167 related articles for article (PubMed ID: 35317596)
1. 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]
2. 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]
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. 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]
6. Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration.
Kang PL; Shang C; Liu ZP
Acc Chem Res; 2020 Oct; 53(10):2119-2129. PubMed ID: 32940999
[TBL] [Abstract][Full Text] [Related]
7. Construction of high-dimensional neural network potentials using environment-dependent atom pairs.
Jose KV; Artrith N; Behler J
J Chem Phys; 2012 May; 136(19):194111. PubMed ID: 22612084
[TBL] [Abstract][Full Text] [Related]
8. Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic-inorganic interfaces.
Westermayr J; Chaudhuri S; Jeindl A; Hofmann OT; Maurer RJ
Digit Discov; 2022 Aug; 1(4):463-475. PubMed ID: 36091414
[TBL] [Abstract][Full Text] [Related]
9. Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.
Varughese B; Manna S; Loeffler TD; Batra R; Cherukara MJ; Sankaranarayanan SKRS
ACS Appl Mater Interfaces; 2024 Apr; ():. PubMed ID: 38593033
[TBL] [Abstract][Full Text] [Related]
10. Improve the performance of machine-learning potentials by optimizing descriptors.
Gao H; Wang J; Sun J
J Chem Phys; 2019 Jun; 150(24):244110. PubMed ID: 31255049
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine.
Ge F; Wang R; Qu C; Zheng P; Nandi A; Conte R; Houston PL; Bowman JM; Dral PO
J Phys Chem Lett; 2024 Apr; 15(16):4451-4460. PubMed ID: 38626460
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes.
Gastegger M; Kauffmann C; Behler J; Marquetand P
J Chem Phys; 2016 May; 144(19):194110. PubMed ID: 27208939
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials.
Imbalzano G; Anelli A; Giofré D; Klees S; Behler J; Ceriotti M
J Chem Phys; 2018 Jun; 148(24):241730. PubMed ID: 29960368
[TBL] [Abstract][Full Text] [Related]
17. Voxelized Atomic Structure Potentials: Predicting Atomic Forces with the Accuracy of Quantum Mechanics Using Convolutional Neural Networks.
Barry MC; Wise KE; Kalidindi SR; Kumar S
J Phys Chem Lett; 2020 Nov; 11(21):9093-9099. PubMed ID: 32985196
[TBL] [Abstract][Full Text] [Related]
18. Atomic Energies from a Convolutional Neural Network.
Chen X; Jørgensen MS; Li J; Hammer B
J Chem Theory Comput; 2018 Jul; 14(7):3933-3942. PubMed ID: 29812930
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. TrIP─Transformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias.
Hedelius BE; Tingey D; Della Corte D
J Chem Theory Comput; 2024 Jan; 20(1):199-211. PubMed ID: 38150692
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
