Publications

Makoto Takamoto, Henrik Christiansen, Francesco Alesiani (2024). ACCELERATING PARTICLE SIMULATIONS USING MACHINE LEARNING AND MOLECULAR DYNAMIC SIMULATIONS.
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Takashi Maruyama, Francesco Alesiani (2024). Hierarchy-based Clifford Group Equivariant Message Passing Neural Networks. ICLR 2024 Workshop on AI4DifferentialEquations In Science.
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Anil Goyal, Ammar Shaker, Francesco Alesiani (2024). Machine learning knowledge management based on lifelong boosting in presence of less data.
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Francesco Alesiani, Markus Zopf (2024). Information system for generation of complex molecule by using graph representing molecule.
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Francesco Alesiani, Markus Zopf, Cristobal Felipe Corvalan Morbiducci (2024). Learning logical rules over graph structured data using message passing.
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Viktor Zaverkin, David Holzmüller, Henrik Christiansen, Federico Errica, Francesco Alesiani, Makoto Takamoto, Mathias Niepert, Johannes Kästner (2024). Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials. npj Computational Materials.
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Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert (2024). Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. arXiv preprint arXiv:2405.14253.
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Markus Zopf, Francesco Alesiani (2024). Graph Reasoning Networks. arXiv preprint arXiv:2407.05816.
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Francesco Alesiani, Markus Zopf (2023). Machine learning for optimized learning of human-understandable logical rules from medical or other data.
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Francesco Alesiani, Markus Zopf (2023). Systems and methods for learning human-understandable logical rules from data.
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