The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Discovering chemistry with an ab initio nanoreactor
Open accessAbstract
Chemical understanding is driven by the experimental discovery of new compounds and reactivity, and is supported by theory and computation that provides detailed physical insight. While theoretical and computational studies have generally focused on specific processes or mechanistic hypotheses, recent methodological and computational advances harken the advent of their principal role in discovery. Here we report the development and application of the ab initio nanoreactor – a highly accelerated, first-principles molecular dynamics simulation of chemical reactions that discovers new molecules and mechanisms without preordained reaction coordinates or elementary steps. Using the nanoreactor we show new pathways for glycine synthesis from primitive compounds proposed to exist on the early Earth, providing new insight into the classic Urey-Miller experiment. These results highlight the emergence of theoretical and computational chemistry as a tool for discovery in addition to its traditional role of interpreting experimental findings.
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Cites
Density-functional thermochemistry. III. The role of exact exchange
Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464913 · Cited by 103,767
Kasting, J. F. (1993) · Science · doi:10.1126/science.11536547 · Cited by 1,374
A Reassessment of Prebiotic Organic Synthesis in Neutral Planetary Atmospheres
Cleaves, H. J., Chalmers, J. H., Lazcano, A., Miller, S. L., & Bada, J. L. (2008) · Origins of Life and Evolution of Biospheres · doi:10.1007/s11084-007-9120-3 · Cited by 313
Cited by
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911
Deep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683
Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
Liu, B., Ramsundar, B., Kawthekar, P., Shi, J., Gomes, J., Luu Nguyen, Q., Ho, S., Sloane, J., Wender, P., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00303 · Cited by 536
Self-citationNandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358
Self-citationDöntgen, M., Przybylski-Freund, M.-D., Kröger, L. C., Kopp, W. A., Ismail, A. E., & Leonhard, K. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00201 · Cited by 308
Metadata
- Authors
- Wang, L.-P., Titov, A., McGibbon, R., Liu, F., Pande, V. S., & Martínez, T. J.
- Venue
- Nature Chemistry
- Published
- 2014-11-02
- Volume
- 6
- Issue
- 12
- Pages
- 1044-1048
- ISSN
- 1755-4330 (ISSN-L)
- 1755-4349
- OpenAlex
- W2060526630
- Cited by
- 422
- References
- 3
- FWCI
- 11.82
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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