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.
109 related articles for article (PubMed ID: 36730848)
1. Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water. Gomes-Filho MS; Torres A; Reily Rocha A; Pedroza LS J Phys Chem B; 2023 Feb; 127(6):1422-1428. PubMed ID: 36730848 [TBL] [Abstract][Full Text] [Related]
2. Using Neural Network Force Fields to Ascertain the Quality of Torres A; Pedroza LS; Fernandez-Serra M; Rocha AR J Phys Chem B; 2021 Sep; 125(38):10772-10778. PubMed ID: 34543024 [TBL] [Abstract][Full Text] [Related]
3. Proceedings of the Second Workshop on Theory meets Industry (Erwin-Schrödinger-Institute (ESI), Vienna, Austria, 12-14 June 2007). Hafner J J Phys Condens Matter; 2008 Feb; 20(6):060301. PubMed ID: 21693862 [TBL] [Abstract][Full Text] [Related]
5. Hydrogen-bond structure dynamics in bulk water: insights from Liu J; He X; Zhang JZH; Qi LW Chem Sci; 2018 Feb; 9(8):2065-2073. PubMed ID: 29675248 [TBL] [Abstract][Full Text] [Related]
6. Structure and dynamics of the hydration shells of the Zn(2+) ion from ab initio molecular dynamics and combined ab initio and classical molecular dynamics simulations. Cauët E; Bogatko S; Weare JH; Fulton JL; Schenter GK; Bylaska EJ J Chem Phys; 2010 May; 132(19):194502. PubMed ID: 20499974 [TBL] [Abstract][Full Text] [Related]
7. Ab initio molecular dynamics with discrete variable representation basis sets: techniques and application to liquid water. Lee HS; Tuckerman ME J Phys Chem A; 2006 Apr; 110(16):5549-60. PubMed ID: 16623489 [TBL] [Abstract][Full Text] [Related]
8. Neural Network Force Fields for Metal Growth Based on Energy Decompositions. Hu Q; Weng M; Chen X; Li S; Pan F; Wang LW J Phys Chem Lett; 2020 Feb; 11(4):1364-1369. PubMed ID: 32000486 [TBL] [Abstract][Full Text] [Related]
9. Understanding the Temperature Dependence and Finite Size Effects in Ab Initio MD Simulations of the Hydrated Electron. Park SJ; Schwartz BJ J Chem Theory Comput; 2022 Aug; 18(8):4973-4982. PubMed ID: 35834750 [TBL] [Abstract][Full Text] [Related]
10. Nanosecond solvation dynamics of the hematite/liquid water interface at hybrid DFT accuracy using committee neural network potentials. Schienbein P; Blumberger J Phys Chem Chem Phys; 2022 Jun; 24(25):15365-15375. PubMed ID: 35703465 [TBL] [Abstract][Full Text] [Related]
11. A simple AIMD approach to derive atomic charges for condensed phase simulation of ionic liquids. Zhang Y; Maginn EJ J Phys Chem B; 2012 Aug; 116(33):10036-48. PubMed ID: 22852554 [TBL] [Abstract][Full Text] [Related]
12. Dynamical properties of liquid water from ab initio molecular dynamics performed in the complete basis set limit. Lee HS; Tuckerman ME J Chem Phys; 2007 Apr; 126(16):164501. PubMed ID: 17477608 [TBL] [Abstract][Full Text] [Related]
13. Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based Ko HY; Jia J; Santra B; Wu X; Car R; DiStasio RA J Chem Theory Comput; 2020 Jun; 16(6):3757-3785. PubMed ID: 32045232 [TBL] [Abstract][Full Text] [Related]
14. Aqueous solutions: state of the art in ab initio molecular dynamics. Hassanali AA; Cuny J; Verdolino V; Parrinello M Philos Trans A Math Phys Eng Sci; 2014 Mar; 372(2011):20120482. PubMed ID: 24516179 [TBL] [Abstract][Full Text] [Related]
16. Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Calio PB; Hocky GM; Voth GA J Chem Theory Comput; 2020 Sep; 16(9):5675-5684. PubMed ID: 32786913 [TBL] [Abstract][Full Text] [Related]
17. Structure, dynamics, and reactivity of hydrated electrons by ab initio molecular dynamics. Marsalek O; Uhlig F; VandeVondele J; Jungwirth P Acc Chem Res; 2012 Jan; 45(1):23-32. PubMed ID: 21899274 [TBL] [Abstract][Full Text] [Related]
18. Quantum effects in liquid water from an ab initio-based polarizable force field. Paesani F; Iuchi S; Voth GA J Chem Phys; 2007 Aug; 127(7):074506. PubMed ID: 17718619 [TBL] [Abstract][Full Text] [Related]
19. Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations. Pattnaik P; Raghunathan S; Kalluri T; Bhimalapuram P; Jawahar CV; Priyakumar UD J Phys Chem A; 2020 Aug; 124(34):6954-6967. PubMed ID: 32786995 [TBL] [Abstract][Full Text] [Related]
20. Developing ab initio quality force fields from condensed phase quantum-mechanics/molecular-mechanics calculations through the adaptive force matching method. Akin-Ojo O; Song Y; Wang F J Chem Phys; 2008 Aug; 129(6):064108. PubMed ID: 18715052 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]