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arXiv Machine Learning By Andr\'es Guzm\'an-Cordero, Cindy Zhang, Majdi Hassan, Marta Skreta, Kirill Neklyudov, Matija Medvidovi\'c

Scaling Density Functional Theory with Gaussian Splatting

Read the original on arXiv Machine Learning →

The paper introduces Gaussian Splatting for Density Functional Theory (GS‑DFT), a method that represents molecular orbitals as a cloud of Gaussians optimized via gradient descent. GS‑DFT replaces fixed atom‑centered basis sets with an adaptive, differentiable orthogonalization and efficient two‑electron integral evaluation, achieving accuracy comparable to large conventional bases with far fewer parameters. The solver scales quadratically with cloud size, enabling simulations of up to 2,742 atoms on a single four‑GPU node at triple‑zeta precision.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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