2021
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| 1. | L. I. Vázquez-Salazar; E. D. Boittier; O. T. Unke; M. Meuwly Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Predictions of Tautomerization Energies Journal Article In: J. Chem. Theo. Comput., vol. 17, pp. 3687, 2021. @article{MM.vazquezsalazar.jctc:2021,
title = {Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Predictions of Tautomerization Energies},
author = {L. I. Vázquez-Salazar and E. D. Boittier and O. T. Unke and M. Meuwly},
url = {arXiv:2104.06099},
year = {2021},
date = {2021-05-31},
urldate = {2021-05-31},
journal = {J. Chem. Theo. Comput.},
volume = {17},
pages = {3687},
keywords = {machine learning, Quantum Chemistry, Tautomerization Energies},
pubstate = {published},
tppubtype = {article}
}
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| 2. | M. Meuwly Machine Learning for Chemical Reactions Journal Article In: Chem. Review, vol. 121, pp. 10218, 2021. @article{MM.meuwly.cr:2021,
title = {Machine Learning for Chemical Reactions},
author = {M. Meuwly},
year = {2021},
date = {2021-05-31},
urldate = {2021-05-31},
journal = {Chem. Review},
volume = {121},
pages = {10218},
keywords = {machine learning},
pubstate = {published},
tppubtype = {article}
}
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| 3. | S. Käser; E. D. Boittier; M. Upadhyay; M. Meuwly Transfer Learning to CCSD (T): Accurate Anharmonic Frequencies from Machine Learning Models Journal Article In: J. Chem. Theo. Comput., 2021. @article{MM.kaeser.jctc:2021,
title = {Transfer Learning to CCSD (T): Accurate Anharmonic Frequencies from Machine Learning Models},
author = {S. Käser and E. D. Boittier and M. Upadhyay and M. Meuwly},
year = {2021},
date = {2021-01-01},
journal = {J. Chem. Theo. Comput.},
keywords = {CCSD(T), machine learning},
pubstate = {published},
tppubtype = {article}
}
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| 4. | J. Arnold; J. C. San Vicente Veliz; D. Koner; N. Singh; R. J. Bemish; M. Meuwly Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System Journal Article In: J. Chem. Phys., vol. 156, pp. 034301, 2021. @article{MM.arnold.jcp:2021,
title = {Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System},
author = {J. Arnold and J. C. San Vicente Veliz and D. Koner and N. Singh and R. J. Bemish and M. Meuwly},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {J. Chem. Phys.},
volume = {156},
pages = {034301},
keywords = {machine learning, Reactive Atom-Diatom Collision System},
pubstate = {published},
tppubtype = {article}
}
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2020
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| 5. | S. Käser; D. Koner; A. S. Christensen; O. A. von Lilienfeld; M. Meuwly ML Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL and PhysNet Journal Article In: J. Phys. Chem. A, vol. 124, pp. 8853–8865, 2020. @article{MM.kaeser.jpca:2020,
title = {ML Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL and PhysNet},
author = {S. Käser and D. Koner and A. S. Christensen and O. A. von Lilienfeld and M. Meuwly},
year = {2020},
date = {2020-10-31},
journal = {J. Phys. Chem. A},
volume = {124},
pages = {8853–8865},
keywords = {Computational Vibrational Spectroscopy, machine learning, physnet},
pubstate = {published},
tppubtype = {article}
}
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| 6. | D. Koner; M. Meuwly Potential Energy Surfaces for Polyatomic Molecules from Energies and Gradients Using Reproducing Kernel Hilbert Space Interpolation Journal Article In: J. Chem. Theo. Comput., vol. 16, pp. 5474–5484, 2020. @article{MM.koner.jctc:2020,
title = {Potential Energy Surfaces for Polyatomic Molecules from Energies and Gradients Using Reproducing Kernel Hilbert Space Interpolation},
author = {D. Koner and M. Meuwly},
year = {2020},
date = {2020-01-01},
journal = {J. Chem. Theo. Comput.},
volume = {16},
pages = {5474–5484},
keywords = {machine learning, potential energy surfaces, reproducing kernel, RKHS},
pubstate = {published},
tppubtype = {article}
}
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| 7. | D. Koner; R. J. Bemish; M. Meuwly Dynamics on Multiple Potential Energy Surfaces: Quantitative Studies of Elementary Processes Relevant to Hypersonics Journal Article In: J. Phys. Chem. A, vol. 124, pp. 6255–6269, 2020. @article{MM.koner.jpca:2020,
title = {Dynamics on Multiple Potential Energy Surfaces: Quantitative Studies of Elementary Processes Relevant to Hypersonics},
author = {D. Koner and R. J. Bemish and M. Meuwly},
year = {2020},
date = {2020-01-01},
journal = {J. Phys. Chem. A},
volume = {124},
pages = {6255–6269},
keywords = {Hypersonics, machine learning, MD, Reactions},
pubstate = {published},
tppubtype = {article}
}
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| 8. | S. Käser; O. T. Unke; M. Meuwly Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces Journal Article In: New J. Phys., vol. 22, pp. 055002, 2020. @article{MM.kaeser.njp:2020,
title = {Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces},
author = {S. Käser and O. T. Unke and M. Meuwly},
year = {2020},
date = {2020-01-01},
journal = {New J. Phys.},
volume = {22},
pages = {055002},
keywords = {machine learning, Molecular Simulations, Neural Networks, Proton Transfer},
pubstate = {published},
tppubtype = {article}
}
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| 9. | O. T. Unke; D. Koner; S. Patra; S. Käser; M. Meuwly High-Dimensional Potential Energy Surfaces for Molecular Simulations Journal Article In: Mach. Learn. Sci. Tech., vol. 1, pp. 013001, 2020. @article{MM.unke.mlst:2020,
title = {High-Dimensional Potential Energy Surfaces for Molecular Simulations},
author = {O. T. Unke and D. Koner and S. Patra and S. Käser and M. Meuwly},
year = {2020},
date = {2020-01-01},
journal = {Mach. Learn. Sci. Tech.},
volume = {1},
pages = {013001},
keywords = {machine learning, Molecular Simulation, Molecular Simulations, potential energy surfaces},
pubstate = {published},
tppubtype = {article}
}
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2019
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| 10. | O. T. Unke; M. Meuwly
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges Journal Article In: J. Chem. Theo. Comput., vol. 15, pp. 3678-3693, 2019. @article{MM.unke.jctc:2019,
title = {PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges},
author = {O. T. Unke and M. Meuwly
},
year = {2019},
date = {2019-01-01},
journal = {J. Chem. Theo. Comput.},
volume = {15},
pages = {3678-3693},
keywords = {Force fields, machine learning},
pubstate = {published},
tppubtype = {article}
}
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