94 research outputs found
Roadmap on Electronic Structure Codes in the Exascale Era
Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing
Molecular simulations and visualization: introduction and overview
Here we provide an introduction and overview of current progress in the field of molecular simulation and visualization, touching on the following topics: (1) virtual and augmented reality for immersive molecular simulations; (2) advanced visualization and visual analytic techniques; (3) new developments in high performance computing; and (4) applications and model building
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
We present the Open MatSci ML Toolkit: a flexible, self-contained, and
scalable Python-based framework to apply deep learning models and methods on
scientific data with a specific focus on materials science and the OpenCatalyst
Dataset. Our toolkit provides: 1. A scalable machine learning workflow for
materials science leveraging PyTorch Lightning, which enables seamless scaling
across different computation capabilities (laptop, server, cluster) and
hardware platforms (CPU, GPU, XPU). 2. Deep Graph Library (DGL) support for
rapid graph neural network prototyping and development. By publishing and
sharing this toolkit with the research community via open-source release, we
hope to: 1. Lower the entry barrier for new machine learning researchers and
practitioners that want to get started with the OpenCatalyst dataset, which
presently comprises the largest computational materials science dataset. 2.
Enable the scientific community to apply advanced machine learning tools to
high-impact scientific challenges, such as modeling of materials behavior for
clean energy applications. We demonstrate the capabilities of our framework by
enabling three new equivariant neural network models for multiple OpenCatalyst
tasks and arrive at promising results for compute scaling and model
performance.Comment: Paper accompanying Open-Source Software from
https://github.com/IntelLabs/matscim
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