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Lausanne, EPFL, 2020.DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening
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Machine Learning Meets Quantum Physics; Springer International Publishing, 2020.Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems
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2019-03-31. National Meeting of the American-Chemical-Society (ACS), Orlando, FL, Mar 31-Apr 04, 2019.Energy Relaxation and Thermal Diffusion in Infrared Pump-Probe Spectroscopy of Hydrogen-Bonded Liquids
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Frontiers In Materials. 2019-04-05. Vol. 6, p. 53. DOI : 10.3389/fmats.2019.00053.Equation of State of Fluid Methane from First Principles with Machine Learning Potentials
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International Journal Of Quantum Chemistry. 2019-05-05. Vol. 119, num. 9, p. e25872. DOI : 10.1002/qua.25872.Transferable Machine-Learning Model of the Electron Density
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Lausanne, EPFL, 2019.Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations
The Journal of Physical Chemistry B. 2018. Vol. 122, num. 44, p. 10158-10171. DOI : 10.1021/acs.jpcb.8b06433.Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction
Chemistry of Materials. 2018. Vol. 30, num. 13, p. 4361-4371. DOI : 10.1021/acs.chemmater.8b01621.Chemical shifts in molecular solids by machine learning
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Physical Chemistry Chemical Physics. 2018. Vol. 20, num. 47, p. 29661-29668. DOI : 10.1039/C8CP05921G.Approximating Matsubara dynamics using the planetary model: Tests on liquid water and ice
The Journal of Chemical Physics. 2018. Vol. 148, num. 10, p. 102336. DOI : 10.1063/1.5004808.Hydrogen Diffusion and Trapping in α-Iron: The Role of Quantum and Anharmonic Fluctuations
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2018-01-01. 7th International Workshop on Electron-Volt Neutron Spectroscopy, Rome, ITALY, Nov 07-08, 2017. p. 012003. DOI : 10.1088/1742-6596/1055/1/012003.Comment on “Water- water correlations in electrolyte solutions probed by hyper-Rayleigh scattering” [J. Chem. Phys. 147, 214505 (2017)]
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2018-08-19. 256th National Meeting and Exposition of the American-Chemical-Society (ACS) – Nanoscience, Nanotechnology and Beyond, Boston, MA, Aug 19-23, 2018.Theoretical prediction of the homogeneous ice nucleation rate: disentangling thermodynamics and kinetics
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2018-08-19. 256th National Meeting and Exposition of the American-Chemical-Society (ACS) – Nanoscience, Nanotechnology and Beyond, Boston, MA, Aug 19-23, 2018.Revealing the Transient Concentration of CO2 in a Mixed-Matrix Membrane by IR Microimaging and Molecular Modeling
Angewandte Chemie-International Edition. 2018. Vol. 57, num. 18, p. 5156-5160. DOI : 10.1002/anie.201713160.Enhanced gas separation performance of 6FDA-DAM based mixed matrix membranes by incorporating MOF UiO-66 and its derivatives
JOURNAL OF MEMBRANE SCIENCE. 2018. Vol. 558, p. 64-77. DOI : 10.1016/j.memsci.2018.04.040.Applications of machine learning for studying local solvation environments
2018. 255th National Meeting and Exposition of the American-Chemical-Society (ACS) – Nexus of Food, Energy, and Water, New Orleans, LA, Mar 18-22, 2018.Early Stages of Precipitation In Aluminum Alloys by First-Principles and Machine-Learning Atomistic Simulations
Lausanne, EPFL, 2018.Communication: Computing the Tolman length for solid-liquid interfaces
The Journal of Chemical Physics. 2018. Vol. 148, num. 23, p. 231102. DOI : 10.1063/1.5038396.Mapping uncharted territory in ice from zeolite networks to ice structures
Nature Communications. 2018. Vol. 9, num. 1, p. 2173. DOI : 10.1038/s41467-018-04618-6.Anisotropy of the Proton Momentum Distribution in Water
The Journal of Physical Chemistry. 2018. Vol. B122, num. 22, p. 6048-6054. DOI : 10.1021/acs.jpcb.8b03896.Fast-forward Langevin dynamics with momentum flips
The Journal of Chemical Physics. 2018. Vol. 148, num. 18, p. 184109. DOI : 10.1063/1.5029833.Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
The Journal of Chemical Physics. 2018. Vol. 148, num. 24, p. 241730. DOI : 10.1063/1.5024611.An Automatic, Data-Driven Definition of Atomic-Scale Structural Motifs
Lausanne, EPFL, 2018.Analyzing Fluxional Molecules Using DORI
Journal of Chemical Theory and Computation. 2018-03-24. Vol. 14, num. 5, p. 2370-2379. DOI : 10.1021/acs.jctc.7b01176.Computing the absolute Gibbs free energy in atomistic simulations: Applications to defects in solids
Physical Review B. 2018. Vol. 97, num. 5, p. 054102. DOI : 10.1103/PhysRevB.97.054102.Recognizing Local and Global Structural Motifs at the Atomic Scale
Journal of Chemical Theory and Computation. 2018. Vol. 14, num. 2, p. 486-498. DOI : 10.1021/acs.jctc.7b00993.Machine learning for the structure–energy–property landscapes of molecular crystals
Chemical Science. 2018. Vol. 9, num. 5, p. 1289-1300. DOI : 10.1039/C7SC04665K.Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
Physical Review Letters. 2018. Vol. 120, num. 3, p. 036002. DOI : 10.1103/PhysRevLett.120.036002.Nuclear quantum effects enter the mainstream
Nature Reviews Chemistry. 2018. Vol. 2, num. 3, p. 0109. DOI : 10.1038/s41570-017-0109.Chemical machine learning with kernels: The key impact of loss functions
2018