What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Molecule
- Thermodynamics
Peter G. Bolhuis focuses on Transition path sampling, Statistical physics, Chemical physics, Path and Colloid.
His study on Transition path sampling is covered under Computational chemistry.
Peter G. Bolhuis interconnects Complex system and Computation in the investigation of issues within Statistical physics.
His research investigates the connection between Complex system and topics such as Importance sampling that intersect with issues in Statistical mechanics, Nanotechnology, Mechanical equilibrium and Trajectory.
His Chemical physics research integrates issues from Crystal, Crystal structure and Nucleation.
His Colloid study also includes fields such as
- Range and related Particle, Scaling, Scaling limit, Renormalization group and Pair potential,
- Excluded volume together with Thermodynamics.
His most cited work include:
- TRANSITION PATH SAMPLING: Throwing Ropes Over Rough Mountain Passes, in the Dark (1370 citations)
- Transition path sampling and the calculation of rate constants (768 citations)
- Tracing the phase boundaries of hard spherocylinders (558 citations)
What are the main themes of his work throughout his whole career to date?
Peter G. Bolhuis mostly deals with Transition path sampling, Chemical physics, Statistical physics, Molecular dynamics and Colloid.
His studies in Transition path sampling integrate themes in fields like Complex system and Reaction coordinate.
Peter G. Bolhuis has included themes like Range, Nucleation, Molecule, Dissociation and Anisotropy in his Chemical physics study.
His Statistical physics course of study focuses on Trajectory and Statistical mechanics.
Peter G. Bolhuis works mostly in the field of Molecular dynamics, limiting it down to topics relating to Protein folding and, in certain cases, Folding.
The concepts of his Colloid study are interwoven with issues in Nanotechnology, Condensed matter physics and Hard spheres, Thermodynamics.
He most often published in these fields:
- Transition path sampling (31.77%)
- Chemical physics (29.69%)
- Statistical physics (28.65%)
What were the highlights of his more recent work (between 2018-2021)?
- Transition path sampling (31.77%)
- Reaction coordinate (11.46%)
- Chemical physics (29.69%)
In recent papers he was focusing on the following fields of study:
Transition path sampling, Reaction coordinate, Chemical physics, Nucleation and Crystallization are his primary areas of study.
Transition path sampling is the subject of his research, which falls under Molecular dynamics.
His Molecular dynamics research includes elements of Native state, Statistical mechanics, Statistical physics and Trajectory.
His work carried out in the field of Statistical physics brings together such families of science as Sampling methodology and Brownian motion.
His studies deal with areas such as Protein structure, Globular protein, Dissociation and Native contact as well as Reaction coordinate.
His research on Nucleation also deals with topics like
- Supercooling which intersects with area such as Classical nucleation theory and Amorphous solid,
- Force field and Phase most often made with reference to Supersaturation.
Between 2018 and 2021, his most popular works were:
- Promoting transparency and reproducibility in enhanced molecular simulations (145 citations)
- Allostery in Its Many Disguises: From Theory to Applications (97 citations)
- OpenPathSampling: A Python Framework for Path Sampling Simulations. 1. Basics (18 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Molecule
- Thermodynamics
Peter G. Bolhuis mainly focuses on Transition path sampling, Gibbs sampling, Replica, Python and Transparency.
The subject of his Transition path sampling research is within the realm of Molecular dynamics.
His Gibbs sampling study integrates concerns from other disciplines, such as Complex system, Theoretical computer science and Molecular systems.
His Replica study overlaps with Computational science, Multiset and Importance sampling.
His Transparency study spans across into fields like Systems engineering and Reproducibility.
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