Machine Learning interatomic potentials (ML-IAP) are currently the most promising Non-empirical IAPs for molecular dynamic (MD) simulations. They use Machine Learning (ML) methods to fit the potential energy surface (PES) with large reference datasets of the atomic configurations and their corresponding properties. Promising near quantum mechanical accuracy while being orders of magnitudes faster than first principle methods, ML-IAPs are the new “hot topic” in material
science research.
Unfortunately, most of the available publications require advanced knowledge about ML methods and IAPs, making them hard to understand for beginners and outsiders. This work serves as a plain introduction, providing all the required knowledge about IAPs, ML, and ML-IAPs from the beginning and giving an overview of the most relevant approaches and concepts for building those
potentials. Exemplary a gaussian approximation potential (GAP) for amorphous carbon is used to simulate the defect induced deformation of carbon nanotubes. Comparing the results with published density-functional tight-binding (DFTB) results and own Empirical IAP MD-simulations shows that publicly available ML-IAP can already be used for simulation, being indeed faster than and
nearly as accurate as first-principle methods.
For the future two main challenges appear: First, the availability of ML-IAPs needs to be improved so that they can be easily used in the established MD codes just as the Empirical IAPs. Second, an accurate characterization of the bonds represented in the reference dataset is needed to assure that a potential is suitable for a special application,
otherwise making it a 'black-box' method.:1 Introduction
2 Molecular Dynamics
2.1 Introduction to Molecular Dynamics
2.2 Interatomic Potentials
2.2.1 Development of PES
3 Machine Learning Methods
3.1 Types of Machine Learning
3.2 Building Machine Learning Models
3.2.1 Preprocessing
3.2.2 Learning
3.2.3 Evaluation
3.2.4 Prediction
4 Machine Learning for Molecular Dynamics Simulation
4.1 Definition
4.2 Machine Learning Potentials
4.2.1 Neural Network Potentials
4.2.2 Gaussian Approximation Potential
4.2.3 Spectral Neighbor Analysis Potential
4.2.4 Moment Tensor Potentials
4.3 Comparison of Machine Learning Potentials
4.4 Machine Learning Concepts
4.4.1 On the fly
4.4.2 De novo Exploration
4.4.3 PES-Learn
5 Simulation of defect induced deformation of CNTs
5.1 Methodology
5.2 Results and Discussion
6 Conclusion and Outlook
6.1 Conclusion
6.2 Outlook
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:35780 |
| Date | 13 November 2019 |
| Creators | Rothe, Tom |
| Contributors | Schuster, Jörg, Teichert, Fabian, Lorenz, Erik E., Technische Universität Chemnitz, Fraunhofer Institut für elektronische Nanosysteme |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/submittedVersion, doc-type:workingPaper, info:eu-repo/semantics/workingPaper, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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