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Article summary:
1. Ab initio molecular dynamics simulations are used to study a variety of chemical processes, but can be computationally expensive.
2. Artificial neural networks (NNs) have been used to construct potential-energy surfaces (PESs) for more than a decade and are very flexible functions that can adapt to a known set of reference points in an iterative fitting process.
3. This article discusses an approach to construct high-dimensional NN PESs based on an expansion of the total energy in terms of environment-dependent atomic energy contributions, using symmetry functions to describe the chemical environments of the atoms.
Article analysis:
The article is generally reliable and trustworthy, as it provides detailed information about the use of artificial neural networks (NNs) for constructing potential-energy surfaces (PESs). The authors provide a clear explanation of how NNs work and how they can be used to accurately fit data from ab initio molecular dynamics simulations. The authors also discuss their approach for constructing high-dimensional NN PESs based on an expansion of the total energy in terms of environment-dependent atomic energy contributions, using symmetry functions to describe the chemical environments of the atoms.
The article does not appear to contain any biases or unsupported claims, as all claims are supported by references and evidence from previous studies. Additionally, all possible risks associated with using NNs for constructing PESs are noted and discussed in detail. The article also presents both sides equally, providing both advantages and disadvantages associated with using NNs for constructing PESs.
In conclusion, this article is reliable and trustworthy, providing detailed information about the use of artificial neural networks for constructing potential-energy surfaces.