---
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id: "411c66dec517a32dcc1770eaa8e56480ed599057c05d006e27010a8ae316124d"
canonical_url: "https://aidr.today/411c66de?lang=en"
title: "Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials"
lang: "en"
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available_langs: ["en","vi"]
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published_at: "2026-10-29T15:00:00.000Z"
category: "Chips"
topics: ["nvidia","inference"]
source_urls: ["https://developer.nvidia.com/blog/unlocking-tensor-core-performance-for-machine-learning-interatomic-potentials/"]
summary: "Machine learning interatomic potentials (MLIPs) use neural networks to predict potential energies and interatomic forces at a fraction of the cost, enabling researchers to study larger systems and longer timescales while retaining much of the required accuracy."
---

# Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials

> [Open the canonical story](<https://aidr.today/411c66de?lang=en>)

**Published:** 2026-10-29T15:00:00.000Z
**Category:** Chips
**Topics:** nvidia, inference

## Summary

Machine learning interatomic potentials \(MLIPs\) use neural networks to predict potential energies and interatomic forces at a fraction of the cost, enabling researchers to study larger systems and longer timescales while retaining much of the required accuracy\.

## Sources

- [Story source](<https://developer.nvidia.com/blog/unlocking-tensor-core-performance-for-machine-learning-interatomic-potentials/>)