Active Learning A Neural Network Model For Gold Clusters \& Bulk From Sparse First Principles Training Data

Abstract

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. Here, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with ~500 total reference calculations. Our NN predictions are within 30 meV/atom and 40 meV/Åof the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments.

References in corpus (11)

• Less is more: sampling chemical space with active learning
• Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
• Density functional study of Au${}_n$ (n=2-20) clusters: lowest-energy structures and electronic properties
• Enhanced piezoelectric response of AlN via CrN alloying
• Tuning the piezoelectric and mechanical properties of the AlN system via alloying with YN and BN
• Two-to-three dimensional transition in neutral gold clusters: the crucial role of van der Waals interactions and temperature
• Describing the diverse geometries of gold from nanoclusters to bulk-- a first-principles based hybrid bond order potential
• Unraveling the Planar-Globular Transition in Gold Nanoclusters through Evolutionary Search
• Ionic vs. van der Waals Layered Materials: Identification and Comparison of Elastic Anisotropy
• Large Piezoelectric Response of van der Waals Layered Solids
• A coarse-grained deep neural network model for liquid water

Cited by in corpus (5)

• Data-driven simulation and characterisation of gold nanoparticle melting
• dPOLY: Deep Learning of Polymer Phases and Phase Transition
• Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
• Learning with Delayed Rewards -- A case study on inverse defect design in 2D materials
• Cascading symmetry constraint during machine learning-enabled structural search for sulfur induced Cu(111)-$(\sqrt{43}\times \sqrt{43})$ surface reconstruction