What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Operating system
- Thermodynamics
His primary areas of investigation include Molecular dynamics, Statistical physics, Condensed matter physics, Dynamical heterogeneity and Parallel computing.
His Molecular dynamics research integrates issues from Molecular physics, Length scale and Nanotechnology.
The Statistical physics study combines topics in areas such as Algorithm, Continuum mechanics, Computational mechanics and Continuum.
The concepts of his Condensed matter physics study are interwoven with issues in Indentation, Supercooling, Centrosymmetry and Contact area.
His Supercooling research is multidisciplinary, incorporating elements of Correlation function, Lennard-Jones potential and Exponential function.
His Dynamical heterogeneity research is multidisciplinary, incorporating perspectives in Particle and Relaxation.
His most cited work include:
- Dislocation nucleation and defect structure during surface indentation (1511 citations)
- DYNAMICAL HETEROGENEITIES IN A SUPERCOOLED LENNARD-JONES LIQUID (646 citations)
- STRINGLIKE COOPERATIVE MOTION IN A SUPERCOOLED LIQUID (641 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Molecular dynamics, Statistical physics, Computational science, Parallel computing and Mechanics.
Steven J. Plimpton does research in Molecular dynamics, focusing on Lennard-Jones potential specifically.
His Statistical physics research includes elements of Discretization and Peridynamics.
His work carried out in the field of Computational science brings together such families of science as Interface, Parallel processing and Massively parallel.
The various areas that Steven J. Plimpton examines in his Parallel computing study include Computation and Connected component.
His Mechanics study combines topics from a wide range of disciplines, such as Slip and Classical mechanics.
He most often published in these fields:
- Molecular dynamics (20.25%)
- Statistical physics (15.34%)
- Computational science (15.34%)
What were the highlights of his more recent work (between 2014-2021)?
- Mechanics (11.04%)
- Direct simulation Monte Carlo (7.98%)
- Neuromorphic engineering (4.29%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Mechanics, Direct simulation Monte Carlo, Neuromorphic engineering, Computational science and Resistive random-access memory.
Steven J. Plimpton combines subjects such as Dissipative system and Degrees of freedom with his study of Mechanics.
His work focuses on many connections between Neuromorphic engineering and other disciplines, such as Energy, that overlap with his field of interest in Backpropagation, Memory management, Noise, Electrical engineering and Analog computer.
His Computational science research is multidisciplinary, relying on both Scalability and Supercomputer.
Steven J. Plimpton has researched Statistical physics in several fields, including Parallel algorithm, Interatomic potential, Molecular dynamics and Discrete particle.
His Molecular dynamics research incorporates elements of Renormalization, Boundary value problem, Cluster analysis, Classical mechanics and Speedup.
Between 2014 and 2021, his most popular works were:
- Li‐Ion Synaptic Transistor for Low Power Analog Computing (178 citations)
- Resistive memory device requirements for a neural algorithm accelerator (66 citations)
- Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator (49 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Operating system
- Thermodynamics
Direct simulation Monte Carlo, Algorithm, Energy, Artificial neural network and Neuromorphic engineering are his primary areas of study.
His Direct simulation Monte Carlo research incorporates elements of Monatomic gas, Statistical physics, Continuum, Grid cell and Fundamental physics.
His research in Statistical physics intersects with topics in Perturbation, Shock wave, Richtmyer–Meshkov instability, Instability and Mach number.
He integrates several fields in his works, including Algorithm and Scaling.
His Energy research incorporates themes from Power, Linearity, Battery and Analog computer.
His research integrates issues of Ideal, Matrix multiplication, Electrical engineering and Computer data storage in his study of Neuromorphic engineering.
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