154 related articles for article (PubMed ID: 37165873)
1. Machine learning transferable atomic forces for large systems from underconverged molecular fragments.
Herbold M; Behler J
Phys Chem Chem Phys; 2023 May; 25(18):12979-12989. PubMed ID: 37165873
[TBL] [Abstract][Full Text] [Related]
2. A Hessian-based assessment of atomic forces for training machine learning interatomic potentials.
Herbold M; Behler J
J Chem Phys; 2022 Mar; 156(11):114106. PubMed ID: 35317596
[TBL] [Abstract][Full Text] [Related]
3. General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer.
Ko TW; Finkler JA; Goedecker S; Behler J
Acc Chem Res; 2021 Feb; 54(4):808-817. PubMed ID: 33513012
[TBL] [Abstract][Full Text] [Related]
4. How to train a neural network potential.
Tokita AM; Behler J
J Chem Phys; 2023 Sep; 159(12):. PubMed ID: 38127396
[TBL] [Abstract][Full Text] [Related]
5. DFT-Quality Adsorption Simulations in Metal-Organic Frameworks Enabled by Machine Learning Potentials.
Goeminne R; Vanduyfhuys L; Van Speybroeck V; Verstraelen T
J Chem Theory Comput; 2023 Sep; 19(18):6313-6325. PubMed ID: 37642314
[TBL] [Abstract][Full Text] [Related]
6. From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5.
Eckhoff M; Behler J
J Chem Theory Comput; 2019 Jun; 15(6):3793-3809. PubMed ID: 31091097
[TBL] [Abstract][Full Text] [Related]
7. Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials.
Omranpour A; Montero De Hijes P; Behler J; Dellago C
J Chem Phys; 2024 May; 160(17):. PubMed ID: 38748006
[TBL] [Abstract][Full Text] [Related]
8. High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark.
Shanavas Rasheeda D; Martín Santa Daría A; Schröder B; Mátyus E; Behler J
Phys Chem Chem Phys; 2022 Dec; 24(48):29381-29392. PubMed ID: 36459127
[TBL] [Abstract][Full Text] [Related]
9. Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions.
Pan X; Yang J; Van R; Epifanovsky E; Ho J; Huang J; Pu J; Mei Y; Nam K; Shao Y
J Chem Theory Comput; 2021 Sep; 17(9):5745-5758. PubMed ID: 34468138
[TBL] [Abstract][Full Text] [Related]
10. A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures.
Zeng C; Chen X; Peterson AA
J Chem Phys; 2022 Feb; 156(6):064104. PubMed ID: 35168344
[TBL] [Abstract][Full Text] [Related]
11. Perspective: Machine learning potentials for atomistic simulations.
Behler J
J Chem Phys; 2016 Nov; 145(17):170901. PubMed ID: 27825224
[TBL] [Abstract][Full Text] [Related]
12. Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses.
Bertani M; Charpentier T; Faglioni F; Pedone A
J Chem Theory Comput; 2024 Feb; 20(3):1358-1370. PubMed ID: 38217496
[TBL] [Abstract][Full Text] [Related]
13. Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks.
Sun G; Sautet P
J Chem Theory Comput; 2019 Oct; 15(10):5614-5627. PubMed ID: 31465216
[TBL] [Abstract][Full Text] [Related]
14. Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding.
Ko TW; Finkler JA; Goedecker S; Behler J
J Chem Theory Comput; 2023 Jun; 19(12):3567-3579. PubMed ID: 37289440
[TBL] [Abstract][Full Text] [Related]
15. Subsystem Density Functional Theory Augmented by a Delta Learning Approach to Achieve Kohn-Sham Accuracy.
Pauletti M; Rybkin VV; Iannuzzi M
J Chem Theory Comput; 2021 Oct; 17(10):6423-6431. PubMed ID: 34505765
[TBL] [Abstract][Full Text] [Related]
16. Learning from the density to correct total energy and forces in first principle simulations.
Dick S; Fernandez-Serra M
J Chem Phys; 2019 Oct; 151(14):144102. PubMed ID: 31615245
[TBL] [Abstract][Full Text] [Related]
17. Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments.
Zaverkin V; Holzmüller D; Steinwart I; Kästner J
J Chem Theory Comput; 2021 Oct; 17(10):6658-6670. PubMed ID: 34585927
[TBL] [Abstract][Full Text] [Related]
18. AENET-LAMMPS and AENET-TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials.
Chen MS; Morawietz T; Mori H; Markland TE; Artrith N
J Chem Phys; 2021 Aug; 155(7):074801. PubMed ID: 34418919
[TBL] [Abstract][Full Text] [Related]
19. A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer.
Ko TW; Finkler JA; Goedecker S; Behler J
Nat Commun; 2021 Jan; 12(1):398. PubMed ID: 33452239
[TBL] [Abstract][Full Text] [Related]
20. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems.
Behler J
Angew Chem Int Ed Engl; 2017 Oct; 56(42):12828-12840. PubMed ID: 28520235
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]