The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Amp: A modular approach to machine learning in atomistic simulations
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Citations
Cites
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
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572
Competition between CO 2 Reduction and H 2 Evolution on Transition-Metal Electrocatalysts
Zhang, Y.-J., Sethuraman, V., Michalsky, R., & Peterson, A. A. (2014) · ACS Catalysis · doi:10.1021/cs5012298 · Cited by 509
Self-citation
Cited by
The atomic simulation environment—a Python library for working with atoms
Hjorth Larsen, A., Jørgen Mortensen, J., Blomqvist, J., Castelli, I. E., Christensen, R., Dułak, M., Friis, J., Groves, M. N., Hammer, B., Hargus, C., et al. (2017) · Journal of Physics: Condensed Matter · doi:10.1088/1361-648x/aa680e · Cited by 4,464
Self-citationUnke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
“org/andrewpeterson/amp [98] TensorMol Mol https://github.”
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
Unke, O. T., & Meuwly, M. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00181 · Cited by 1,073
Four Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 873
Metadata
- Authors
- Khorshidi, A., & Peterson, A. A.
- Venue
- Computer Physics Communications
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 207
- Pages
- 310-324
- ISSN
- 0010-4655 (ISSN-L)
- OpenAlex
- W2410722695
- Cited by
- 360
- References
- 6
- FWCI
- 14.41
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Khorshidi, A., & Peterson, A. A. (2016). Amp: A modular approach to machine learning in atomistic simulations. Computer Physics Communications. https://doi.org/10.1016/j.cpc.2016.05.010
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.
