent Representations with the Deep Copula Information Bottleneck’. ICLR: ICLR. Available at: https://openreview.net/forum?id=Hk0wHx-RW.

URLs

Members (3)

Volker Roth

Principal Investigator

Mario Wieser

Project Member

Vitali Nesterov

Project Member

Research Groups

Biomedical Data Analysis