Method Development
- Density functional global optimisation
- DFT software development and implementation (TURBOMOLE)
- Development of neural network potentials and active/delta learning methods
- Machine learning based enhanced sampling schemes for reactive modelling
- Development of machine learning predictors of spectroscopic observables
- Grand Canonical Monte Carlo schemes
Selected publications on the topic
M. Sipka
A. Erlebach
L. Grajciar
Constructing Collective Variables Using Invariant Learned Representations
Constructing Collective Variables Using Invariant Learned Representations
Journal of Chemical Theory and Computation (PMID: 36696574),
19(3),
2023
A. Erlebach
P. Nachtigall
L. Grajciar
Accurate large-scale simulations of siliceous zeolites by neural network potentials
Accurate large-scale simulations of siliceous zeolites by neural network potentials
npj Computational Materials,
8(1),
2022
L. Grajciar
C. J. Heard
A. A. Bondarenko
M. V. Polynski
J. Meeprasert
E. A. Pidko
P. Nachtigall
Towards operando computational modeling in heterogeneous catalysis
Towards operando computational modeling in heterogeneous catalysis
Chemical Society Reviews,
47(22),
2018
R. \Lazarski
A. M. Burow
L. Grajciar
M. Sierka
Density functional theory for molecular and periodic systems using density fitting and continuous fast multipole method: Analytical gradients
Density functional theory for molecular and periodic systems using density fitting and continuous fast multipole method: Analytical gradients
Journal of Computational Chemistry,
37(28),
2016
