Monte Carlo Refinement is a computational optimization method that belongs to the class of stochastic sampling techniques, distinguished by its use of random perturbations and probabilistic acceptance criteria to iteratively improve structural models in relation to experimental or target data.
35
Publications
1.4K
Citations
111
Authors
55
Institutions
Publications per year
2017–2026
7
Parents
Authors
111
- AL
4
h-index
- MT
3
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- MG
2
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- YY
2
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- IO
2
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1–5 of 111
Institutions
55
University of Oxford
Oxford, United Kingdom
5 h-index
11 publications
537 citations
Rutherford Appleton Laboratory
Didcot, United Kingdom
4 h-index
4 publications
275 citations
Institut Laue-Langevin
Grenoble, France
2 h-index
3 publications
60 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
2 publications
27 citations
University of Cambridge
Cambridge, United Kingdom
2 h-index
7 publications
499 citations
Stony Brook University
Stony Brook, United States
2 h-index
4 publications
588 citations
Queen Mary University of London
London, United Kingdom
2 h-index
4 publications
82 citations
Oak Ridge National Laboratory
Oak Ridge, United States
2 h-index
1 publications
21 citations
1–8 of 55
Venues
- Chemistry of Materials
2 publications · 234 citations
2
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- Journal of Applied Crystallography
1 publications · 22 citations
2
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- The Journal of Chemical Physics
2 publications · 157 citations
2
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- Journal of the American Chemical Society
JACS · 2 publications · 88 citations
2
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- Journal of Physics Condensed Matter
2 publications · 58 citations
2
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