# A novel approach to describe chemical environments in high-dimensional neural network potentials.

@article{Kocer2019ANA, title={A novel approach to describe chemical environments in high-dimensional neural network potentials.}, author={Emir Kocer and Jeremy K. Mason and Hakan Erturk}, journal={The Journal of chemical physics}, year={2019}, volume={150 15}, pages={ 154102 } }

A central concern of molecular dynamics simulations is the potential energy surfaces that govern atomic interactions. These hypersurfaces define the potential energy of the system and have generally been calculated using either predefined analytical formulas (classical) or quantum mechanical simulations (ab initio). The former can accurately reproduce only a selection of material properties, whereas the latter is restricted to short simulation times and small systems. Machine learning… Expand

#### 10 Citations

Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials.

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- 2020

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Continuous and optimally complete description of chemical environments using Spherical Bessel descriptors

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- Journal of chemical theory and computation
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Abstract Quantum mechanics (QM) approaches (DFT, MP2, CCSD(T), etc.) play an important role in calculating molecules and crystals with a high accuracy and acceptable efficiency. In recent years, with… Expand

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The bin-and-hash (BAH) algorithm is presented, which is general and can be combined with any current type of MLP, to enable the efficient identification and comparison of large numbers of multidimensional vectors. Expand

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A critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design are reviewed. Expand

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Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce su...

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This work compares the performance of fingerprints based on the overlap matrix, the smooth overlap of atomic positions, Behler–Parrinello atom-centered symmetry functions, modified Behler-Parrinllo symmetry functions used in the ANI-1ccx potential and the Faber–Christensen–Huang–Lilienfeld fingerprint under various aspects. Expand

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