Abstract
Interatomic potential models based on machine learning (ML) are rapidly developing as tools for material simulations. However, because of their flexibility, they require large fitting databases that are normally created with substantial manual selection and tuning of reference configurations. Here, we show that ML potentials can be built in a largely automated fashion, exploring and fitting potential-energy surfaces from the beginning (de novo) within one and the same protocol. The key enabling step is the use of a configuration-averaged kernel metric that allows one to select the few most relevant and diverse structures at each step. The resulting potentials are accurate and robust for the wide range of configurations that occur during structure searching, despite only requiring a relatively small number of single-point DFT calculations on small unit cells. We apply the method to materials with diverse chemical nature and coordination environments, marking an important step toward the more routine application of ML potentials in physics, chemistry, and materials science.
Highlights
Atomic-scale modeling has become a cornerstone of scientific research
We have previously shown that the potential-energy surface (PES) of boron can be iteratively sampled without prior knowledge of any crystal structure involved; we called the method “Gaussian approximation potential (GAP)-driven random structure searching” (GAP-RSS),[18] reminiscent of the successful ab initio random structure-searching (AIRSS) approach.[41,42]
The overarching aim is to construct a Machine learning (ML) potential with minimal effort, both in terms of computational resources and in terms of input required from the user
Summary
Quantum-mechanical methods, most prominently based on density-functional theory (DFT), describe the atomistic structures and physical properties of materials with high confidence;[1] increasingly, they make it possible to discover previously unknown crystal structures and synthesis targets.[2] Still, quantum-mechanical material simulations are severely limited by their high computational cost. ML potentials enable accurate simulations that are orders of magnitude faster than the reference method. They can solve challenging structural problems, as has been demonstrated for the atomic-scale deposition and growth of amorphous carbon films,[13] for proton-transfer mechanisms,[14] or dislocations in materials,[15,16] involving thousands of atoms in the simulation. It was shown that ML potentials can be suitable tools for global structure searches targeting crystalline phases,[17,18,19,20] clusters,[21,22,23,24] and nanostructures.[25]
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