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Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods

Research article (The Journal of Chemical Physics, 2022) · cited 51× · AI/ML
academia openalex:W4200635768
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Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods

Summary

Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods is a scholarly article[1].

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  • Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods's instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods. Retrieved May 24, 2026, from https://4ort.xyz/entity/permutationally-invariant-polynomial-regression-for-energies-and-gradients-using-reverse-differentiation-achieves-orders
MLA “Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/permutationally-invariant-polynomial-regression-for-energies-and-gradients-using-reverse-differentiation-achieves-orders.
BibTeX @misc{4ortxyz_permutationally-invariant-polynomial-regression-for-energies-and-gradients-using-reverse-differentiation-achieves-orders_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods}}, year = {2026}, url = {https://4ort.xyz/entity/permutationally-invariant-polynomial-regression-for-energies-and-gradients-using-reverse-differentiation-achieves-orders}, note = {Accessed: 2026-05-24}}
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