Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials
<img width="768" height="432" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-768x432.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="Colorful matrix-like blocks transition into a lattice of spheres, illustrating computation for atomistic simulation." style="display: block; margin-bottom: 5px; clear:both;max-width: 100%;" link_thumbnail="" decoding="async" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-768x432.jpg 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-179x101.jpg 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-300x169.jpg 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-625x352.jpg 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-1536x864.jpg 1536w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-645x363.jpg 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-660x370.jpg 660w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-500x281.jpg 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-160x90.jpg 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-362x204.jpg 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-196x110.jpg 196w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-1024x576.jpg 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-960x540.jpg 960w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured.webp 2048w" sizes="(max-width: 768px) 100vw, 768px" title="Tensor Core computation for machine learning interatomic potentials" />Atomistic simulations help researchers understand how molecules and materials behave, from chemical reactions and phase transitions to mechanical failure. These...<img width="768" height="432" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-768x432.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="Colorful matrix-like blocks transition into a lattice of spheres, illustrating computation for atomistic simulation." style="display: block; margin-bottom: 5px; clear:both;max-width: 100%;" link_thumbnail="" decoding="async" loading="lazy" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-768x432.jpg 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-179x101.jpg 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-300x169.jpg 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-625x352.jpg 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-1536x864.jpg 1536w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-645x363.jpg 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-660x370.jpg 660w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-500x281.jpg 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-160x90.jpg 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-362x204.jpg 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-196x110.jpg 196w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-1024x576.jpg 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured-960x540.jpg 960w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/mlip-tensor-core-featured.webp 2048w" sizes="auto, (max-width: 768px) 100vw, 768px" title="Tensor Core computation for machine learning interatomic potentials" /><p>Atomistic simulations help researchers understand how molecules and materials behave, from chemical reactions and phase transitions to mechanical failure. These simulations come with a cost-accuracy trade-off. Quantum-mechanical methods such as density functional theory (DFT) can provide accurate energies and forces, but their computational cost often limits their use for large systems or long…</p>
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