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Next generation extended Lagrangian first principles molecular dynamics
2017
Journal of Chemical Physics
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In combination with proposed low-rank and on-the-fly updates of the kernel, this formulation provides an efficient and general framework for quantum based Born-Oppenheimer molecular dynamics simulations ...
Materials systems that normally exhibit slow self-consistent field convergence can be simulated using integration time steps of the same order as in direct Born-Oppenheimer molecular dynamics, but without ...
It can therefore be used as a simple and efficient single rank-1 update approximation of the kernel K in extended Lagrangian Born-Oppenheimer molecular dynamics. ...
doi:10.1063/1.4985893
pmid:28789552
fatcat:a42fxvgr2vavjlg5bxi2tfunxu
Neural networks and kernel ridge regression for excited states dynamics of CH_2NH_2^+: From single-state to multi-state representations and multi-property machine learning models
[article]
2019
arXiv
pre-print
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An important goal for excited-state machine learning models is their use in dynamics simulations, which needs not only state-specific information but also couplings, i.e., properties involving pairs of ...
Since the applicability of these simulation techniques is limited by the costs of the underlying electronic structure calculations, we develop and assess different machine learning models for this task ...
A trend can be obtained from independent works that applied kernel ridge regression (KRR) and neural networks (NNs) to replace quantum chemical calculations in nonadiabatic molecular dynamics simulations ...
arXiv:1912.08484v1
fatcat:z5j3p3cnhffn7bpl2zch4vt2hu
Machine learning of accurate energy-conserving molecular force fields
2017
Science Advances
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molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. ...
The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with ...
In addition, long-time scale simulations are required to completely understand the dynamics of molecular systems. ...
doi:10.1126/sciadv.1603015
pmid:28508076
pmcid:PMC5419702
fatcat:mphy2qzxxfdknco2rjes4zljpe
Towards exact molecular dynamics simulations with machine-learned force fields
2018
Nature Communications
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Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine ...
Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. ...
Part of this research was performed while the authors were visiting the Institute for Pure and Applied Mathematics, which is supported by the NSF. ...
doi:10.1038/s41467-018-06169-2
pmid:30250077
pmcid:PMC6155327
fatcat:wzda3toxtbfj5goqqjtnemmvuq
Taking materials dynamics to new extremes using machine learning interatomic potentials
2021
Journal of Materials Informatics
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The new potentials are constructed by machinelearning with a high degree of fidelity from quantum-mechanical calculations. ...
Atomic simulations have had a considerable impact on this endeavor because of their ability to uncover materials' microstructure evolution and properties at the scale of the relevant physical phenomena ...
functional theory (DFT) calculations and the computational high efficiency of MD simulations. ...
doi:10.20517/jmi.2021.001
fatcat:qsmwfwkv6fffpoeyelpmqoqqta
A NANOSCALE MESHFREE PARTICLE METHOD WITH THE IMPLEMENTATION OF THE QUASICONTINUUM METHOD
2005
International Journal of Computational Methods
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The intrinsic properties of the material associated with each particle will be sought from the atomic level via the Cauchy-Born rule. ...
Since meshfree particle methods have advantages on simulating the problems involving extremely large deformations, fractures etc., they become attractive options to be used in the hierarchical multiscale ...
Acknowledgments We gratefully acknowledge the startup fund supports from the College of Engineering and Center for Computer-Aided Design at the University of Iowa. ...
doi:10.1142/s0219876205000533
fatcat:k5t5p645e5dvfms3czuc6luftq
Accelerated MD Program Using CUDA Technology
2011
Communications in Physics
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Molecular dynamic (MD) simulation is proven to be an important tool to study the structure as well as the physical properties at atomic level in materials science. ...
The calculation shows that the computing time depends on the size of system and could be decreased by 37 times. ...
INTRODUCTION Molecular dynamics (MD) simulation is one of the important tools widely used to study the structure as well as the dynamic behavior of materials at atomic level. ...
doi:10.15625/0868-3166/21/2/107
fatcat:flbpv4cnajhepiejun4wr5khya
GPU-accelerated molecular dynamics and free energy methods in Amber18: performance enhancements and new features
2018
Journal of Chemical Information and Modeling
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These methods can be used in conjunction with enhanced sampling techniques such as replica exchange, constant pH molecular dynamics and new 12-6-4 potentials for metal ions. ...
Encapsulated free energy modules The development of molecular simulation software designed for optimal performance on specialized hardware requires customization and careful redesign of the underlying ...
Computational resources were provided by the Office of Advanced Research Computing (OARC) at Rutgers, The State University of New Jersey, the National Institutes of Health under Grant No. ...
doi:10.1021/acs.jcim.8b00462
pmid:30199633
pmcid:PMC6226240
fatcat:g5na3zyn7ja63bhy5wwedsy6cu
Generalized extended Lagrangian Born-Oppenheimer molecular dynamics
2014
Journal of Chemical Physics
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2014 pre-print arXiv:1410.3749v1 fatcat:y7sbj25555bize377enlsgb6aa
Extended Lagrangian Born-Oppenheimer molecular dynamics based on Kohn-Sham density functional theory is generalized in the limit of vanishing self-consistent field optimization prior to the force evaluations ...
The equations of motion are derived directly from the extended Lagrangian under the condition of an adiabatic separation between the nuclear and the electronic degrees of freedom. ...
INTRODUCTION Born-Oppenheimer molecular dynamics simulations, where classical molecular trajectories are propagated by forces that are calculated on-the-fly from the relaxed electronic ground state in ...
doi:10.1063/1.4898803
pmid:25362288
fatcat:bgdqh6l2lfapfk7v3gfwj6qi7q
Jdpd: an open java simulation kernel for molecular fragment dissipative particle dynamics
2018
Journal of Cheminformatics
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Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics with parallelizable force calculation, efficient caching options and fast property calculations. ...
The new kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated "all-in-one" simulation systems. ...
The support of GNWI -Gesellschaft für naturwissenschaftliche Informatik mbH, Oer-Erkenschwick, Germany, is gratefully acknowledged. ...
doi:10.1186/s13321-018-0278-7
pmid:29785513
pmcid:PMC5962482
fatcat:h7h6chnc6rhhdk4ynafoltmgcu
Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
[article]
2019
arXiv
pre-print
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Additionally, a careful analysis of molecular dynamics simulations yields new qualitative insights into dynamics and vibrational spectroscopy of small molecules close to spectroscopic accuracy. ...
Here we present the path for the construction of machine learned molecular force fields by discussing the hierarchical pathway from generating the dataset of reference calculations to the construction ...
DFT) of statistical properties computed from molecular dynamics simulation. ...
arXiv:1909.08565v1
fatcat:ftvp53fusjgxpmf4kni4jkwgly
Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models
2017
Scientific Reports
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2017 pre-print arXiv:1705.09919v1 fatcat:hq4qaofhpnajfi6edpa4buu7ka
of the entire dynamics trajectories. ...
As illustrated with the example of sinapic acids, the estimation error for both ground and excited state is very close, which indicates one could predict the ground and excited state molecular properties ...
Acknowledgements The work is supported by National Natural Science Foundation of China (Nos. 21503249, 21373124), and Huazhong Agricultural University Scientific & Technological Self-innovation ...
doi:10.1038/s41598-017-09347-2
pmid:28821842
pmcid:PMC5562909
fatcat:niwwvvemobbp5jy75uqtxdh5ou
Probing Biomolecular Machines with Graphics Processors
2009
Queue
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Molecular Dynamics. One of the most compelling and successful applications for GPU acceleration has been molecular dynamics simulation, which is dominated by N-body atomic force calculation. ...
HOOMD (Highly Optimized Objectoriented Molecular Dynamics), a recently developed package specializing in molecular dynamics simulations of polymer systems, is unique in that it was designed from the ground ...
doi:10.1145/1626135.1629155
fatcat:ded6hyp5wfdgbhukhfvgsgrnqu
Bayesian machine learning for quantum molecular dynamics
[article]
2019
arXiv
pre-print
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This can be used to inform the design of efficient quantum dynamics calculations. ...
This article discusses applications of Bayesian machine learning for quantum molecular dynamics. ...
Acknowledgments The work of the author is supported by NSERC of Canada. ...
arXiv:1904.03730v2
fatcat:sz65wbkwazfcdgklnun3jymgfa
Probing biomolecular machines with graphics processors
2009
Communications of the ACM
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Molecular Dynamics. One of the most compelling and successful applications for GPU acceleration has been molecular dynamics simulation, which is dominated by N-body atomic force calculation. ...
HOOMD (Highly Optimized Objectoriented Molecular Dynamics), a recently developed package specializing in molecular dynamics simulations of polymer systems, is unique in that it was designed from the ground ...
doi:10.1145/1562764.1562780
fatcat:sdn2bizd5bcovpv3bbtvqurzra
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