Search Results - "Gabellini, Cristian"

Unveiling Liquid‐Phase Exfoliation of Graphite and Boron Nitride Using Fluorescent Dyes Through Combined Experiments and Simulations by He, Yilin, Qian, Xuliang, da Silva, Guilherme Carneiro Queiroz, Gabellini, Cristian, Lucherelli, Matteo Andrea, Biagiotti, Giacomo, Richichi, Barbara, Ménard‐Moyon, Cécilia, Gao, Huajian, Posocco, Paola, Bianco, Alberto

“…Liquid‐phase exfoliation (LPE) in aqueous solutions provides a simple, scalable, and green approach to produce 2D materials. By combining atomistic simulations…”
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The importance of molecular structure and functionalization of oxo-graphene sheets for gene silencing by Reina, Giacomo, Gabellini, Cristian, Maranska, Maria, Grote, Fabian, Chin, Shan Min, Jacquemin, Lucas, Berger, François, Posocco, Paola, Eigler, Siegfried, Bianco, Alberto

“…Gene therapy has recently reached the front line in the treatments of different diseases. Although graphene oxide has been widely used as a gene vector, its…”
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Spotting Local Environments in Self-Assembled Monolayer-Protected Gold Nanoparticles by Gabellini, Cristian, Şologan, Maria, Pellizzoni, Elena, Marson, Domenico, Daka, Mario, Franchi, Paola, Bignardi, Luca, Franchi, Stefano, Posel, Zbyšek, Baraldi, Alessandro, Pengo, Paolo, Lucarini, Marco, Pasquato, Lucia, Posocco, Paola

“…Organic–inorganic (O–I) nanomaterials are versatile platforms for an incredible high number of applications, ranging from heterogeneous catalysis to molecular…”
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Gotta be SAFE: a new framework for molecular design by Noutahi, Emmanuel, Gabellini, Cristian, Craig, Michael, Lim, Jonathan S. C, Tossou, Prudencio

“…Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of…”
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Gotta be SAFE: A New Framework for Molecular Design by Noutahi, Emmanuel, Gabellini, Cristian, Craig, Michael, Lim, Jonathan S. C, Tossou, Prudencio

“…Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of…”
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Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets by Beaini, Dominique, Huang, Shenyang, Cunha, Joao Alex, Li, Zhiyi, Moisescu-Pareja, Gabriela, Dymov, Oleksandr, Maddrell-Mander, Samuel, McLean, Callum, Wenkel, Frederik, Müller, Luis, Mohamud, Jama Hussein, Parviz, Ali, Craig, Michael, Koziarski, Michał, Lu, Jiarui, Zhu, Zhaocheng, Gabellini, Cristian, Klaser, Kerstin, Dean, Josef, Wognum, Cas, Sypetkowski, Maciej, Rabusseau, Guillaume, Rabbany, Reihaneh, Tang, Jian, Morris, Christopher, Koutis, Ioannis, Ravanelli, Mirco, Wolf, Guy, Tossou, Prudencio, Mary, Hadrien, Bois, Therence, Fitzgibbon, Andrew, Banaszewski, Błażej, Martin, Chad, Masters, Dominic

“…Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are…”
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