| Understanding morphology-mobility dependence in PEDOT: Tos N Rolland, JF Franco-Gonzalez, R Volpi, M Linares, IV Zozoulenko Physical Review Materials 2 (4), 045605, 2018 | 64 | 2018 |
| Parameter estimation for the cosmic microwave background with Bayesian neural networks HJ Hortúa, R Volpi, D Marinelli, L Malagò Physical Review D 102 (10), 103509, 2020 | 27 | 2020 |
| Effect of Polarization on the Mobility of C60: A Kinetic Monte Carlo Study R Volpi, S Kottravel, MS Nørby, S Stafstrom, M Linares Journal of chemical theory and computation 12 (2), 812-824, 2016 | 26 | 2016 |
| Theoretical Study of the Charge-Transfer State Separation within Marcus Theory: The C60-Anthracene Case Study R Volpi, R Nassau, MS Nørby, M Linares ACS Applied Materials & Interfaces 8 (37), 24722-24736, 2016 | 22 | 2016 |
| Constraining the reionization history using Bayesian normalizing flows HJ Hortúa, L Malagò, R Volpi Machine Learning: Science and Technology 1 (3), 035014, 2020 | 21 | 2020 |
| Transition fields in organic materials: From percolation to inverted Marcus regime. A consistent Monte Carlo simulation in disordered PPV R Volpi, S Stafström, M Linares The Journal of Chemical Physics 142 (9), 2015 | 16 | 2015 |
| Modelling charge transport of discotic liquid-crystalline triindoles: the role of peripheral substitution R Volpi, ACS Camilo, DA da Silva Filho, JTL Navarrete, B Gómez-Lor, ... Physical Chemistry Chemical Physics 19 (35), 24202-24208, 2017 | 13 | 2017 |
| Mobility field and mobility temperature dependence in PC61BM: A kinetic Monte-Carlo study L Sousa, R Volpi, DA da Silva Filho, M Linares Chemical Physics Letters 689, 74-81, 2017 | 9 | 2017 |
| Organic solar cells R Volpi, M Linares Specialist Periodic Reports-Chemical Modelling, RSC 13, 1-26, 2016 | 7 | 2016 |
| Natural wake-sleep algorithm C Várady, N Ay, R Volpi, L Malagò | 6 | 2020 |
| Parameters estimation from the 21 cm signal using variational inference HJ Hortúa, R Volpi, L Malagò arXiv preprint arXiv:2005.02299, 2020 | 5 | 2020 |
| Accelerating mcmc algorithms through bayesian deep networks HJ Hortua, R Volpi, D Marinelli, L Malago arXiv preprint arXiv:2011.14276, 2020 | 4 | 2020 |
| Evaluating natural alpha embeddings on intrinsic and extrinsic tasks R Volpi, L Malagò Proceedings of the 5th Workshop on Representation Learning for NLP, 61-71, 2020 | 4 | 2020 |
| Evaluating the robustness of defense mechanisms based on autoencoder reconstructions against carlini-wagner adversarial attacks P Hlihor, R Volpi, L Malagò Proceedings of the Northern Lights Deep Learning Workshop 1, 6-6, 2020 | 4 | 2020 |
| Study of the cold charge transfer state separation at the TQ1/PC71BM interface R Volpi, M Linares Journal of computational chemistry 38 (14), 1039-1048, 2017 | 4 | 2017 |
| Natural alpha embeddings R Volpi, L Malagò Information Geometry 4 (1), 3-29, 2021 | 3 | 2021 |
| Reliable uncertainties for Bayesian neural networks using alpha-divergences HJ Hortua, L Malago, R Volpi arXiv preprint arXiv:2008.06729, 2020 | 3 | 2020 |
| Visual analysis of stochastic trajectory ensembles in organic solar cell design S Kottravel, R Volpi, M Linares, T Ropinski, I Hotz Informatics 4 (3), 25, 2017 | 3 | 2017 |
| Modelling Charge Transport for Organic Solar Cells within Marcus Theory R Volpi Linköping University Electronic Press, 2016 | 3 | 2016 |
| Changing the Geometry of Representations: α-Embeddings for NLP Tasks R Volpi, U Thakur, L Malagò Entropy 23 (3), 287, 2021 | 2 | 2021 |
