Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly. J. Phys. Chem. B, (128):3662-3676, 2024. [PUMA: A-1 A-3 A-5 A-7 theoretische stuttgart chemie 3 kaestner 6 kästner PN6 PN theochem] URL
Performance of two complementary machine-learned potentials in modelling chemically complex systems. Npj Comput. Mater., (9):129, 2023. [PUMA: chemie from:kgugeler pn6-a1 kaestner kästner pn6 theoretische EXC2075 stuttgart theochem] URL
Performance of two complementary machine-learned potentials in modelling chemically complex systems. Npj Comput. Mater., (9):129, 2023. [PUMA: EXC2075 chemie kaestner kästner pn6 pn6-a1 stuttgart theochem theoretische] URL
Transfer learning for chemically accurate interatomic neural network potentials. Phys. Chem. Chem. Phys., (25)7:5383-5396, 2023. [PUMA: EXC2075 chemie kaestner kästner pn6 stuttgart theochem theoretische]
Transfer learning for chemically accurate interatomic neural network potentials. Phys. Chem. Chem. Phys., (25)7:5383-5396, 2023. [PUMA: chemie kaestner kästner pn6 theoretische EXC2075 stuttgart from:danielborn theochem]
Exploring chemical and conformational spaces by batch mode deep active learning. Digital Discovery, (1):605-620, 2022. [PUMA: EXC2075 chemie kaestner kästner pn6 stuttgart theochem theoretische]
Exploring chemical and conformational spaces by batch mode deep active learning. Digital Discovery, (1):605-620, 2022. [PUMA: chemie kaestner kästner pn6 theoretische EXC2075 stuttgart from:danielborn theochem]
Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – II. Diffusion. Mon. Not. R. Astron. Soc., (510)2:3063-3070, 2022. [PUMA: theoretische EXC2075 stuttgart from:danielborn chemie kaestner kästner pn6 theochem] URL
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments. J. Chem. Theory Comput., (18):1-12, 2022. [PUMA: EXC2075 chemie from:danielborn kaestner koehn kästner köhn pn6 stuttgart theochem theoretische] URL
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments. J. Chem. Theory Comput., (18):1-12, 2022. [PUMA: EXC2075 chemie kaestner kästner pn6 stuttgart theochem theoretische] URL
Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – II. Diffusion. Mon. Not. R. Astron. Soc., (510)2:3063-3070, 2022. [PUMA: EXC2075 chemie kaestner kästner pn6 stuttgart theochem theoretische] URL
Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – II. Diffusion. Mon. Not. R. Astron. Soc., (510)2:3063-3070, 2021. [PUMA: theoretische EXC2075 stuttgart from:danielborn chemie kaestner kästner pn6 theochem] URL
Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations. Mach. Learn.: Sci. Technol., (2):031001, 2021. [PUMA: EXC2075 chemie from:danielborn kaestner kästner pn6 stuttgart theochem theoretische] URL
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. J. Chem. Theory Comput., (17)10:6658-6670, 2021. [PUMA: EXC2075 chemie from:danielborn kaestner kästner pn6 stuttgart theochem theoretische] URL
Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design. Mach. Learn.: Sci. Technol., (2):035009, 2021. [PUMA: EXC2075 chemie from:danielborn kaestner kästner pn6 stuttgart theochem theoretische] 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 chemie from:danielborn kaestner kästner pn6 stuttgart theochem theoretische] URL
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. J. Chem. Theory Comput., (17)10:6658-6670, 2021. [PUMA: EXC2075 chemie kaestner kästner pn6 stuttgart theochem theoretische] URL
Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations. Mach. Learn.: Sci. Technol., (2):031001, 2021. [PUMA: EXC2075 chemie kaestner kästner pn6 stuttgart theochem theoretische] URL
Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations. Mach. Learn.: Sci. Technol., (2):031001, 2021. [PUMA: EXC2075 chemie from:danielborn kaestner kästner pn6 stuttgart theochem theoretische] URL
Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design. Mach. Learn.: Sci. Technol., (2):035009, 2021. [PUMA: EXC2075 chemie kaestner kästner pn6 stuttgart theochem theoretische] URL