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Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems
Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems
Summary
Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems is a scholarly article[1].
Key Facts
Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems's instance of is recorded as scholarly article[2].
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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.
APA4ort.xyz Knowledge Graph. (2026). Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems. Retrieved May 24, 2026, from https://4ort.xyz/entity/sparse-gaussian-process-based-machine-learning-first-principles-potentials-for-materials-simulations-application-to-batt
MLA“Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/sparse-gaussian-process-based-machine-learning-first-principles-potentials-for-materials-simulations-application-to-batt.
BibTeX@misc{4ortxyz_sparse-gaussian-process-based-machine-learning-first-principles-potentials-for-materials-simulations-application-to-batt_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems}}, year = {2026}, url = {https://4ort.xyz/entity/sparse-gaussian-process-based-machine-learning-first-principles-potentials-for-materials-simulations-application-to-batt}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems — https://4ort.xyz/entity/sparse-gaussian-process-based-machine-learning-first-principles-potentials-for-materials-simulations-application-to-batt (retrieved 2026-05-24)
