# 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

#### 11 Citations

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

- Physics, Mathematics
- Journal of chemical theory and computation
- 2020

This work proposes a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks and proposes an extendable invariant local molecular descriptor constructed from geometric moments. Expand

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

- Physics, Computer Science
- AIP Advances
- 2020

The Spherical Bessel descriptors have the advantage of allowing machine learning potentials of comparable accuracy that require roughly an order of magnitude less computation time per evaluation than the Smooth Overlap of Atomic Position descriptors, which appear to be the common choice of descriptors in recent studies. Expand

Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments.

- Medicine, Physics
- Journal of chemical theory and computation
- 2021

An improved NN architecture based on the previous GM-NN model is presented, which shows an improved prediction accuracy and considerably reduced training times and extends the applicability of Gaussian moment-based interatomic potentials to periodic systems and demonstrates the overall excellent transferability and robustness of the respective models. Expand

Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations

- Physics
- Machine Learning: Science and Technology
- 2021

Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional… Expand

Choosing the right molecular machine learning potential

- Computer Science
- 2021

This work evaluates the performance of popular machine learning potentials in terms of accuracy and computational cost, and delivers structured information for non-specialists in machine learning to guide them through the maze of acronyms, recognize each potential's main features, and judge what they could expect from each one. Expand

Machine learning accelerates quantum mechanics predictions of molecular crystals

- Physics
- Physics Reports
- 2021

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

A bin and hash method for analyzing reference data and descriptors in machine learning potentials

- Computer Science, Physics
- Mach. Learn. Sci. Technol.
- 2021

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

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

- Physics, Chemistry
- Chemical reviews
- 2021

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

Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution

- Materials Science
- 2020

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...

Choosing the right molecular machine learning potential

- Computer Science
- Chemical Science
- 2021

This article provides a lifeline for those lost in the sea of the molecular machine learning potentials by providing a balanced overview and evaluation of popular potentials.

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