These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
195 related articles for article (PubMed ID: 37184017)
1. Machine learning interatomic potential for silicon-nitride (Si3N4) by active learning. Milardovich D; Wilhelmer C; Waldhoer D; Cvitkovich L; Sivaraman G; Grasser T J Chem Phys; 2023 May; 158(19):. PubMed ID: 37184017 [TBL] [Abstract][Full Text] [Related]
2. Atomistic Simulation of HF Etching Process of Amorphous Si Hong C; Oh S; An H; Kim PH; Kim Y; Ko JH; Sue J; Oh D; Park S; Han S ACS Appl Mater Interfaces; 2024 Sep; 16(36):48457-48469. PubMed ID: 39198036 [TBL] [Abstract][Full Text] [Related]
3. Gaussian approximation potentials for accurate thermal properties of two-dimensional materials. Kocabaş T; Keçeli M; Vázquez-Mayagoitia Á; Sevik C Nanoscale; 2023 May; 15(19):8772-8780. PubMed ID: 37098822 [TBL] [Abstract][Full Text] [Related]
4. Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga Liu YB; Yang JY; Xin GM; Liu LH; Csányi G; Cao BY J Chem Phys; 2020 Oct; 153(14):144501. PubMed ID: 33086840 [TBL] [Abstract][Full Text] [Related]
9. Modeling the Phase-Change Memory Material, Ge Mocanu FC; Konstantinou K; Lee TH; Bernstein N; Deringer VL; Csányi G; Elliott SR J Phys Chem B; 2018 Sep; 122(38):8998-9006. PubMed ID: 30173522 [TBL] [Abstract][Full Text] [Related]
10. Combining phonon accuracy with high transferability in Gaussian approximation potential models. George J; Hautier G; Bartók AP; Csányi G; Deringer VL J Chem Phys; 2020 Jul; 153(4):044104. PubMed ID: 32752705 [TBL] [Abstract][Full Text] [Related]
11. 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]
12. Harnessing machine learning for efficient large-scale interatomic potential for sildenafil and pharmaceuticals containing H, C, N, O, and S. Nikidis E; Kyriakopoulos N; Tohid R; Kachrimanis K; Kioseoglou J Nanoscale; 2024 Oct; 16(38):18014-18026. PubMed ID: 39252581 [TBL] [Abstract][Full Text] [Related]
13. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. Deringer VL; Caro MA; Csányi G Adv Mater; 2019 Nov; 31(46):e1902765. PubMed ID: 31486179 [TBL] [Abstract][Full Text] [Related]
14. Data-Driven Learning of Total and Local Energies in Elemental Boron. Deringer VL; Pickard CJ; Csányi G Phys Rev Lett; 2018 Apr; 120(15):156001. PubMed ID: 29756876 [TBL] [Abstract][Full Text] [Related]
15. 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]
16. Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts. Choyal V; Sagar N; Sai Gautam G J Chem Theory Comput; 2024 Jun; 20(11):4844-4856. PubMed ID: 38787289 [TBL] [Abstract][Full Text] [Related]
17. Improved interatomic potentials for silicon-fluorine and silicon-chlorine. Humbird D; Graves DB J Chem Phys; 2004 Feb; 120(5):2405-12. PubMed ID: 15268380 [TBL] [Abstract][Full Text] [Related]
18. An accurate and transferable machine learning potential for carbon. Rowe P; Deringer VL; Gasparotto P; Csányi G; Michaelides A J Chem Phys; 2020 Jul; 153(3):034702. PubMed ID: 32716159 [TBL] [Abstract][Full Text] [Related]
19. Development of interatomic potential for Al-Tb alloys using a deep neural network learning method. Tang L; Yang ZJ; Wen TQ; Ho KM; Kramer MJ; Wang CZ Phys Chem Chem Phys; 2020 Sep; 22(33):18467-18479. PubMed ID: 32778859 [TBL] [Abstract][Full Text] [Related]