{"categories":[{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"},{"label":"Chemistry","url":"https://skillfed.io/packages/category/scientific-engineering-chemistry"}],"enrichment":{"capability":"GPU-accelerated batched primitives for atomistic simulation: neighbor lists, molecular dynamics ensembles, geometry optimization, and interatomic interactions (dispersion, electrostatics) built on NVIDIA Warp with PyTorch and JAX bindings.","skillfed_tags":["gpu-computing","molecular-dynamics","computational-chemistry"],"use_cases":["Accelerate existing molecular dynamics workflows by replacing CPU neighbor list computation with GPU-batched cell-list or cluster-pair methods.","Compute DFT-D3 dispersion corrections on batches of molecules during model training with automatic differentiation.","Build production molecular dynamics simulations with NVT or NPT ensembles using GPU-optimized Langevin or Nos\u00e9-Hoover thermostats.","Perform geometry optimization (lattice and coordinate relaxation) via FIRE2 on large systems without implementing custom GPU kernels.","Evaluate particle mesh Ewald electrostatics with automatic parameter tuning and backpropagation for charge-aware training."],"what_it_does":"NVIDIA ALCHEMI Toolkit-Ops is a collection of GPU-optimized kernels written in warp-lang for accelerating atomistic simulations. It provides batched, high-throughput primitives for neighbor list computation (naive, cell-list, and tiled cluster-pair methods), molecular dynamics (NVE, NVT, NPT, NPH ensembles with multiple thermostat options), geometry optimization (FIRE and FIRE2), and interatomic interactions including DFT-D3 dispersion and electrostatics (DSF, Ewald, PME). The package targets systems with large atom counts and microsecond-scale per-atom throughput on GPUs.\n\nIt integrates with PyTorch and JAX, enabling differentiable computation of forces, charge gradients, virials, and stress tensors. The kernels are modular and reusable, intended for library developers, researchers developing new methods, and engineers building production molecular dynamics or interatomic potential applications. Installation is straightforward (pure Python wheel with numpy and warp-lang dependencies), but requires PyTorch or JAX with CUDA support and Python 3.11\u20133.14.","worth_installing":"Yes, if you are working on GPU-accelerated atomistic simulations or molecular dynamics and need production-ready, batched kernels. The package is actively maintained, has no known vulnerabilities, carries a permissive Apache-2.0 license, and integrates cleanly with PyTorch and JAX. Install friction is low. The main constraint is the requirement for CUDA 12 or 13 and Python 3.11\u20133.14; verify CPU performance expectations if GPU access is limited."},"id":"nvalchemi-toolkit-ops","links":{"html":"https://skillfed.io/packages/nvalchemi-toolkit-ops","md":"https://skillfed.io/packages/nvalchemi-toolkit-ops.md","pypi":"https://pypi.org/project/nvalchemi-toolkit-ops/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-04","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"nvalchemi-toolkit-ops","python_support":"supports_current","summary":"High-performance NVIDIA Warp primitives for GPU-enabled computational chemistry and atomistic simulation workflows."},"popularity":{"monthly_downloads":238443,"position":8938,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.1"}