469 research outputs found
Accuracy and transferability of Gaussian approximation potential models for tungsten
We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the Gaussian approximation potential framework, fitted to a database of first-principles density functional theory calculations. We investigate the performance of a sequence of models based on databases of increasing coverage in configuration space and showcase our strategy of choosing representative small unit cells to train models that predict properties observable only using thousands of atoms. The most comprehensive model is then used to calculate properties of the screw dislocation, including its structure, the Peierls barrier and the energetics of the vacancy-dislocation interaction. All software and raw data are available at www.libatoms.org
Near-Optimally Teaching the Crowd to Classify
How should we present training examples to learners to teach them
classification rules? This is a natural problem when training workers for
crowdsourcing labeling tasks, and is also motivated by challenges in
data-driven online education. We propose a natural stochastic model of the
learners, modeling them as randomly switching among hypotheses based on
observed feedback. We then develop STRICT, an efficient algorithm for selecting
examples to teach to workers. Our solution greedily maximizes a submodular
surrogate objective function in order to select examples to show to the
learners. We prove that our strategy is competitive with the optimal teaching
policy. Moreover, for the special case of linear separators, we prove that an
exponential reduction in error probability can be achieved. Our experiments on
simulated workers as well as three real image annotation tasks on Amazon
Mechanical Turk show the effectiveness of our teaching algorithm
Representing atomic environments
Representing atomic neighbourhood environments play an important role in high-throughput materials modelling applications. For example, machine-learning based fitting of potential energy surfaces of atomic systems requires faithful representation of chemical environments. Such representations need to be invariant to rotations and permutations of identical atoms while changing in a continuous and smooth manner with the atomic positions. The author presents a unifying view of different approaches and examine their behaviour in concrete numerical examples. Finally, the author will introduce a new way of measuring similarity of atomic environments, which intends to eliminate the shortcomings of earlier representations
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