mlsanityData to 14 Oct 2026

Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction

arXiv 2302.01486 ↗First posted 3 Feb 2023cs.LGcited by 4

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Abstract (arXiv)

Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property predictions, while more challenging spectral properties remain less emphasized. We formulate a crystal-to-sequence learning task and propose a novel attention-based learning method, Xtal2DoS, which decodes the sequential representation of the material density of states (DoS) properties by incorporating the learned atomic embeddings through attention networks. Experiments show Xtal2DoS is faster than the existing models, and consistently outperforms other state-of-the-art methods on four metrics for two fundamental spectral properties, phonon and electronic DoS.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 21 of its 47 references that are in the map. First posted on arXiv 3 Feb 2023.

Builds on

21 of 47 references.

References · 21

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9 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2023M2Hub: Unlocking the Potential of Machine Learning for Materials Discoverycited by 8
  2. 2024CrysToGraph: A Comprehensive Predictive Model for Crystal Materials Properties and the Benchmarkcited by 3
  3. 2024Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletabilitycited by 1
  4. 2024Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approachcited by 1