Home › Entities › academia › Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information

Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information

Research article (ACS Omega, 2021) · cited 45× · AI/ML
academia openalex:W3164305566
Press Enter · cited answer in seconds

Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information

Summary

Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information is a scholarly article[1].

Key Facts

  • Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information. Retrieved May 24, 2026, from https://4ort.xyz/entity/prediction-and-classification-of-formation-energies-of-binary-compounds-by-machine-learning-an-approach-without-crystal-
MLA “Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/prediction-and-classification-of-formation-energies-of-binary-compounds-by-machine-learning-an-approach-without-crystal-.
BibTeX @misc{4ortxyz_prediction-and-classification-of-formation-energies-of-binary-compounds-by-machine-learning-an-approach-without-crystal-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information}}, year = {2026}, url = {https://4ort.xyz/entity/prediction-and-classification-of-formation-energies-of-binary-compounds-by-machine-learning-an-approach-without-crystal-}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information — https://4ort.xyz/entity/prediction-and-classification-of-formation-energies-of-binary-compounds-by-machine-learning-an-approach-without-crystal- (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/prediction-and-classification-of-formation-energies-of-binary-compounds-by-machine-learning-an-approach-without-crystal- · Last refreshed: