Pillar 2

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

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 (2022). [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 BaTiO_3 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