AB-SID-iVAR enables Gaussian process active learning for self-induced Boltzmann distributions by closed-form approximation of the target, with high-probability error vanishing guarantees and empirical gains on PES and drug discovery tasks.
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OGPP is a particle flow-matching method using orbit-space canonicalization and geometric paths that achieves lower error and fewer steps than prior approaches on 3D benchmarks.
A replica exchange MCMC algorithm couples constrained and relaxed chains to sample from disconnected implicit manifolds defined by nonlinear constraints.
The paper reviews key computational methods for ultrastable glasses, discusses their efficiency and limitations, and compares the stability levels achieved.
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Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
AB-SID-iVAR enables Gaussian process active learning for self-induced Boltzmann distributions by closed-form approximation of the target, with high-probability error vanishing guarantees and empirical gains on PES and drug discovery tasks.
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Generative Modeling with Orbit-Space Particle Flow Matching
OGPP is a particle flow-matching method using orbit-space canonicalization and geometric paths that achieves lower error and fewer steps than prior approaches on 3D benchmarks.
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A Replica Exchange Markov Chain Monte Carlo Method for Disconnected Implicit Manifolds via Tubular Relaxation
A replica exchange MCMC algorithm couples constrained and relaxed chains to sample from disconnected implicit manifolds defined by nonlinear constraints.
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Computational Methods towards Ultrastable Glasses
The paper reviews key computational methods for ultrastable glasses, discusses their efficiency and limitations, and compares the stability levels achieved.