Generalization of long-range machine learning potentials in complex chemical spaces
Digital Discovery, Vol. 5, pp. 2195-2208
| Adresse | Multiscale Modeling of Fluid Materials Maschinenwesen, TUM Boltzmannstr. 15 85748 Garching bei München, Deutschland |
| Büro | Gebäude 1, 2. Stock, Raum 5501.02.137 |
| Telefon | +49 (89) 289 - 55301 |
| m.sanocki@tum.de |
Research Interests
- Machine Learning
- Graph Neural Networks
- Computational Chemistry
- Metal Organic Frameworks (MOF)
Education
| 2024 | MSc. Computational Science University of Amsterdam |
| 2024 | Exchange program, University of British Columbia |
| 2022 | BSc. Chemistry University of Warschau |
| 2021 | BSc. Quantitive Methods Warsaw School of Economics |
Publications
2 Publications
Learning non-local molecular interactions via equivariant local representations and charge equilibration
npj Computational Materials, Vol. 11