The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
An overview of spatial microscopic and accelerated kinetic Monte Carlo methods
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Citations
Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Adhi Harmoko, S., M.Komp, Jacquard, J. M., Zuse, K., & Eniac (2005) · Cited by 18,035
Computer Simulation of Liquids
P, S., & J, K. (1988) · Journal of Molecular Liquids · doi:10.1016/0167-7322(88)80022-9 · Cited by 15,812
Savitzky-Golay Smoothing Filters
Press, W. H., & Teukolsky, S. A. (1990) · Computers in Physics · doi:10.1063/1.4822961 · Cited by 11,670
Unified Approach for Molecular Dynamics and Density-Functional Theory
Car, R., & Parrinello, M. (1985) · Physical Review Letters · doi:10.1103/physrevlett.55.2471 · Cited by 10,754
Exact stochastic simulation of coupled chemical reactions
Gillespie, D. T. (1977) · The Journal of Physical Chemistry · doi:10.1021/j100540a008 · Cited by 10,136
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
Daw, M. S., & Baskes, M. I. (1984) · Physical Review B · doi:10.1103/physrevb.29.6443 · Cited by 7,263
Cited by
Computational Methods in Heterogeneous Catalysis
Chen, B. W. J., Xu, L., & Mavrikakis, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01060 · Cited by 447
On the inner workings of Monte Carlo codes
Dubbeldam, D., Torres-Knoop, A., & Walton, K. S. (2013) · Molecular Simulation · doi:10.1080/08927022.2013.819102 · Cited by 422
Adaptive machine learning framework to accelerate ab initio molecular dynamics
Botu, V., & Ramprasad, R. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24836 · Cited by 395
Salciccioli, M., Stamatakis, M., Caratzoulas, S., & Vlachos, D. G. (2011) · Chemical Engineering Science · doi:10.1016/j.ces.2011.05.050 · Cited by 366
Self-citationMatera, S., Schneider, W. F., Heyden, A., & Savara, A. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b01234 · Cited by 222
Metadata
- Authors
- Chatterjee, A., & Vlachos, D. G.
- Venue
- Journal of Computer-Aided Materials Design
- Published
- 2007-02-28
- Volume
- 14
- Issue
- 2
- Pages
- 253-308
- ISSN
- 0928-1045 (ISSN-L)
- 1573-4900
- OpenAlex
- W1973641880
- Cited by
- 464
- References
- 95
- FWCI
- 18.30
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chatterjee, A., & Vlachos, D. G. (2007). An overview of spatial microscopic and accelerated kinetic Monte Carlo methods. Journal of Computer-Aided Materials Design. https://doi.org/10.1007/s10820-006-9042-9
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
