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  Diego Inostroza. Propuesta de un Método Acelerado utilizando Descriptores de Reactividad para Explorar Superficies de Energía Potencial en Clúster Atómicos. Coordination chemistry. Sorbonne Université; Universidad Andrés Bello (Santiago), 2023. Español. ⟨NNT : 2023SORUS514⟩. ⟨tel-04457279⟩
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  Maxime Herda, Pierre Monmarché, Benoît Perthame. Wasserstein contraction for the stochastic Morris-Lecar neuron model. 2024. ⟨hal-04170316v2⟩
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  Agathe Fayet. Étude théorique et expérimentale du réarrangement de Hock. Chimie organique. Sorbonne Université, 2023. Français. ⟨NNT : 2023SORUS425⟩. ⟨tel-04304298⟩
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  Pier Paolo Poier, Théo Jaffrelot Inizan, Olivier Adjoua, Louis Lagardère, Jean-Philip Piquemal. Accurate Deep Learning-aided Density-free Strategy for Many-Body Dispersion-corrected Density Functional Theory. Journal of Physical Chemistry Letters, 2022, 13 (19), pp.4381-4388. ⟨10.1021/acs.jpclett.2c00936⟩. ⟨hal-03624156⟩
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  Théo Jaffrelot Inizan. Advancing Large-Scale Molecular Dynamics through Machine Learning. Theoretical and/or physical chemistry. Sorbonne Université, 2023. English. ⟨NNT : 2023SORUS423⟩. ⟨tel-04391213⟩
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  Bartolomeo Civalleri, Roberto Dovesi, Pascal Pernot, Davide Presti, Andreas Savin. On the Use of Benchmarks for Multiple Properties. Computation, 2016, 4 (2), pp.20. ⟨10.3390/computation4020020⟩. ⟨hal-01415837⟩
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  Pascal Pernot, Bartolomeo Civalleri, Davide Presti, Andreas Savin. Prediction Uncertainty of Density Functional Approximations for Properties of Crystals with Cubic Symmetry. Journal of Physical Chemistry A, 2015, 119 (21), pp.5288-5304. ⟨10.1021/jp509980w⟩. ⟨hal-02010768⟩
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  Andreas Savin, Pascal Pernot. Acknowledging User Requirements for Accuracy in Computational Chemistry Benchmarks. Journal of Inorganic and General Chemistry / Zeitschrift für anorganische und allgemeine Chemie, 2019, 14 (8), pp.e0221707. ⟨10.1002/zaac.202000109⟩. ⟨hal-02563664⟩
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  Beata Taudul, Frederik Tielens, Monica Calatayud. Raman characterization of plastics: a DFT study of polystyrene. 2023. ⟨hal-04344053⟩
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