Zobrazeno 1 - 10
of 4 470
pro vyhledávání: '"Ceriotti, A."'
Autor:
Chong, Sanggyu, Bigi, Filippo, Grasselli, Federico, Loche, Philip, Kellner, Matthias, Ceriotti, Michele
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training effici
Externí odkaz:
http://arxiv.org/abs/2408.14311
Autor:
Borges, Beatriz, Foroutan, Negar, Bayazit, Deniz, Sotnikova, Anna, Montariol, Syrielle, Nazaretzky, Tanya, Banaei, Mohammadreza, Sakhaeirad, Alireza, Servant, Philippe, Neshaei, Seyed Parsa, Frej, Jibril, Romanou, Angelika, Weiss, Gail, Mamooler, Sepideh, Chen, Zeming, Fan, Simin, Gao, Silin, Ismayilzada, Mete, Paul, Debjit, Schöpfer, Alexandre, Janchevski, Andrej, Tiede, Anja, Linden, Clarence, Troiani, Emanuele, Salvi, Francesco, Behrens, Freya, Orsi, Giacomo, Piccioli, Giovanni, Sevel, Hadrien, Coulon, Louis, Pineros-Rodriguez, Manuela, Bonnassies, Marin, Hellich, Pierre, van Gerwen, Puck, Gambhir, Sankalp, Pirelli, Solal, Blanchard, Thomas, Callens, Timothée, Aoun, Toni Abi, Alonso, Yannick Calvino, Cho, Yuri, Chiappa, Alberto, Sclocchi, Antonio, Bruno, Étienne, Hofhammer, Florian, Pescia, Gabriel, Rizk, Geovani, Dadi, Leello, Stoffl, Lucas, Ribeiro, Manoel Horta, Bovel, Matthieu, Pan, Yueyang, Radenovic, Aleksandra, Alahi, Alexandre, Mathis, Alexander, Bitbol, Anne-Florence, Faltings, Boi, Hébert, Cécile, Tuia, Devis, Maréchal, François, Candea, George, Carleo, Giuseppe, Chappelier, Jean-Cédric, Flammarion, Nicolas, Fürbringer, Jean-Marie, Pellet, Jean-Philippe, Aberer, Karl, Zdeborová, Lenka, Salathé, Marcel, Jaggi, Martin, Rajman, Martin, Payer, Mathias, Wyart, Matthieu, Gastpar, Michael, Ceriotti, Michele, Svensson, Ola, Lévêque, Olivier, Ienne, Paolo, Guerraoui, Rachid, West, Robert, Kashyap, Sanidhya, Piazza, Valerio, Simanis, Viesturs, Kuncak, Viktor, Cevher, Volkan, Schwaller, Philippe, Friedli, Sacha, Jermann, Patrick, Kaser, Tanja, Bosselut, Antoine
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges for assessment and learning outcomes.
Externí odkaz:
http://arxiv.org/abs/2408.11841
Autor:
How, Wei Bin, Chong, Sanggyu, Grasselli, Federico, Huguenin-Dumittan, Kevin K., Ceriotti, Michele
The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and electronic properties and therefore guide computational material design. Given i
Externí odkaz:
http://arxiv.org/abs/2407.01068
Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the pr
Externí odkaz:
http://arxiv.org/abs/2406.17747
Autor:
Litman, Yair, Kapil, Venkat, Feldman, Yotam M. Y., Tisi, Davide, Begušić, Tomislav, Fidanyan, Karen, Fraux, Guillaume, Higer, Jacob, Kellner, Matthias, Li, Tao E., Pós, Eszter S., Stocco, Elia, Trenins, George, Hirshberg, Barak, Rossi, Mariana, Ceriotti, Michele
Atomic-scale simulations have progressed tremendously over the past decade, largely due to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to rea
Externí odkaz:
http://arxiv.org/abs/2405.15224
Iron (Fe) reduction is one of Earth's most ancient microbial metabolisms, but after atmosphere-ocean oxygenation, this anaerobic process was relegated to niche anoxic environments below the water and soil surface. However, new technologies to monitor
Externí odkaz:
http://arxiv.org/abs/2404.07137
Regression methods are fundamental for scientific and technological applications. However, fitted models can be highly unreliable outside of their training domain, and hence the quantification of their uncertainty is crucial in many of their applicat
Externí odkaz:
http://arxiv.org/abs/2403.02251
Autor:
Kellner, Matthias, Ceriotti, Michele
Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental
Externí odkaz:
http://arxiv.org/abs/2402.16621
Publikováno v:
Phys. Rev. Materials 8 (2024) 065403
The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (o
Externí odkaz:
http://arxiv.org/abs/2401.12936
Autor:
Cignoni, Edoardo, Suman, Divya, Nigam, Jigyasa, Cupellini, Lorenzo, Mennucci, Benedetta, Ceriotti, Michele
Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be combined exp
Externí odkaz:
http://arxiv.org/abs/2311.00844