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.
216 related articles for article (PubMed ID: 31615245)
21. Performance and Cost Assessment of Machine Learning Interatomic Potentials. Zuo Y; Chen C; Li X; Deng Z; Chen Y; Behler J; Csányi G; Shapeev AV; Thompson AP; Wood MA; Ong SP J Phys Chem A; 2020 Jan; 124(4):731-745. PubMed ID: 31916773 [TBL] [Abstract][Full Text] [Related]
22. Adaptive, Geometric Networks for Efficient Coarse-Grained Ab Initio Molecular Dynamics with Post-Hartree-Fock Accuracy. Ricard TC; Haycraft C; Iyengar SS J Chem Theory Comput; 2018 Jun; 14(6):2852-2866. PubMed ID: 29771516 [TBL] [Abstract][Full Text] [Related]
23. Transferability and accuracy by combining dispersionless density functional and incremental post-Hartree-Fock theories: Noble gases adsorption on coronene/graphene/graphite surfaces. de Lara-Castells MP; Bartolomei M; Mitrushchenkov AO; Stoll H J Chem Phys; 2015 Nov; 143(19):194701. PubMed ID: 26590547 [TBL] [Abstract][Full Text] [Related]
24. Ab initio molecular dynamics with nuclear quantum effects at classical cost: Ring polymer contraction for density functional theory. Marsalek O; Markland TE J Chem Phys; 2016 Feb; 144(5):054112. PubMed ID: 26851913 [TBL] [Abstract][Full Text] [Related]
25. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. Grimme S; Antony J; Ehrlich S; Krieg H J Chem Phys; 2010 Apr; 132(15):154104. PubMed ID: 20423165 [TBL] [Abstract][Full Text] [Related]
26. 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]
27. 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]
28. The accurate calculation of the band gap of liquid water by means of GW corrections applied to plane-wave density functional theory molecular dynamics simulations. Fang C; Li WF; Koster RS; Klimeš J; van Blaaderen A; van Huis MA Phys Chem Chem Phys; 2015 Jan; 17(1):365-75. PubMed ID: 25388568 [TBL] [Abstract][Full Text] [Related]
29. General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. Gong X; Li H; Zou N; Xu R; Duan W; Xu Y Nat Commun; 2023 May; 14(1):2848. PubMed ID: 37208320 [TBL] [Abstract][Full Text] [Related]
30. Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation. Schütt O; VandeVondele J J Chem Theory Comput; 2018 Aug; 14(8):4168-4175. PubMed ID: 29957943 [TBL] [Abstract][Full Text] [Related]
31. Balancing simulation accuracy and efficiency with the Amber united atom force field. Hsieh MJ; Luo R J Phys Chem B; 2010 Mar; 114(8):2886-93. PubMed ID: 20131885 [TBL] [Abstract][Full Text] [Related]
32. Constructing simple yet accurate potentials for describing the solvation of HCl/water clusters in bulk helium and nanodroplets. Boese AD; Forbert H; Masia M; Tekin A; Marx D; Jansen G Phys Chem Chem Phys; 2011 Aug; 13(32):14550-64. PubMed ID: 21687854 [TBL] [Abstract][Full Text] [Related]
33. Machine learning accurate exchange and correlation functionals of the electronic density. Dick S; Fernandez-Serra M Nat Commun; 2020 Jul; 11(1):3509. PubMed ID: 32665540 [TBL] [Abstract][Full Text] [Related]
35. Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations. Xie X; Persson KA; Small DW J Chem Theory Comput; 2020 Jul; 16(7):4256-4270. PubMed ID: 32502350 [TBL] [Abstract][Full Text] [Related]
36. Machine learning electronic structure methods based on the one-electron reduced density matrix. Shao X; Paetow L; Tuckerman ME; Pavanello M Nat Commun; 2023 Oct; 14(1):6281. PubMed ID: 37805614 [TBL] [Abstract][Full Text] [Related]
37. Treating Semiempirical Hamiltonians as Flexible Machine Learning Models Yields Accurate and Interpretable Results. Hu F; He F; Yaron DJ J Chem Theory Comput; 2023 Sep; 19(18):6185-6196. PubMed ID: 37705220 [TBL] [Abstract][Full Text] [Related]
38. 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]
39. Determination of hyper-parameters in the atomic descriptors for efficient and robust molecular dynamics simulations with machine learning forces. Lin J; Tamura R; Futamura Y; Sakurai T; Miyazaki T Phys Chem Chem Phys; 2023 Jul; 25(27):17978-17986. PubMed ID: 37377109 [TBL] [Abstract][Full Text] [Related]
40. BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules. Laghuvarapu S; Pathak Y; Priyakumar UD J Comput Chem; 2020 Mar; 41(8):790-799. PubMed ID: 31845368 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]