Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics
出版年份 2022 全文链接
标题
Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics
作者
关键词
-
出版物
Journal of Chemical Theory and Computation
Volume 18, Issue 2, Pages 599-604
出版商
American Chemical Society (ACS)
发表日期
2022-01-05
DOI
10.1021/acs.jctc.1c01085
参考文献
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- (2021) Chenghan Li et al. Journal of Chemical Theory and Computation
- Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Ab Initio Molecular Dynamics Simulations of Water
- (2020) Paul B. Calio et al. Journal of Chemical Theory and Computation
- Nuclear Quantum Effects Largely Influence Molecular Dissociation and Proton Transfer in Liquid Water under an Electric Field
- (2020) Giuseppe Cassone Journal of Physical Chemistry Letters
- Decoding the spectroscopic features and time scales of aqueous proton defects
- (2018) Joseph A. Napoli et al. JOURNAL OF CHEMICAL PHYSICS
- Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
- (2018) Linfeng Zhang et al. PHYSICAL REVIEW LETTERS
- Nuclear quantum effects enter the mainstream
- (2018) Thomas E. Markland et al. Nature Reviews Chemistry
- Accelerated path-integral simulations using ring-polymer interpolation
- (2017) Samuel J. Buxton et al. JOURNAL OF CHEMICAL PHYSICS
- Quantum Dynamics and Spectroscopy of Ab Initio Liquid Water: The Interplay of Nuclear and Electronic Quantum Effects
- (2017) Ondrej Marsalek et al. Journal of Physical Chemistry Letters
- Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges
- (2016) Michele Ceriotti et al. CHEMICAL REVIEWS
- Accurate molecular dynamics and nuclear quantum effects at low cost by multiple steps in real and imaginary time: Using density functional theory to accelerate wavefunction methods
- (2016) V. Kapil et al. JOURNAL OF CHEMICAL PHYSICS
- Ab initio molecular dynamics with nuclear quantum effects at classical cost: Ring polymer contraction for density functional theory
- (2016) Ondrej Marsalek et al. JOURNAL OF CHEMICAL PHYSICS
- Accelerating Ab Initio Path Integral Simulations via Imaginary Multiple-Timestepping
- (2016) Xiaolu Cheng et al. Journal of Chemical Theory and Computation
- How to remove the spurious resonances from ring polymer molecular dynamics
- (2014) Mariana Rossi et al. JOURNAL OF CHEMICAL PHYSICS
- Communication: Multiple-timestep ab initio molecular dynamics with electron correlation
- (2013) Ryan P. Steele JOURNAL OF CHEMICAL PHYSICS
- Benchmark Study of the SCC-DFTB Approach for a Biomolecular Proton Channel
- (2013) Ruibin Liang et al. Journal of Chemical Theory and Computation
- Efficient First-Principles Calculation of the Quantum Kinetic Energy and Momentum Distribution of Nuclei
- (2012) Michele Ceriotti et al. PHYSICAL REVIEW LETTERS
- DFTB3: Extension of the Self-Consistent-Charge Density-Functional Tight-Binding Method (SCC-DFTB)
- (2011) Michael Gaus et al. Journal of Chemical Theory and Computation
- Application of the SCC-DFTB Method to Neutral and Protonated Water Clusters and Bulk Water
- (2011) Puja Goyal et al. JOURNAL OF PHYSICAL CHEMISTRY B
- A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu
- (2010) Stefan Grimme et al. JOURNAL OF CHEMICAL PHYSICS
- The Self-Consistent Charge Density Functional Tight Binding Method Applied to Liquid Water and the Hydrated Excess Proton: Benchmark Simulations
- (2010) C. Mark Maupin et al. JOURNAL OF PHYSICAL CHEMISTRY B
- An efficient ring polymer contraction scheme for imaginary time path integral simulations
- (2008) Thomas E. Markland et al. JOURNAL OF CHEMICAL PHYSICS
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