Inq, a Modern GPU-Accelerated Computational Framework for (Time-Dependent) Density Functional Theory.

Xavier Andrade Chaitanya Das Pemmaraju Alexey I Kartsev Jun Xiao Aaron M LindenbergSangeeta RajpurohitLiang Z TanTadashi OgitsuAlfredo A Correa

Published in: Journal of chemical theory and computation (2021)

We present inq, a new implementation of density functional theory (DFT) and time-dependent DFT (TDDFT) written from scratch to work on graphic processing units (GPUs). Besides GPU support, inq makes use of modern code design features and takes advantage of newly available hardware. By designing the code around algorithms, rather than against specific implementations and numerical libraries, we aim to provide a concise and modular code. The result is a fairly complete DFT/TDDFT implementation in roughly 12 000 lines of open-source C++ code representing a modular platform for community-driven application development on emerging high-performance computing architectures.

Keyphrases

density functional theory
molecular dynamics
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machine learning
quality improvement
mental health
deep learning