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Found 5 papers, 1 papers with code
Bayesian inference of composition-dependent phase diagrams
Phase diagrams serve as a highly informative tool for materials design, encapsulating information about the phases that a material can manifest under specific conditions.
Accurate melting point prediction through autonomous physics-informed learning
We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble.
Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe
The accuracy is achieved by a two-step minimization scheme that coarse-grains the atomic and the spin space.
Atomic Physics
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning
Active learning methods for neural networks are usually based on greedy criteria which ultimately give a single new design point for the evaluation.
Dropout-based Active Learning for Regression
Active learning is relevant and challenging for high-dimensional regression models when the annotation of the samples is expensive.
