A Gaussian mixture model is used to learn spectral densities from 2DES experiments, enabling extraction of vibronic couplings, spectral extrapolation, and optimized experiment selection across simulated and experimental systems.
InvDesFlow-AL: active learning- based workflow for inverse design of functional materials
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Machine learning models that respect material symmetries are accelerating the identification of topological phases and the discovery of d-wave, g-wave, and i-wave altermagnets in quantum materials.
A survey of generative crystal modeling, multimodal learning, and closed-loop inverse design pipelines for crystalline solids, including failure modes and evaluation practices.
citing papers explorer
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Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning
A Gaussian mixture model is used to learn spectral densities from 2DES experiments, enabling extraction of vibronic couplings, spectral extrapolation, and optimized experiment selection across simulated and experimental systems.
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Machine Learning and Deep Learning in Quantum Materials: Symmetry, Topology, and the Rise of Altermagnets
Machine learning models that respect material symmetries are accelerating the identification of topological phases and the discovery of d-wave, g-wave, and i-wave altermagnets in quantum materials.
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Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
A survey of generative crystal modeling, multimodal learning, and closed-loop inverse design pipelines for crystalline solids, including failure modes and evaluation practices.