Pillar 2

Published datasets resulting from Pillar 2 "Machine Learning Platform for Molecules and Materials".

10.5281/zenodo.12804608 — rlaplaza/spock: Release for the manuscript, by R. Laplaza, R. Graux
Related MARVEL publication:
- M. Suvarna, R. Laplaza, R. Graux, N. López, C. Corminboeuf, K. Jorner, J. Pérez-Ramírez, SPOCK tool for constructing empirical volcano diagrams from catalytic data, ACS Catalysis 15, 7296–7307 (2025). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.24435/materialscloud:b7-xq — FlashMD: long-stride, universal prediction of molecular dynamics, by F. Bigi, S. Chong, A. Kristiadi, M. Ceriotti
Related MARVEL publication:
- F. Bigi, S. Chong, A. Kristiadi, M. Ceriotti, FlashMD: long-stride, universal prediction of molecular dynamics, arXiv:2505.19350 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.5281/zenodo.15783260 — scicode-widgets: Supplementary code for figures, by A. Goscinski, T. J. Baird, D. Du, J. Prado, D. Suman, T. Sodjargal, S. Bonella, G. Pizzi, M. Ceriotti
Related MARVEL publication:
- A. Goscinski, T. J. Baird, D. Du, J. Prado, D. Suman, T. Sodjargal, S. Bonella, G. Pizzi, M. Ceriotti, scicode-widgets: Bringing Computational Experiments to the Classroom with Jupyter Widgets, arXiv:2507.05734 (2025). [Open Access URL]
  Group(s): Bonella, Ceriotti, Pizzi / Project(s): P2, P3
10.24435/materialscloud:r5-vh — Comparing the latent features of universal machine-learning interatomic potentials, by S. Chorna, D. Tisi, C. Malosso, W. B. How, M. Ceriotti, S. Chong
Related MARVEL publication:
- S. Chorna, D. Tisi, C. Malosso, W. B. How, M. Ceriotti, S. Chong, Comparing the latent features of universal machine-learning interatomic potentials, arXiv:2512.05717 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:mg-8f — Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework, by D. Suman, J. Nigam, S. Saade, P. Pegolo, H. Tuerk, X. Zhang, G. Kin-Lic Chan, M. Ceriotti
Related MARVEL publication:
- D. Suman, J. Nigam, S. Saade, P. Pegolo, H. Türk, X. Zhang, G. K. Chan, M. Ceriotti, Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework, Journal of Chemical Theory and Computation 21, 6505–6516 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:bd-fp — The three-dimensional atomic-level structure of an amorphous glucagon-like peptide-1 receptor agonist, by D. Torodii, M. Cordova, J. Holmes, P. Moutzouri, T. Casalini, S. Nilsson Lill, A. Pinon, C. Knee, A. Svensk Ankarberg, O. Dwichandra Putra, S. Schantz, L. Emsley
Related MARVEL publication:
- D. Torodii, M. Cordova, J. B. Holmes, P. Moutzouri, T. Casalini, S. O. Nilsson Lill, A. C. Pinon, C. S. Knee, A. Svensk Ankarberg, O. D. Putra, S. Schantz, L. Emsley, Three-Dimensional Atomic-Level Structure of an Amorphous Glucagon-Like Peptide-1 Receptor Agonist, Journal of the American Chemical Society 147, 17077–17087 (2025).[Open Access URL]
  Group(s): Emsley / Project(s): P2
10.24435/materialscloud:fj-a1 — Integer linear programming for unsupervised training set selection in molecular machine learning, by M. Haeberle, P. van Gerwen, R. Laplaza, K. R. Briling, J. Weinreich, F. Eisenbrand, C. Corminboeuf
Related MARVEL publication:
- M. Haeberle, P. van Gerwen, R. Laplaza, K. R. Briling, J. Weinreich, F. Eisenbrand, C. Corminboeuf, Integer linear programming for unsupervised training set selection in molecular machine learning, Machine Learning: Science and Technology 6, 025030 (2025). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.24435/materialscloud:d0-ck — Determination of key functional structures of an amorphous VHL-based SMARCA2 PROTAC, by D. Torodii, J. Holmes, M. Cordova, P. Moutzouri, L. van Beek, F. Edfeldt, E. Malmerberg, J. Johansson, A. Milbradt, S. Nilsson Lil, B. Malfait, S. Schantz, L. Emsley
Related MARVEL publication:
- D. Torodii, J. B. Holmes, M. Cordova, P. Moutzouri, L. V. Beek, F. Edfeldt, E. Malmerberg, S. D. Friis, J. R. Johansson, A. G. Milbradt, S. O. N. Lill, B. Malfait, S. Schantz, L. Emsley, Determination of key functional structures of an amorphous VHL-based SMARCA2 PROTAC, Nature Communications 16, 9694 (2025). [Open Access URL]
  Group(s): Emsley / Project(s): P2
10.24435/materialscloud:fe-1p — PET-MAD, a lightweight universal interatomic potential for advanced materials modeling, by M. Arslan, B. Filippo, K. Matthias, P. Paolo, T. Davide, F. Guillaume, P. Sergey, C. Michele
Related MARVEL publication:
- A. Mazitov, F. Bigi, M. Kellner, P. Pegolo, D. Tisi, G. Fraux, S. Pozdnyakov, P. Loche, M. Ceriotti, PET-MAD as a lightweight universal interatomic potential for advanced materials modeling, Nature Communications 16, 10653 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:9m-4v — Reconstructions and dynamics of β-lithium thiophosphate surfaces, by H. Türk, D. Tisi, M. Ceriotti
Related MARVEL publication:
- H. Türk, D. Tisi, M. Ceriotti, Reconstructions and Dynamics of β-Lithium Thiophosphate Surfaces, PRX Energy 4, 033010 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:ab-y2 — Massive Atomic Diversity: a compact universal dataset for atomistic machine learning, by A. Mazitov, S. Chorna, G. Fraux, M. Bercx, G. Pizzi, S. De, M. Ceriotti
Related MARVEL publication:
- A. Mazitov, S. Chorna, G. Fraux, M. Bercx, G. Pizzi, S. De, M. Ceriotti, Massive Atomic Diversity: a compact universal dataset for atomistic machine learning, Scientific Data 12, 1857 (2025). [Open Access URL]
  Group(s): Ceriotti, Pizzi / Project(s): P2, P3
10.24435/materialscloud:zq-a6 — Representing spherical tensors with scalar-based machine-learning models, by M. Domina, F. Bigi, P. Pegolo, M. Ceriotti
Related MARVEL publication:
- M. Domina, F. Bigi, P. Pegolo, M. Ceriotti, Representing spherical tensors with scalar-based machine-learning models, The Journal of Chemical Physics 163, 164114 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:23-99 — Physics-inspired equivariant descriptors of non-bonded interactions, by K. K. Huguenin-Dumittan, P. Loche, H. Ni, M. Ceriotti
Related MARVEL publication:
- P. Loche, K. K. Huguenin-Dumittan, M. Honarmand, Q. Xu, E. Rumiantsev, W. B. How, M. F. Langer, M. Ceriotti, Fast and flexible long-range models for atomistic machine learning, The Journal of Chemical Physics 162, 142501 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.5281/zenodo.14792591 — PicoCentauri/ewaldAD-code: Paper release version, by P. Loche, E. Rumiantsev, Marcel
Related MARVEL publication:
- P. Loche, K. K. Huguenin-Dumittan, M. Honarmand, Q. Xu, E. Rumiantsev, W. B. How, M. F. Langer, M. Ceriotti, Fast and flexible long-range models for atomistic machine learning, The Journal of Chemical Physics 162, 142501 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.5281/zenodo.11175491 — A Force-Matching Based Polarizable Force Field for Perovskite and Non-Perovskite Phases of CsPbI3, by C. Vona, A. BOZIKI, U. Rothlisberger, M. Dankl, M. Bircher
Related MARVEL publication:
- C. Vona, M. Dankl, A. Boziki, M. P. Bircher, U. Rothlisberger, Force-Matching-Based Approach for the Generation of Polarizable and Nonpolarizable Force Fields Applied to CsPbI₃, The Journal of Physical Chemistry C 129, 3040–3053 (2025). [Open Access URL]
  Group(s): Roethlisberger / Project(s): VP2
10.24435/materialscloud:kt-kd — A deep learning model for chemical shieldings in molecular organic solids including anisotropy, by M. Kellner, J. B. Holmes, R. Rodriguez-Madrid, F. Viscosi, Y. Zhang, L. Emsley, M. Ceriotti
Related MARVEL publication:
- M. Kellner, J. B. Holmes, R. Rodriguez-Madrid, F. Viscosi, Y. Zhang, L. Emsley, M. Ceriotti, A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy, The Journal of Physical Chemistry Letters 16, 8714–8722 (2025). [Open Access URL]
  Group(s): Ceriotti, Emsley / Project(s): P2
10.5281/zenodo.14778890 — The dark side of the forces: assessing non-conservative force models for atomistic machine learning, by A. Anonymous
Related MARVEL publication:
- F. Bigi, M. F. Langer, M. Ceriotti, The dark side of the forces: assessing non-conservative force models for atomistic machine learning, 267, 4384–4414 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.5281/zenodo.18344004 — SPOC-group/ConformalAmp.jl: Uai-version, by lucas-andry-c
Related MARVEL publication:
- L. Clarté, L. Zdeborová, Building conformal prediction intervals with approximate message passing, 798 (2025).[Open Access URL]
  Group(s): Zdeborova / Project(s): P2

github.com/acesuit/mace-mp — MACE-FOUNDATION models, by I. Batatia, J. Riebesell, and Y. Chiang
Related MARVEL publication:
- I. Batatia, P. Benner, Y. Chiang, A. M. Elena, D. P. Kovács, J. Riebesell, X. R. Advincula, M. Asta, M. Avaylon, W. J. Baldwin, F. Berger, N. Bernstein, A. Bhowmik, F. Bigi, S. M. Blau, V. Cărare, M. Ceriotti, S. Chong, J. P. Darby, S. De, F. Della Pia, V. L. Deringer, R. Elijošius, Z. El-Machachi, E. Fako, F. Falcioni, A. C. Ferrari, J. L. A. Gardner, M. J. Gawkowski, A. Genreith-Schriever, J. George, R. E. A. Goodall, J. Grandel, C. P. Grey, P. Grigorev, S. Han, W. Handley, H. H. Heenen, K. Hermansson, C. H. Ho, S. Hofmann, C. Holm, J. Jaafar, K. S. Jakob, H. Jung, V. Kapil, A. D. Kaplan, N. Karimitari, J. R. Kermode, P. Kourtis, N. Kroupa, J. Kullgren, M. C. Kuner, D. Kuryla, G. Liepuoniute, C. Lin, J. T. Margraf, I. Magdău, A. Michaelides, J. H. Moore, A. A. Naik, S. P. Niblett, S. W. Norwood, N. O'Neill, C. Ortner, K. A. Persson, K. Reuter, A. S. Rosen, L. A. M. Rosset, L. L. Schaaf, C. Schran, B. X. Shi, E. Sivonxay, T. K. Stenczel, C. Sutton, V. Svahn, T. D. Swinburne, J. Tilly, C. van der Oord, S. Vargas, E. Varga-Umbrich, T. Vegge, M. Vondrák, Y. Wang, W. C. Witt, T. Wolf, F. Zills, G. Csányi, A foundation model for atomistic materials chemistry, The Journal of Chemical Physics 163, 184110 (2025).[Open Access URL]
  Group(s): Ceriotti / Project(s): P2

10.24435/materialscloud:6x-gs — Prediction rigidities for data-driven chemistry, by S. Chong, F. Bigi, F. Grasselli, P. Loche, M. Kellner, M. Ceriotti
Related MARVEL publication:
- S. Chong, F. Bigi, F. Grasselli, P. Loche, M. Kellner, M. Ceriotti, Prediction rigidities for data-driven chemistry, Faraday Discussions 256, 322 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:9r-b9 — Atomic-level structure of the amorphous drug Atuliflapon via NMR crystallography, by J. Holmes, D. Torodii, M. Balodis, M. Cordova, A. Hofstetter, F. Paruzzo, S. Nilsson Lill, E. Eriksson, P. Berruyer, B. Simões de Almeida, M. Quayle, S. Norberg, A. Svensk Ankarberg, S. Schantz, L. Emsley
Related MARVEL publication:
- J. B. Holmes, D. Torodii, M. Balodis, M. Cordova, A. Hofstetter, F. Paruzzo, S. O. Nilsson Lill, E. Eriksson, P. Berruyer, B. Simões de Almeida, M. Quayle, S. Norberg, A. S. Ankarberg, S. Schantz, L. Emsley, Atomic-level structure of the amorphous drug atuliflapon via NMR crystallography, Faraday Discussions 255, 342 (2025). [Open Access URL]
  Group(s): Emsley / Project(s): P2
10.24435/materialscloud:qk-x9 — Crystal structure validation of verinurad via proton-detected ultra-fast MAS NMR and machine learning, by D. Torodii, J. Holmes, P. Moutzouri, S. Nilsson Lill, M. Cordova, A. Pinon, K. Grohe, S. Wegner, O. D. Putra, S. Norberg, A. Welinder, S. Schantz, L. Emsley
Related MARVEL publication:
- D. Torodii, J. B. Holmes, P. Moutzouri, S. O. Nilsson Lill, M. Cordova, A. C. Pinon, K. Grohe, S. Wegner, O. D. Putra, S. Norberg, A. Welinder, S. Schantz, L. Emsley, Crystal structure validation of verinurad via proton-detected ultra-fast MAS NMR and machine learning, Faraday Discussions 255, 143 (2025). [Open Access URL]
  Group(s): Emsley / Project(s): P2
10.24435/materialscloud:s9-c5 — Adaptive energy reference for machine-learning models of the electronic density of states, by W. B. How, S. Chong, F. Grasselli, K. K. Huguenin-Dumittan, M. Ceriotti
Related MARVEL publication:
- W. B. How, S. Chong, F. Grasselli, K. K. Huguenin-Dumittan, M. Ceriotti, Adaptive energy reference for machine-learning models of the electronic density of states, Physical Review Materials 9, 013802 (2025). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:5r-rf — A prediction rigidity formalism for low-cost uncertainties in trained neural networks, by F. Bigi, S. Chong, M. Ceriotti, F. Grasselli
Related MARVEL publication:
- F. Bigi, S. Chong, M. Ceriotti, F. Grasselli, A prediction rigidity formalism for low-cost uncertainties in trained neural networks, Machine Learning: Science and Technology 5, 045018 (2024). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:kz-3b — Probing the effects of broken symmetries in machine learning, by M. F. Langer, S. N. Pozdnyakov, M. Ceriotti
Related MARVEL publication:
- M. F. Langer, S. N. Pozdnyakov, M. Ceriotti, Probing the effects of broken symmetries in machine learning, Machine Learning: Science and Technology 5, 04LT01 (2024). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.5281/zenodo.7952084 — Wigner Kernels, by Anonymous
Related MARVEL publication:
- F. Bigi, S. N. Pozdnyakov, M. Ceriotti, Wigner kernels: Body-ordered equivariant machine learning without a basis, The Journal of Chemical Physics 161, 044116 (2024). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:az-j9 — Analysis of bootstrap and subsampling in high-dimensional regularized regression (code), by L. Clarte, A. Vandenbroucque, G. Dalle, B. Loureiro, F. Krzakala, L. Zdeborova
Related MARVEL publication:
- L. Clarté, A. Vandenbroucque, G. Dalle, B. Loureiro, F. Krzakala, L. Zdeborová, Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression, 244, 787–819 (2024). [Open Access URL]
  Group(s): Zdeborova / Project(s): P2
10.24435/materialscloud:xd-ef — 3DReact: geometric deep learning for chemical reactions, by P. van Gerwen, K. Briling, C. Bunne, V. R. Somnath, R. Laplaza, A. Krause, C. Corminboeuf
Related MARVEL publication:
- P. van Gerwen, K. R. Briling, C. Bunne, V. R. Somnath, R. Laplaza, A. Krause, C. Corminboeuf, 3DReact: Geometric deep learning for chemical reactions, Journal of Chemical Information and Modeling 64, 5771–5785 (2024). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.24435/materialscloud:xw-g5 — Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling, by L. Gigli, A. Goscinski, M. Ceriotti, G. A. Tribello
Related MARVEL publication:
- L. Gigli, A. Goscinski, M. Ceriotti, G. A. Tribello, Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling, Physical Review B 110, 024101 (2024). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:nv-1g — Thermal transport of Li₃PS₄ solid electrolytes with ab initio accuracy, by D. Tisi, F. Grasselli, L. Gigli, M. Ceriotti
Related MARVEL publication:
- D. Tisi, F. Grasselli, L. Gigli, M. Ceriotti, Thermal conductivity of Li₃PS₄ solid electrolytes with ab initio accuracy, Physical Review Materials 8, 065403 (2024). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:fm-za — The rule of four: anomalous stoichiometries of inorganic compounds, by E. Gazzarrini, R. K. Cersonsky, M. Bercx, C. S. Adorf, N. Marzari
Related MARVEL publication:
- E. Gazzarrini, R. K. Cersonsky, M. Bercx, C. S. Adorf, N. Marzari, The rule of four: anomalous distributions in the stoichiometries of inorganic compounds, npj Computational Materials 10, 73 (2024). [Open Access URL]
  Group(s): Marzari / Project(s): P2
10.24435/materialscloud:zh-q9 — Surface segregation in high-entropy alloys from alchemical machine learning: dataset HEA25S, by A. Mazitov, M. A. Springer, N. Lopanitsyna, G. Fraux, S. De, M. Ceriotti
Related MARVEL publication:
- A. Mazitov, M. A. Springer, N. Lopanitsyna, G. Fraux, S. De, M. Ceriotti, Surface segregation in high-entropy alloys from alchemical machine learning, JPhys Materials 7, 025007 (2024). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.5281/zenodo.8003294 — Bispectrum degenerate B8 data, by J. Nigam, M. Ceriotti
Related MARVEL publication:
- J. Nigam, S. N. Pozdnyakov, K. K. Huguenin-Dumittan, M. Ceriotti, Completeness of atomic structure representations, APL Machine Learning 2, 016110 (2024). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:j2-58 — Electronic excited states from physically-constrained machine learning, by E. Cignoni, D. Suman, J. Nigam, L. Cupellini, B. Mennucci, M. Ceriotti
Related MARVEL publication:
- E. Cignoni, D. Suman, J. Nigam, L. Cupellini, B. Mennucci, M. Ceriotti, Electronic Excited States from Physically Constrained Machine Learning, ACS Central Science 10, 637–648 (2024). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:pz-2y — From organic fragments to photoswitchable catalysts: the off-on structural repository for transferable kernel-based potentials, by F. Célerse, M. D. Wodrich, S. Vela, S. Gallarati, R. Fabregat, V. Juraskova, C. Corminboeuf
Related MARVEL publication:
- F. Célerse, M. D. Wodrich, S. Vela, S. Gallarati, R. Fabregat, V. Juraskova, C. Corminboeuf, From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials, Journal of Chemical Information and Modeling 64, 1201–1212 (2024). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.24435/materialscloud:qy-gv — Mechanism of charge transport in lithium thiophosphate, by L. Gigli, D. Tisi, F. Grasselli, M. Ceriotti
Related MARVEL publication:
- L. Gigli, D. Tisi, F. Grasselli, M. Ceriotti, Mechanism of Charge Transport in Lithium Thiophosphate, Chemistry of Materials 36, 1482–1496 (2024). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.5281/zenodo.8263407 — varbench/varbench: v1.1.0, by J. M. Silvester, D. Wu, ltocchio, N. Astrakhantsev, G. Carleo, Y. Yang, F. Ferrari, R. Pohle, ssorella, M. Hibat-Allah, jrm874, yusukenomura, F. Vicentini, M. Schmid, xiaodongcao, I. Romero, A. Wietek, J. Nys, Q. Yang
Related MARVEL publication:
- D. Wu, R. Rossi, F. Vicentini, N. Astrakhantsev, F. Becca, X. Cao, J. Carrasquilla, F. Ferrari, A. Georges, M. Hibat-Allah, M. Imada, A. M. Läuchli, G. Mazzola, A. Mezzacapo, A. Millis, J. R. Moreno, T. Neupert, Y. Nomura, J. Nys, O. Parcollet, R. Pohle, I. Romero, M. Schmid, J. M. Silvester, S. Sorella, L. F. Tocchio, L. Wang, S. R. White, A. Wietek, Q. Yang, Y. Yang, S. Zhang, G. Carleo, Variational benchmarks for quantum many-body problems, Science 386, 296–301 (2024). [Open Access URL]
  Group(s): Carleo / Project(s): P2, QS
10.6084/m9.figshare.27102529 — Data accompanying "Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation", by J. Nys
Related MARVEL publication:
- J. Nys, G. Pescia, A. Sinibaldi, G. Carleo, Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation, Nature Communications 15, 9404 (2024). [Open Access URL]
  Group(s): Carleo / Project(s): P2
10.24435/materialscloud:1g-w5 — SPAᴴM(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations, by K. R. Briling, Y. Calvino Alonso, A. Fabrizio, C. Corminboeuf
Related MARVEL publication:
- K. R. Briling, Y. Calvino Alonso, A. Fabrizio, C. Corminboeuf, SPAHM(a,b): Encoding the density information from guess Hamiltonian in quantum machine learning representations, Journal of Chemical Theory and Computation20, 1108–1117 (2024). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.24435/materialscloud:jx-a5 — Automated prediction of ground state spin for transition metal complexes, by Y. Cho, R. Laplaza, S. Vela, C. Corminboeuf
Related MARVEL publication:
- Y. Cho, R. Laplaza, S. Vela, C. Corminboeuf, Automated prediction of ground state spin for transition metal complexes, Digital Discovery 3, 1638–1647 (2024). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.24435/materialscloud:vm-5n — Spectral operator representations, by A. Zadoks, A. Marrazzo, N. Marzari
Related MARVEL publication:
- A. Zadoks, A. Marrazzo, N. Marzari, Spectral Operator Representations, npj Computational Materials 10, 278 (2024). [Open Access URL]
  Group(s): Marzari / Project(s): P2
10.24435/materialscloud:w7-k1 — Teaching oxidation states to neural networks, by C. Malica, N. Marzari
Related MARVEL publication:
- C. Malica, N. Marzari, Teaching oxidation states to neural networks, arXiv:2412.01652 (2024). [Open Access URL]
  Group(s): Marzari / Project(s): P2
10.24435/materialscloud:xd-10 — Benchmarking machine-readable vectors of chemical reactions on computed activation barriers, by P. van Gerwen, K. R. Briling, Y. Calvino Alonso, M. Franke, C. Corminboeuf
Related MARVEL publication:
- P. van Gerwen, K. R. Briling, Y. Calvino Alonso, M. Franke, C. Corminboeuf, Benchmarking machine-readable vectors of chemical reactions on computed activation barriers, Digital Discovery 3, 932–943 (2024). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.5281/zenodo.7967079 — Point Edge Transformer, by Anonymous
Related MARVEL publication:
- S. Pozdnyakov, M. Ceriotti, Smooth, exact rotational symmetrization for deep learning on point clouds, 36, 79469–79501 (2023). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2

github.com/i-pi/ipiv3_data — i-PI v3.0: source data and figures, by Yair Litman, Venkat Kapil, Yotam M. Y. Feldman, Davide Tisi, Tomislav Belgusic, Karen Fidanyan, Guillaume Fraux, Jacob Higer, Matthias Kellner, Tao E. Li, Eszter S. Pos, Elia Stocco, George Trenins, Barak Hirshberg, Mariana Rossi, and Michele Ceriotti
Related MARVEL publication:
- Y. Litman, V. Kapil, Y. M. Y. Feldman, D. Tisi, T. Belgusic, K. Fidanyan, G. Fraux, J. Higer, M. Kellner, T. E. Li, E. S. Pos, E. Stocco, G. Trenins, B. Hirshberg, M. Rossi, M. Ceriotti, i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations, The Journal of Chemical Physics 161, 062504 (2024). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
github.com/JorenBE/GPT-Challenge — GPT-Challenge, by Joren Van Herck, Maria Victoria Gil, and Amir Elahi
Related MARVEL publication:
- J. Van Herck, M. V. Gil, K. M. Jablonka, A. Abrudan, A. S. Anker, M. Asgari, B. Blaiszik, A. Buffo, L. Choudhury, C. Corminboeuf, H. Daglar, A. M. Elahi, I. T. Foster, S. Garcia, M. Garvin, G. Godin, L. L. Good, J. Gu, N. Xiao Hu, X. Jin, T. Junkers, S. Keskin, T. P. J. Knowles, R. Laplaza, M. Lessona, S. Majumdar, H. Mashhadimoslem, R. D. Mcintosh, S. M. Moosavi, B. Mouriño, F. Nerli, C. Pevida, N. Poudineh, M. Rajabi-Kochi, K. L. Saar, F. Hooriabad Saboor, M. Sagharichiha, K. J. Schmidt, J. Shi, E. Simone, D. Svatunek, M. Taddei, I. Tetko, D. Tolnai, S. Vahdatifar, J. Whitmer, D. C. F. Wieland, R. Willumeit-Römer, A. Züttel, B. Smit, Assessment of fine-tuned large language models for real-world chemistry and material science applications, Chemical Science 16, 670–684 (2025). [Open Access URL]
  Group(s): Corminboeuf, Smit / Project(s): P1, P2
github.com/SPOC-group/numerics-gibbs-sampling-neural-nets — Gibbs Sampling the Posterior of Neural Networks, by Giovanni Piccioli, Emanuele Troiani, and Lenka Zdeborová
Related MARVEL publication:
- G. Piccioli, E. Troiani, L. Zdeborová, Gibbs sampling the posterior of neural networks, Journal of Physics A: Mathematical and Theoretical 57, 125002 (2024). [Open Access URL]
  Group(s): Zdeborova / Project(s): P2

10.24435/materialscloud:1g-w5 — SPAᴴM(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations, by K. R. Briling, Y. Calvino Alonso, A. Fabrizio, C. Corminboeuf
Related MARVEL publication:
- K. R. Briling, Y. Calvino Alonso, A. Fabrizio, C. Corminboeuf, SPA^HM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations, Journal of Chemical Theory and Computation(2024). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.24435/materialscloud:aa-2w — Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design, by J. T. Blaskovits, R. Laplaza, S. Vela, C. Corminboeuf
Related MARVEL publication:
- J. T. Blaskovits, R. Laplaza, S. Vela, C. Corminboeuf, Data‐Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top‐Down/Bottom‐Up Design, Advanced Materials 2023, 2305602 (2023). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.24435/materialscloud:73-yn — Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25, by N. Lopanitsyna, G. Fraux, M. A. Springer, S. De, M. Ceriotti
Related MARVEL publication:
- N. Lopanitsyna, G. Fraux, M. A. Springer, S. De, M. Ceriotti, Modeling high-entropy transition metal alloys with alchemical compression, Physical Review Materials 7, 045802 (2023). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:71-21 — Lattice energies and relaxed geometries for 2'707 organic molecular crystals and their 3'242 molecular components., by R. Cersonsky, M. Pakhnova, E. Engel, M. Ceriotti
Related MARVEL publication:
- R. K. Cersonsky, M. Pakhnova, E. A. Engel, M. Ceriotti, A data-driven interpretation of the stability of organic molecular crystals, Chemical Science 14, 1272–1285 (2023). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2, DD1
10.5281/zenodo.8003293 — Bispectrum degenerate B8 data, by J. Nigam, M. Ceriotti
Related MARVEL publication:
- J. Nigam, S. N. Pozdnyakov, K. K. Huguenin-Dumittan, M. Ceriotti, Completeness of Atomic Structure Representations, arXiv:2302.14770 (2023). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2

github.com/lab-cosmo/sphericart — sphericart, by M. Ceriotti, F. Bigi, G. Fraux, N. J. Browning, kanduri, C. Ortner, L. Zhang, G. Schwartz, and F. Musil
Related MARVEL publication:
- F. Bigi, G. Fraux, N. J. Browning, M. Ceriotti, Fast evaluation of spherical harmonics with sphericart, The Journal of Chemical Physics 159, 064802 (2023). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2

10.24435/materialscloud:js-pz — SPAᴴM: the spectrum of approximated hamiltonian matrices representations, by A. Fabrizio, K. R. Briling, C. Corminboeuf
Related MARVEL publication:
- A. Fabrizio, K. R. Briling, C. Corminboeuf, SPAHM: the spectrum of approximated Hamiltonian matrices representations, Digital Discovery 1, 286–294 (2022). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.24435/materialscloud:v4-sn — OSCAR: An extensive repository of chemically and functionally diverse organocatalysts, by S. Gallarati, P. van Gerwen, R. Laplaza, S. Vela, A. Fabrizio, C. Corminboeuf
Related MARVEL publication:
- S. Gallarati, P. van Gerwen, R. Laplaza, S. Vela, A. Fabrizio, C. Corminboeuf, OSCAR: an extensive repository of chemically and functionally diverse organocatalysts, Chemical Science 13, 13782–13794 (2022). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2
10.24435/materialscloud:9g-k6 — Thermodynamics and dielectric response of BaTiO₃ by data-driven modeling, by L. Gigli, M. Veit, M. Kotiuga, G. Pizzi, N. Marzari, M. Ceriotti
Related MARVEL publication:
- L. Gigli, M. Veit, M. Kotiuga, G. Pizzi, N. Marzari, M. Ceriotti, Thermodynamics and dielectric response of BaTiO₃ by data-driven modeling, npj Computational Materials 8, 209 (2022). [Open Access URL]
  Group(s): Ceriotti, Marzari, Pizzi / Project(s): P2, P3, P4
10.24435/materialscloud:xw-5k — Ranking the synthesizability of hypothetical zeolites with the sorting hat, by B. A. Helfrecht, G. Pireddu, R. Semino, S. M. Auerbach, M. Ceriotti
Related MARVEL publication:
- B. A. Helfrecht, G. Pireddu, R. Semino, S. M. Auerbach, M. Ceriotti, Ranking the Synthesizability of Hypothetical Zeolites with the Sorting Hat, Digital Discovery 1, 779–789 (2022). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:36-ff — Predicting hot-electron free energies from ground-state data, by C. Ben Mahmoud, F. Grasselli, M. Ceriotti
Related MARVEL publication:
- C. B. Mahmoud, F. Grasselli, M. Ceriotti, Predicting hot-electron free energies from ground-state data, Physical Review B 106, L121116 (2022). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:3f-g3 — Unified theory of atom-centered representations and message-passing machine-learning schemes, by J. Nigam, S. Pozdnyakov, G. Fraux, M. Ceriotti
Related MARVEL publication:
- J. Nigam, S. Pozdnyakov, G. Fraux, M. Ceriotti, Unified Theory of Atom-Centered Representations and Message-Passing Machine-Learning Schemes, The Journal of Chemical Physics 156, 204115 (2022). [Open Access URL]
  Group(s): Ceriotti / Project(s): P2
10.24435/materialscloud:g5-5r — cell2mol: encoding chemistry to interpret crystallographic data, by S. Vela, R. Laplaza, Y. Cho, C. Corminboeuf
Related MARVEL publication:
- S. Vela, R. Laplaza, Y. Cho, C. Corminboeuf, cell2mol: encoding chemistry to interpret crystallographic data, npj Computational Materials 8, 188 (2022). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2

10.5281/zenodo.5172582 — lcmd-epfl/Local_Kernel_Regression: First release, by Raimon-Fa
Related MARVEL publication:
- R. Fabregat, A. Fabrizio, E. A. Engel, B. Meyer, V. Juraskova, M. Ceriotti, C. Corminboeuf, Local kernel regression and neural network approaches to the conformational landscapes of oligopeptides, Journal of Chemical Theory and Computation 18, 1467–1479 (2022). [Open Access URL]
  Group(s): Ceriotti, Corminboeuf / Project(s): P2

10.5281/zenodo.6627913 — Data to support "Physics-based representations for machine learning properties of chemical reactions, by P. Van Gerwen, A. Fabrizio, M. Wodrich, C. Corminboeuf
Related MARVEL publication:
- P. van Gerwen, A. Fabrizio, M. Wodrich, C. Corminboeuf, Physics-based representations for machine learning properties of chemical reactions, Machine Learning: Science and Technology 3, 045005 (2022). [Open Access URL]
  Group(s): Corminboeuf / Project(s): P2