相似文献
1
Using Machine Learning to Greatly Accelerate Path Integral Molecular Dynamics.
J Chem Theory Comput. 2022 Feb 8;18(2):599-604. doi: 10.1021/acs.jctc.1c01085. Epub 2022 Jan 4.
2
Ab initio molecular dynamics with nuclear quantum effects at classical cost: Ring polymer contraction for density functional theory.
J Chem Phys. 2016 Feb 7;144(5):054112. doi: 10.1063/1.4941093.
3
Simulating Nuclear and Electronic Quantum Effects in Enzymes.
Methods Enzymol. 2016;577:389-418. doi: 10.1016/bs.mie.2016.05.047. Epub 2016 Jul 15.
4
Modeling quantum nuclei with perturbed path integral molecular dynamics.
Chem Sci. 2016 Feb 1;7(2):1368-1372. doi: 10.1039/c5sc03443d. Epub 2015 Oct 30.
5
Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach.
Front Mol Biosci. 2022 May 19;9:851311. doi: 10.3389/fmolb.2022.851311. eCollection 2022.
6
Multiple time step integrators in ab initio molecular dynamics.
J Chem Phys. 2014 Feb 28;140(8):084116. doi: 10.1063/1.4866176.
7
Affordable Path Integral for Thermodynamic Properties via Molecular Dynamics Simulations Using Semiempirical Reference Potential.
J Phys Chem A. 2021 Dec 23;125(50):10677-10685. doi: 10.1021/acs.jpca.1c07727. Epub 2021 Dec 12.
8
Quantum ring-polymer contraction method: Including nuclear quantum effects at no additional computational cost in comparison to ab initio molecular dynamics.
Phys Rev E. 2016 Apr;93:043305. doi: 10.1103/PhysRevE.93.043305. Epub 2016 Apr 7.
9
Ab initio molecular dynamics with discrete variable representation basis sets: techniques and application to liquid water.
J Phys Chem A. 2006 Apr 27;110(16):5549-60. doi: 10.1021/jp0570770.
10
Combining Machine Learning Approaches and Accurate Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution.
J Chem Theory Comput. 2022 Sep 13;18(9):5410-5421. doi: 10.1021/acs.jctc.2c00400. Epub 2022 Aug 5.
引用本文的文献
1
Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.
Chem Sci. 2025 Sep 4. doi: 10.1039/d5sc05579b.
2
Coarse-Graining with Equivariant Neural Networks: A Path Toward Accurate and Data-Efficient Models.
J Phys Chem B. 2023 Dec 14;127(49):10564-10572. doi: 10.1021/acs.jpcb.3c05928. Epub 2023 Nov 30.
3
Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics.
Nat Commun. 2023 Feb 23;14(1):1008. doi: 10.1038/s41467-023-36666-y.
4
Quantum Free Energy Profiles for Molecular Proton Transfers.
J Chem Theory Comput. 2023 Jan 10;19(1):18-24. doi: 10.1021/acs.jctc.2c00874. Epub 2022 Dec 23.
5
Facilitating QM/MM free energy simulations by Gaussian process regression with derivative observations.
Phys Chem Chem Phys. 2022 Oct 27;24(41):25134-25143. doi: 10.1039/d2cp02820d.
6
Centroid Molecular Dynamics Can Be Greatly Accelerated through Neural Network Learned Centroid Forces Derived from Path Integral Molecular Dynamics.
J Chem Theory Comput. 2022 Oct 11;18(10):5856-5863. doi: 10.1021/acs.jctc.2c00706. Epub 2022 Sep 14.
7
Coupled Multiphysics Modelling of Sensors for Chemical, Biomedical, and Environmental Applications with Focus on Smart Materials and Low-Dimensional Nanostructures.
Chemosensors (Basel). 2022 Apr 25;10(5):157. doi: 10.3390/chemosensors10050157. eCollection 2022 May.
本文引用的文献
1
Using Constrained Density Functional Theory to Track Proton Transfers and to Sample Their Associated Free Energy Surface.
J Chem Theory Comput. 2021 Sep 14;17(9):5759-5765. doi: 10.1021/acs.jctc.1c00609. Epub 2021 Sep 1.
2
Nuclear Quantum Effects Largely Influence Molecular Dissociation and Proton Transfer in Liquid Water under an Electric Field.
J Phys Chem Lett. 2020 Nov 5;11(21):8983-8988. doi: 10.1021/acs.jpclett.0c02581. Epub 2020 Oct 9.
3
Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Molecular Dynamics Simulations of Water.
J Chem Theory Comput. 2020 Sep 8;16(9):5675-5684. doi: 10.1021/acs.jctc.0c00558. Epub 2020 Aug 13.
4
Decoding the spectroscopic features and time scales of aqueous proton defects.
J Chem Phys. 2018 Jun 14;148(22):222833. doi: 10.1063/1.5023704.
5
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics.
Phys Rev Lett. 2018 Apr 6;120(14):143001. doi: 10.1103/PhysRevLett.120.143001.
6
Accelerated path-integral simulations using ring-polymer interpolation.
J Chem Phys. 2017 Dec 14;147(22):224107. doi: 10.1063/1.5006465.
7
Quantum Dynamics and Spectroscopy of Ab Initio Liquid Water: The Interplay of Nuclear and Electronic Quantum Effects.
J Phys Chem Lett. 2017 Apr 6;8(7):1545-1551. doi: 10.1021/acs.jpclett.7b00391. Epub 2017 Mar 22.
8
Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges.
Chem Rev. 2016 Jul 13;116(13):7529-50. doi: 10.1021/acs.chemrev.5b00674. Epub 2016 Apr 6.
9
Accelerating Ab Initio Path Integral Simulations via Imaginary Multiple-Timestepping.
J Chem Theory Comput. 2016 Apr 12;12(4):1627-38. doi: 10.1021/acs.jctc.6b00021. Epub 2016 Mar 25.
10
Ab initio molecular dynamics with nuclear quantum effects at classical cost: Ring polymer contraction for density functional theory.
J Chem Phys. 2016 Feb 7;144(5):054112. doi: 10.1063/1.4941093.
Zotero 插件