 | Dr. Michael Gastegger University of Vienna Institute of Theoretical Chemistry Währinger Str. 17 1090 Vienna Austria michael.gastegger@univie.ac.at +43 1 4277 52754 |
| Focus Area |
| Main focus of my work is the development of new protocols and algorithms for the computational de-novo design of artificial enzymes bearing novel functionalities. To this end, quantum chemical computations and bioinformatic approaches, such as evolutionary algorithms and machine learning, are combined in order to enhance and rationalize the computational enzyme design process. The current target of my modelling endeavours is the design of an enzyme for the ring-closing olefin metathesis of diallylether. |
| Publications |
Deep Learning Study of Tyrosine Reveals that Roaming can Lead to Photodamage Nat. Chem. 14 , 914–919, (2022), DOI: 10.1038/s41557-022-00950-z 9. J. Westermayr, M. Gastegger, P. Marquetand Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics J. Phys. Chem. Lett. 11, 3828-3834, (2020), DOI: 10.1021/acs.jpclett.0c00527 J. Westermayr, M. Gastegger, P. Marquetand Molecular Dynamics with Neural Network Potentials. In: Schütt K., Chmiela S., von Lilienfeld O., Tkatchenko A., Tsuda K., Müller KR. (eds) Machine Learning Meets Quantum Physics Lecture Notes in Physics, Springer Cham 968, 233-252, (2020), DOI: 10.1007/978-3-030-40245-7_12 7. J. Westermayr, M. Gastegger, M. Menger, S. Mai, L. González, P. Marquetand Machine Learning Enables Long Time Scale Molecular Photodynamics Simulations Chem. Sci. 10, 8100-8107, (2019), DOI: 10.1039/C9SC01742A Cover Image: J. Westermayr, M. Gastegger, M. Menger, S. Mai, L. González, P. Marquetand Exploring Density Functional Subspaces with Genetic Algorithms Chemical Monthly 150, 173-182, (2019), DOI: 10.1007/s00706-018-2335-3 5. M. Gastegger, P. Marquetand "Molecular dynamics with neural-network potentials" in K.T. Schütt, S. Chmiela, A. von Lilienfeld, A. Tkatchenko, K. Tsuda, K.-R. Müller (Eds): Machine Learning for Quantum Simulations of Molecules and Materials (submitted), (2018) M. Gastegger, P. Marquetand wACSF - Weighted Atom-Centered Symmetry Functions as descriptors in machine learning potentials J. Chem. Phys. 148, 241709, (2018), DOI: 10.1063/1.5019667 M. Gastegger, L. Schwiedrzik, M. Bittermann, F. Berzsenyi, P. Marquetand Machine learning molecular dynamics for the simulation of infrared spectra Chem. Sci. 8, 6924-6935, (2017), DOI: 10.1039/C7SC02267K M. Gastegger, J. Behler, P. Marquetand Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes J. Chem. Phys. 144, 194110, (2016), DOI: 10.1063/1.4950815 1. M. Gastegger, P. Marquetand High-dimensional neural network potentials for organic reactions and an improved training algorithm J Chem Theory Comput 11, 2187-2198, (2015), DOI: 10.1021/acs.jctc.5b00211 |