Performance of two complementary machine-learned potentials in modelling chemically complex systems. Npj Comput. Mater., (9):129, 2023. [PUMA: EXC2075 PN2 PN2-3(1) PN2-3A PN3 PN3-4 PN6 PN6-6 PN6A-1 curated] URL
Transfer learning for chemically accurate interatomic neural network potentials. Phys. Chem. Chem. Phys., (25)7:5383-5396, The Royal Society of Chemistry, 2023. [PUMA: EXC2075 PN3 PN3-4 PN6 PN6-3 PN6A-1 curated] URL
A Framework and Benchmark for Deep Batch Active Learning for Regression. Journal of Machine Learning Research, (24)164:1--81, 2023. [PUMA: EXC2075 PN6 PN6-3 PN6A-1 curated] URL
Exploring chemical and conformational spaces by batch mode deep active learning. Digital Discovery, (1):605-620, 2022. [PUMA: EXC2075 PN6 PN6-3 PN6A-1 curated]
Predicting properties of periodic systems from cluster data: A case study of liquid water. The Journal of Chemical Physics, (156)11:114103, 2022. [PUMA: EXC2075 PN3 PN3-4 PN6 PN6-3 PN6A-1 curated] URL
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments. J. Chem. Theory Comput., (18):1-12, 2022. [PUMA: EXC2075 PN3 PN3-4 PN6 PN6A-1 curated] URL
Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design. Machine Learning: Science and Technology, (2)3:035009, IOP Publishing, 2021. [PUMA: EXC2075 PN6 PN6A-1 curated]
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. Journal of Chemical Theory and Computation, (17)10:6658-6670, 2021. [PUMA: EXC2075 PN6 PN6-3 PN6A-1 curated] URL
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials. J. Chem. Theory Comput., (16):5410-5421, 2020. [PUMA: EXC2075 PN3 PN3-4 PN6 PN6A-1 curated] URL
Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption. Mon. Not. R. Astron. Soc., (499):1373-1384, 2020. [PUMA: EXC2075 PN3 PN3-4 PN6 PN6A-1 curated] URL