A5080296937- Machine Learning in Materials Science
- Spectroscopy Techniques in Biomedical and Chemical Research
- Electrochemical Analysis and Applications
- Graphene research and applications
- Graphene and Nanomaterials Applications
- Advanced biosensing and bioanalysis techniques
- Chemistry and Chemical Engineering
- MXene and MAX Phase Materials
- Advancements in Battery Materials
- Spectroscopy and Quantum Chemical Studies
- Spectroscopy and Laser Applications
- Molecular Junctions and Nanostructures
- Computational Drug Discovery Methods
- CO2 Reduction Techniques and Catalysts
- Nanopore and Nanochannel Transport Studies
- Spectroscopy and Chemometric Analyses
- Scientific Computing and Data Management
University of Bayreuth
2024
Fritz Haber Institute of the Max Planck Society
2023
Regional Centre of Advanced Technologies and Materials
2018-2022
Palacký University Olomouc
2018-2022
Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) significant computational human effort that must go into development validation potentials for each particular system interest; (ii) a general lack transferability from one chemical to next. Here, using state-of-the-art MACE architecture we introduce single general-purpose ML model,...
Many state-of-the art machine learning (ML) interatomic potentials are based on a local or semi-local (message-passing) representation of chemical environments. They, therefore, lack description long-range electrostatic interactions and non-local charge transfer. In this context, there has been much interest in developing ML-based equilibration models, which allow the rigorous calculation energetic response molecules materials to external fields. The recently reported kQEq method achieves by...
Graphene-based materials enable the sensing of diverse biomolecules using experimental approaches based on electrochemistry, spectroscopy, or other methods. Although basic was achieved, it had until now not been possible to understand and control biomolecules' structural morphological organization graphene surfaces (i.e. their stacking, folding/unfolding, self-assembly, nano-patterning). Here we present insight into in water, an RNA hairpin as a model system. We show that key parameters...
Vibrational spectroscopy is a cornerstone technique for molecular characterization and offers an ideal target the computational investigation of materials. Building on previous comprehensive assessments efficient methods infrared (IR) spectroscopy, this study investigates predictive accuracy efficiency gas-phase IR spectra calculations, accessible through combination modern semiempirical quantum mechanical transferable machine learning potentials. A composite approach prediction based...
Vibrational spectroscopy is a cornerstone technique for molecular characterization and offers an ideal target the computational investigation of materials. Building on previous comprehensive assessments efficient methods infrared (IR) spectroscopy, this study investigates predictive accuracy efficiency gas-phase IR spectra calculations, accessible through combination modern semiempirical quantum mechanical transferable machine learning potentials. A composite approach prediction based...
Many state-of-the art machine learning (ML) interatomic potentials are based on a local or semi-local (message-passing) representation of chemical environments. They therefore lack description long-range electrostatic interactions and non-local charge transfer. In this context, there has been much interest in developing ML-based equilibration models, which allow the rigorous calculation energetic response molecules materials to external fields. The recently reported kQEq method achieves by...
Graphene derivatives are an emerging and important class of promising materials because they can bear a wide variety functional groups, rendering them suitable for plethora applications, ranging from energy storage to sensorics. Further functionalisation these requires thorough understanding their reactivity at the molecular level organic groups close effectively infinite surface, which may affect reactivity. Nitrile grafted on graphene be easily hydrolysed carboxyl but resistant reduction...
Graphene derivatives are an emerging and important class of promising materials because they can bear a wide variety functional groups, rendering them suitable for plethora applications, ranging from energy storage to sensorics. Further functionalisation these requires thorough understanding their reactivity at the molecular level organic groups close effectively infinite surface, which may affect reactivity. Nitrile grafted on graphene be easily hydrolysed carboxyl but resistant reduction...