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Abstract Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both ab initio calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.
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This content will become publicly available on May 14, 2026
Bubbling and mixing of vibrated and non-vibrated gas-fluidized active granular matter
Numerical simulations reveal that the change in transport regimes for fluidized active granular materials is dependent on the balance of drag force, gravitational force, and active force.
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- Award ID(s):
- 2144763
- PAR ID:
- 10597975
- Publisher / Repository:
- Soft Matter
- Date Published:
- Journal Name:
- Soft Matter
- Volume:
- 21
- Issue:
- 19
- ISSN:
- 1744-683X
- Page Range / eLocation ID:
- 3899 to 3909
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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