| 1 | Intercavity polariton slows down dynamics in strongly coupled cavities | Coautor: Sauceda H.E., García Jomaso Y.A., Vargas B., Domínguez D.L., et al. | 2024 | NATURE COMMUNICATIONS | WoS-id: 001198930500031
Scopus-id: 2-s2.0-85189616868
| 2 | 2 |
| 2 | Remote control of excitonic materials using coupled optical cavities | Coautor: Sauceda H., Pirruccio G., Jomaso Y.G., Vargas B., et al. | 2024 | EOS ANNUAL MEETING, EOSAM 2024 | WoS-id: 001353751800113
Scopus-id: 2-s2.0-85212512281
| 0 | 0 |
| 3 | Accurate global machine learning force fields for molecules with hundreds of atoms | Coautor: Sauceda, Huziel E., Chmiela, Stefan, Vassilev-Galindo, Valentin, Unke, Oliver T., et al. | 2023 | Science Advances | WoS-id: 000911464300045
Scopus-id: 2-s2.0-85146140799
| 89 | 94 |
| 4 | High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks | Coautor y autor de correspondencia: Sauceda, Huziel E., Winkler, Ludwig, Mueller, Klaus-Robert | 2022 | Machine Learning-Science And Technology | WoS-id: 000802740000001
Scopus-id: 2-s2.0-85131679246
| 9 | 9 |
| 5 | BIGDML?Towards accurate quantum machine learning force fields for materials | 1ᵉʳ autor: Sauceda, Huziel E., Galvez-Gonzalez, Luis E., Chmiela, Stefan, Paz-Borbón L.O., et al. | 2022 | NATURE COMMUNICATIONS | WoS-id: 000830675000008
Scopus-id: 2-s2.0-85133016527
| 46 | 47 |
| 6 | Machine Learning Force Fields | Coautor: Sauceda H.E., Unke O.T., Chmiela S., Gastegger M., et al. | 2021 | CHEMICAL REVIEWS | WoS-id: 000691784200010
Scopus-id: 2-s2.0-85102968338
| 830 | 876 |
| 7 | SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects | Coautor: Sauceda H.E., Unke O.T., Chmiela S., Gastegger M., et al. | 2021 | NATURE COMMUNICATIONS | WoS-id: 000730391400003
Scopus-id: 2-s2.0-85121316968
| 189 | 201 |
| 8 | Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields | 1ᵉʳ autor: Sauceda H.E., Gastegger M., Chmiela S., Müller K.-R., et al. | 2020 | JOURNAL OF CHEMICAL PHYSICS | WoS-id: 000576382700002
Scopus-id: 2-s2.0-85092055441
| 30 | 29 |
| 9 | Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches | 2ᵒ autor: Sauceda H.E., Chmiela S., Tkatchenko A., Müller K.-R. | 2020 | Lecture Notes in Physics | WoS-id: 000999317000009
Scopus-id: 2-s2.0-85086092345
| 10 | 16 |
| 10 | Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights | 1ᵉʳ autor: Sauceda H.E., Chmiela S., Poltavsky I., Müller K.-R., et al. | 2020 | Lecture Notes in Physics | WoS-id: 000999317000018
Scopus-id: 2-s2.0-85086098504
| 6 | 8 |
| 11 | Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces | 1ᵉʳ autor: Sauceda H.E., Chmiela S., Poltavsky I., Müller K.-R., et al. | 2019 | JOURNAL OF CHEMICAL PHYSICS | WoS-id: 000462014500006
Scopus-id: 2-s2.0-85063040498
| 99 | 100 |
| 12 | sGDML: Constructing accurate and data efficient molecular force fields using machine learning | 2ᵒ autor: Sauceda H.E., Chmiela S., Poltavsky I., Müller K.-R., et al. | 2019 | COMPUTER PHYSICS COMMUNICATIONS | WoS-id: 000474312900005
Scopus-id: 2-s2.0-85062624346
| 171 | 181 |
| 13 | SchNet - A deep learning architecture for molecules and materials | 2ᵒ autor: Sauceda H.E., Schütt K.T., Kindermans P.-J., Tkatchenko A., et al. | 2018 | JOURNAL OF CHEMICAL PHYSICS | WoS-id: 000437190300025
Scopus-id: 2-s2.0-85044731105
| 1402 | 1487 |
| 14 | Towards exact molecular dynamics simulations with machine-learned force fields | 2ᵒ autor: Sauceda H.E., Chmiela S., Müller K.-R., Tkatchenko A. | 2018 | NATURE COMMUNICATIONS | WoS-id: 000445329000022
Scopus-id: 2-s2.0-85053868687
| 553 | 581 |