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- Robert D. Skeel

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
40
Citations
27,385
110
World Ranking
1327
National Ranking
602

2011 - SIAM Fellow For contributions to computational molecular biophysics and to numerical ordinary differential equations and linear algebra.

- Quantum mechanics
- Mathematical analysis
- Algebra

Robert D. Skeel mainly focuses on Molecular dynamics, Discretization, Mathematical analysis, Computational science and Impulse. His Molecular dynamics research integrates issues from Tree, Load balancing, Statistical physics and Parallel computing. The concepts of his Mathematical analysis study are interwoven with issues in Verlet integration and Dynamical systems theory.

The Dynamical systems theory study combines topics in areas such as Numerical integration, Applied mathematics, Algorithm and Differential equation. His Impulse research is multidisciplinary, incorporating elements of Stochastic process, Langevin equation, Computational chemistry, Newtonian fluid and Autocorrelation. He integrates several fields in his works, including Molecular graphics, Multipole expansion, Object-oriented programming, Source code, Scripting language and Software.

- Scalable molecular dynamics with NAMD (11850 citations)
- NAMD2: Greater Scalability for Parallel Molecular Dynamics (1962 citations)
- Langevin stabilization of molecular dynamics (532 citations)

His primary scientific interests are in Applied mathematics, Mathematical analysis, Statistical physics, Molecular dynamics and Symplectic integrator. His work deals with themes such as Verlet integration, Mathematical optimization, Ordinary differential equation and Hybrid Monte Carlo, which intersect with Applied mathematics. Robert D. Skeel combines subjects such as Path, Computation and Markov chain with his study of Statistical physics.

His Molecular dynamics research focuses on subjects like Classical mechanics, which are linked to Brownian dynamics. His research in the fields of Leapfrog integration overlaps with other disciplines such as Symplectic geometry, Hamiltonian system and Numerical integration. His research in Discretization tackles topics such as Nonlinear system which are related to areas like Equations of motion, Impulse and Algorithm.

- Applied mathematics (31.25%)
- Mathematical analysis (22.32%)
- Statistical physics (18.75%)

- Statistical physics (18.75%)
- Hybrid Monte Carlo (5.36%)
- Applied mathematics (31.25%)

His primary areas of study are Statistical physics, Hybrid Monte Carlo, Applied mathematics, Markov chain Monte Carlo and Monte Carlo method. His biological study spans a wide range of topics, including Transition rate matrix, Path, Computation and Markov chain. His Applied mathematics research integrates issues from Function, Potential energy, Classical mechanics and Holonomic.

His Markov chain Monte Carlo study incorporates themes from Numerical analysis and Autocorrelation. Integral equation is a primary field of his research addressed under Mathematical analysis. The various areas that Robert D. Skeel examines in his Mathematical analysis study include Finite element method and Nonlinear system.

- Bayesian Sampling Using Stochastic Gradient Thermostats (154 citations)
- Compressible generalized hybrid Monte Carlo (36 citations)
- Multilevel summation method for electrostatic force evaluation. (36 citations)

- Quantum mechanics
- Mathematical analysis
- Algebra

Robert D. Skeel mostly deals with Applied mathematics, Hybrid Monte Carlo, Monte Carlo method, Statistical physics and Symplectic geometry. His study in Applied mathematics is interdisciplinary in nature, drawing from both Probability distribution, Phase space, Markov chain Monte Carlo, Stochastic process and Configuration space. His Hybrid Monte Carlo research includes elements of Discretization, Sampling, Numerical analysis and Mathematical optimization.

The Statistical physics study combines topics in areas such as Monte Carlo method in statistical physics, Path, Point and Invariant. His research on Symplectic geometry concerns the broader Mathematical analysis. His Energy conservation study spans across into fields like Symplectic integrator and Hamiltonian.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Scalable molecular dynamics with NAMD

James C. Phillips;Rosemary Braun;Wei Wang;James C. Gumbart.

Journal of Computational Chemistry **(2005)**

17100 Citations

NAMD2: Greater Scalability for Parallel Molecular Dynamics

Laxmikant Kalé;Robert Skeel;Milind Bhandarkar;Robert Brunner.

Journal of Computational Physics **(1999)**

2870 Citations

Langevin stabilization of molecular dynamics

Jesús A. Izaguirre;Daniel P. Catarello;Justin M. Wozniak;Robert D. Skeel.

Journal of Chemical Physics **(2001)**

869 Citations

Scalable molecular dynamics on CPU and GPU architectures with NAMD.

James C. Phillips;David J. Hardy;Julio D.C. Maia;John E. Stone.

Journal of Chemical Physics **(2020)**

763 Citations

NAMD: a Parallel, Object-Oriented Molecular Dynamics Program

Mark T. Nelson;William Humphrey;Attila Gursoy;Andrew Dalke.

ieee international conference on high performance computing data and analytics **(1996)**

689 Citations

A Method for the Spatial Discretization of Parabolic Equations in One Space Variable.

Robert D. Skeel;Martin Berzins.

SIAM Journal on Scientific Computing **(1990)**

446 Citations

Symplectic Numerical Integrators in Constrained Hamiltonian Systems

Benedict J. Leimkuhler;Robert D. Skeel.

Journal of Computational Physics **(1994)**

306 Citations

Long-Time-Step Methods for Oscillatory Differential Equations

B. García-Archilla;J. M. Sanz-Serna;R. D. Skeel.

SIAM Journal on Scientific Computing **(1999)**

284 Citations

Algorithmic Challenges in Computational Molecular Biophysics

Tamar Schlick;Robert D Skeel;Axel T Brunger;Laxmikant V Kalé.

Journal of Computational Physics **(1999)**

259 Citations

Computational Molecular Dynamics: Challenges, Methods, Ideas

Peter Deuflhard;Jan Hermans;Benedict Leimkuhler;Alan E. Mark.

**(1999)**

232 Citations

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