Zobrazeno 1 - 10
of 1 367
pro vyhledávání: '"A. Vegge"'
MolMiner: Transformer architecture for fragment-based autoregressive generation of molecular stories
Deep generative models for molecular discovery have become a very popular choice in new high-throughput screening paradigms. These models have been developed inheriting from the advances in natural language processing and computer vision, achieving e
Externí odkaz:
http://arxiv.org/abs/2411.06608
Autor:
Batatia, Ilyes, Benner, Philipp, Chiang, Yuan, Elena, Alin M., Kovács, Dávid P., Riebesell, Janosh, Advincula, Xavier R., Asta, Mark, Avaylon, Matthew, Baldwin, William J., Berger, Fabian, Bernstein, Noam, Bhowmik, Arghya, Blau, Samuel M., Cărare, Vlad, Darby, James P., De, Sandip, Della Pia, Flaviano, Deringer, Volker L., Elijošius, Rokas, El-Machachi, Zakariya, Falcioni, Fabio, Fako, Edvin, Ferrari, Andrea C., Genreith-Schriever, Annalena, George, Janine, Goodall, Rhys E. A., Grey, Clare P., Grigorev, Petr, Han, Shuang, Handley, Will, Heenen, Hendrik H., Hermansson, Kersti, Holm, Christian, Jaafar, Jad, Hofmann, Stephan, Jakob, Konstantin S., Jung, Hyunwook, Kapil, Venkat, Kaplan, Aaron D., Karimitari, Nima, Kermode, James R., Kroupa, Namu, Kullgren, Jolla, Kuner, Matthew C., Kuryla, Domantas, Liepuoniute, Guoda, Margraf, Johannes T., Magdău, Ioan-Bogdan, Michaelides, Angelos, Moore, J. Harry, Naik, Aakash A., Niblett, Samuel P., Norwood, Sam Walton, O'Neill, Niamh, Ortner, Christoph, Persson, Kristin A., Reuter, Karsten, Rosen, Andrew S., Schaaf, Lars L., Schran, Christoph, Shi, Benjamin X., Sivonxay, Eric, Stenczel, Tamás K., Svahn, Viktor, Sutton, Christopher, Swinburne, Thomas D., Tilly, Jules, van der Oord, Cas, Varga-Umbrich, Eszter, Vegge, Tejs, Vondrák, Martin, Wang, Yangshuai, Witt, William C., Zills, Fabian, Csányi, Gábor
Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and hum
Externí odkaz:
http://arxiv.org/abs/2401.00096
Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting and applying interatomic forces. In these settings, it is crucial to det
Externí odkaz:
http://arxiv.org/abs/2305.16325
CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-11 (2024)
Abstract Ion transport in non-aqueous electrolytes is crucial for high performance lithium-ion battery (LIB) development. The design of superior electrolytes requires extensive experimentation across the compositional space. To support data driven ac
Externí odkaz:
https://doaj.org/article/55c4c19d2bdb43fc9066cbe7a9674db5
Machine Learning (ML) models have, in contrast to their usefulness in molecular dynamics studies, had limited success as surrogate potentials for reaction barrier search. It is due to the scarcity of training data in relevant transition state regions
Externí odkaz:
http://arxiv.org/abs/2207.12858
Quantum mechanical methods like Density Functional Theory (DFT) are used with great success alongside efficient search algorithms for studying kinetics of reactive systems. However, DFT is prohibitively expensive for large scale exploration. Machine
Externí odkaz:
http://arxiv.org/abs/2207.09971
Utilizing active learning to accelerate segmentation of microstructures with tiny annotation budgets
Autor:
Rieger, Laura Hannemose, Cadiou, François, Jacquet, Quentin, Vanpeene, Victor, Villanova, Julie, Lyonnard, Sandrine, Vegge, Tejs, Bhowmik, Arghya
Publikováno v:
In Energy Storage Materials November 2024 73
Autor:
Wilson, Max, Moroni, Saverio, Holzmann, Markus, Gao, Nicholas, Wudarski, Filip, Vegge, Tejs, Bhowmik, Arghya
Publikováno v:
Phys. Rev. B 107, 235139 (2023)
We design a neural network Ansatz for variationally finding the ground-state wave function of the Homogeneous Electron Gas, a fundamental model in the physics of extended systems of interacting fermions. We study the spin-polarised and paramagnetic p
Externí odkaz:
http://arxiv.org/abs/2202.04622
Autor:
Mikkelsen, August E. G., Kristoffersen, Henrik H., Schiøtz, Jakob, Vegge, Tejs, Hansen, Heine A., Jacobsen, Karsten W.
Publikováno v:
Phys. Chem. Chem. Phys., 2022,24, 9885-9890
The interactions between water and hydroxyl species on Pt(111) surfaces have been intensely investigated due to their importance to fuel cell electrocatalysis. Here we present a room temperature molecular dynamics study of their structure and energet
Externí odkaz:
http://arxiv.org/abs/2112.08803
Autor:
Busk, Jonas, Jørgensen, Peter Bjørn, Bhowmik, Arghya, Schmidt, Mikkel N., Winther, Ole, Vegge, Tejs
Data-driven methods based on machine learning have the potential to accelerate computational analysis of atomic structures. In this context, reliable uncertainty estimates are important for assessing confidence in predictions and enabling decision ma
Externí odkaz:
http://arxiv.org/abs/2107.06068