MC
Michele Ceriotti
physics.chem-phcond-mat.mtrl-sciphysics.comp-phcs.LGcond-mat.stat-mechstat.MLcond-mat.softMolecular Dynamics Simulationcond-mat.dis-nnAlgorithms
On Valency
published · living versionsW_f6nx38av·v1 · currentpublished
Combining Simulations and Spectroscopy to Design Protective Coatings on Sulfide Solid Electrolytes
with Zhiyi Wang, Leonie Braks, Ali Coskun, Hanna Türk +1
1 version
W_39jyz6pm·v1published
W_vmvfkhks·v2published
Preprints & journals
227 papers in the corpus · 2006–2026Using large language models to probe the limits of atom-centered structural descriptors.42861784 · Domina, Michelangelo, Ceriotti, Michele2026 · 0 citationsThe Journal of chemical physics. 2026;165(14)
Quantitative modeling of acid-base equilibria2610.13265v1 · Qianjun Xu, Simin Liu, Cesare Malosso et al.2026 · 0 citationsarXiv
Overcoming the limitations of body-ordered potentials for atomistic machine learning2610.10830v1 · Filippo Bigi, Michelangelo Domina, Maximilian Lloyd Ach et al.2026 · 0 citationsarXiv
A strategic roadmap for an atomistic machine-learning ecosystem2609.39090v2 · Jorg Behler, Michele Ceriotti, Cecilia Clementi et al.2026 · 0 citationsarXiv
Truncated automatic sparse differentiation for machine learning interatomic potentials2609.20510v1 · Marcel F. Langer, Adrian Hill, Michele Ceriotti2026 · 0 citationsarXiv
Machine-Learned NMR Shieldings in Molecular Solids with Built-In Hybrid-Functional Molecular Corrections2608.21313v1 · Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B. Holmes et al.2026 · 0 citationsarXivon Valency
Simultaneous learning of static and dynamic charges.42312373 · Stärk, Philipp, Stooß, Henrik, Langer, Marcel F et al.2026 · 1 citationPhysical chemistry chemical physics : PCCP. 2026;28(26):16249-16258
Transferable machine learning of excited-state dynamics with extremal pooling2606.16859v2 · Cesare Malosso, Wei Bin How, Gonzalo D'iaz Mir'on et al.2026 · 0 citationsarXiv
Learning the Action for Long-Time-Step Simulations of Molecular Dynamics.42360950 · Bigi, Filippo, Spies, Johannes, Ceriotti, Michele2026 · 1 citationPhysical review letters. 2026;136(23):237301
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models2606.07327v2 · Isabel Creed, Tim Rein, Ingvars Vitenburgs et al.2026 · 0 citationsarXiv
How to Train a Shallow Ensemble.42100964 · Schäfer, Moritz, Kellner, Matthias, Kästner, Johannes et al.2026 · 1 citationJournal of chemical theory and computation. 2026;22(10):4939-4950
Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments2604.24607v1 · Matthias Kellner, Teitur Hansen, Thomas Bligaard et al.2026 · 0 citationsarXiv
Path Integral Methods in Atomistic Modelling: An Introduction2603.28588v1 · Michele Ceriotti, David E. Manolopoulos, Thomas E. Markland et al.2026 · 0 citationsarXiv
How unconstrained machine-learning models learn physical symmetries2603.24638v1 · Michelangelo Domina, Joseph William Abbott, Paolo Pegolo et al.2026 · 1 citationarXiv
Quantum-corrected NMR crystallography at scale2603.06236v2 · Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B. Holmes et al.2026 · 0 citationsarXiv
A universal machine learning model for the electronic density of states.41815233 · How, Wei Bin, Febrer, Pol, Chong, Sanggyu et al.2026 · 6 citationsDigital discovery. 2026;5(4):1635-1649
High-quality, high-information datasets for universal atomistic machine learning2603.02089v3 · Cesare Malosso, Filippo Bigi, Paolo Pegolo et al.2026 · 3 citationsarXiv
Resolving the body-order paradox of machine learning interatomic potentials.41685856 · Chong, Sanggyu, Jiang, Tong, Domina, Michelangelo et al.2026 · 1 citationThe Journal of chemical physics. 2026;164(6)
Pushing the limits of unconstrained machine-learned interatomic potentials2601.16195v3 · Filippo Bigi, Paolo Pegolo, Arslan Mazitov et al.2026 · 5 citationsarXiv
scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science.38234865 · Goscinski, Alexander, Jorgensen, Christian A, Principe, Victor Paul et al.2025 · 21 citationsOpen research Europe. 2023;3:81
Molecular Surface Engineering of Sulfide Electrolytes with Enhanced Humidity Tolerance for Robust Lithium Metal All-Solid-State Batteries.41399952 · Fadillah, Laras, Braks, Leonie, Oh, Jihoon et al.2026 · 24 citationsAdvanced materials (Deerfield Beach, Fla.). 2026;38(9):e15013
Comparing the latent features of universal machine-learning interatomic potentials2512.05717v3 · Sofiia Chorna, Davide Tisi, Cesare Malosso et al.2025 · 2 citationsarXiv
PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.41309557 · Mazitov, Arslan, Bigi, Filippo, Kellner, Matthias et al.2026 · 59 citationsNature communications. 2025;16(1):10653
Massive Atomic Diversity: a compact universal dataset for atomistic machine learning.41271817 · Mazitov, Arslan, Chorna, Sofiia, Fraux, Guillaume et al.2026 · 18 citationsScientific data. 2025;12(1):1857
Mechanistic study of mixed lithium halides solid state electrolytes2511.15402v2 · Davide Tisi, Sergey Pozdnyakov, Michele Ceriotti2025 · 1 citationarXiv
A foundation model for atomistic materials chemistry.41230846 · Batatia, Ilyes, Benner, Philipp, Chiang, Yuan et al.2025 · 594 citationsThe Journal of chemical physics. 2025;163(18)
Representing spherical tensors with scalar-based machine-learning models.41128319 · Domina, M, Bigi, F, Pegolo, P et al.2025 · 1 citationThe Journal of chemical physics. 2025;163(16)
An Adapted Similarity Kernel and Generalized Convex Hull for Molecular Crystal Structure Prediction.41211598 · Martin, Jennie, Ceriotti, Michele, Day, Graeme M2025 · 2 citationsCrystal growth & design. 2025;25(21):9461-9474
An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction10.26434/chemrxiv-2025-2tt4c-v2 · Jennie Martin, Michele Ceriotti, Graeme Day2025 · 0 citationsChemRxiv
Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning2508.15704v3 · Filippo Bigi, Joseph W. Abbott, Philip Loche et al.2025 · 10 citationsarXiv
A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy.40825152 · Kellner, Matthias, Holmes, Jacob B, Rodriguez-Madrid, Ruben et al.2025 · 12 citationsThe journal of physical chemistry letters. 2025;16(34):8714-8722
Learning Long-Range Representations with Equivariant Messages2507.19382v2 · Egor Rumiantsev, Marcel F. Langer, Tulga-Erdene Sodjargal et al.2025 · 0 citationsarXiv
scicode-widgets: Bringing Computational Experiments to the Classroom with Jupyter Widgets2507.05734v1 · Alexander Goscinski, Taylor James Baird, Dou Du et al.2025 · 1 citationarXiv
Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework.40556546 · Suman, Divya, Nigam, Jigyasa, Saade, Sandra et al.2026 · 16 citationsJournal of chemical theory and computation. 2025;21(13):6505-6516
FlashMD: long-stride, universal prediction of molecular dynamics2505.19350v3 · Filippo Bigi, Sanggyu Chong, Agustinus Kristiadi et al.2025 · 2 citationsarXiv
Roadmap on Advancements of the FHI-aims Software Package2505.00125v3 · Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush et al.2025 · 11 citationsarXiv
Reconstructions and Dynamics of -Lithium Thiophosphate Surfaces2504.11553v1 · Hanna Turk, Davide Tisi, Michele Ceriotti2025 · 6 citationsPRX Energy 4(3), 033010 (2025)
Fast and flexible long-range models for atomistic machine learning.40197567 · Loche, Philip, Huguenin-Dumittan, Kevin K, Honarmand, Melika et al.2025 · 25 citationsThe Journal of chemical physics. 2025;162(14)
PLUMED Tutorials: A collaborative, community-driven learning ecosystem.40035582 · Tribello, Gareth A, Bonomi, Massimiliano, Bussi, Giovanni et al.2025 · 35 citationsThe Journal of chemical physics. 2025;162(9)
Prediction rigidities for data-driven chemistry.39319702 · Chong, Sanggyu, Bigi, Filippo, Grasselli, Federico et al.2025 · 11 citationsFaraday discussions. 2025;256(0):322-344
The dark side of the forces: assessing non-conservative force models for atomistic machine learning2412.11569v6 · Filippo Bigi, Marcel Langer, Michele Ceriotti2024 · 5 citationsarXiv
i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations.39140447 · Litman, Yair, Kapil, Venkat, Feldman, Yotam M Y et al.2024 · 72 citationsThe Journal of chemical physics. 2024;161(6)
Wigner kernels: Body-ordered equivariant machine learning without a basis.39056390 · Bigi, Filippo, Pozdnyakov, Sergey N, Ceriotti, Michele2024 · 19 citationsThe Journal of chemical physics. 2024;161(4)
Adaptive energy reference for machine-learning models of the electronic density of states2407.01068v4 · Wei Bin How, Sanggyu Chong, Federico Grasselli et al.2024 · 9 citationsPhys. Rev. Materials 9, 013802 (2025)
Probing the effects of broken symmetries in machine learning2406.17747v1 · Marcel F. Langer, Sergey N. Pozdnyakov, Michele Ceriotti2024 · 12 citationsMach. Learn.: Sci. Technol. 5 04LT01 (2024)
A prediction rigidity formalism for low-cost uncertainties in trained neural networks2403.02251v1 · Filippo Bigi, Sanggyu Chong, Michele Ceriotti et al.2024 · 13 citationsarXiv
Electronic Excited States from Physically Constrained Machine Learning.38559300 · Cignoni, Edoardo, Suman, Divya, Nigam, Jigyasa et al.2026 · 32 citationsACS central science. 2024;10(3):637-648
Uncertainty quantification by direct propagation of shallow ensembles2402.16621v2 · Matthias Kellner, Michele Ceriotti2024 · 36 citationsarXiv
Mechanism of Charge Transport in Lithium Thiophosphate.38370276 · Gigli, Lorenzo, Tisi, Davide, Grasselli, Federico et al.2026 · 27 citationsChemistry of materials : a publication of the American Chemical Society. 2024;36(3):1482-1496
Thermal conductivity of LiPS solid electrolytes with ab initio accuracy2401.12936v3 · Davide Tisi, Federico Grasselli, Lorenzo Gigli et al.2024 · 18 citationsPhys. Rev. Materials 8 (2024) 065403
Career total: at least 542 works. 227 are in this corpus.Showing the 50 most recent.
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