For decades, computational chemistry has faced a tug-of-war between accuracy and speed. Ab initio methods like density functional theory (DFT) provide high…

NVIDIA DevTalk serves as a vibrant community hub where developers can engage in discussions, seek assistance, and collaborate on projects involving NVIDIA hardware and software. Developers can tap into the collective expertise of the NVIDIA developer community, sharing insights, troubleshooting issues, and exploring best practices for GPU programming and AI development. Additionally, DevTalk provides a platform for developers to showcase their projects, receive feedback, and network with peers, fostering collaboration and knowledge exchange within the NVIDIA ecosystem.

NVIDIA Developer

NVIDIA has released the ALCHEMI Toolkit, a GPU-native, PyTorch-based framework for building custom atomistic simulation workflows in chemistry and materials science. It bridges the gap between machine learning interatomic potentials (MLIPs) and classical simulation infrastructure by providing modular, composable building blocks including batched dynamics engines, geometry optimizers (FIRE/FIRE2), molecular dynamics integrators (Velocity Verlet, Langevin, NPT), model wrappers for MACE/TensorNet/AIMNet2, and GPU-resident data management. Key features include a FusedStage API for single-GPU pipelines with torch.compile support, a pipe operator for distributing stages across multiple GPUs/nodes, and a hook system for custom dynamics like simulated annealing. Partners including Orbital, MatGL, and Matlantis have already integrated the toolkit, reporting speedups of up to 33x for batched simulations. Installation is via pip with Python 3.11+ and CUDA 12+.

Building Custom Atomistic Simulation Workflows for Chemistry and Materials Science with NVIDIA ALCHEMI Toolkit

How does ALCHEMI Toolkit advance digital chemistry?

Key features of ALCHEMI Toolkit for building end-to-end workflows

Get started building molecular workflows with ALCHEMI Toolkit