FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

Self Cite

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI modelsincluding CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this frameworkin the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

End-to-end AI framework for interpretable prediction of molecular and crystal properties

Park

Zhu

Huerta

et al. 2023

Self Cite

“…Beyond these original contributions, we also provide an end-to-end framework that unifies initial data production, construction of BCs and their use to train, validate and test the performance and reliability of AI surrogates. These activities aim to create FAIR findable, accessible, interoperable and reusable and AI-ready datasets and AI models [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Applications of physics informed neural operators

Rosofsky

Majed

Huerta

2023

Self Cite

We present a critical analysis of physics-informed neural operators to solve partial differential equations that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate physics-informed neural operators in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our physics-informed neural operators to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled partial differential equations. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the \href{https://github.com/shawnrosofsky/PINO_Applications/tree/main}{source code}, an interactive \href{https://shawnrosofsky.github.io/PINO_Applications/}{website} to visualize the predictions of our physics informed neural operators, and a tutorial for their use at the \href{https://www.dlhub.org}{Data and Learning Hub for Science}.

“…Specifically, the FAIR principles were originally introduced [7] as guidelines for the management and stewardship of scientific datasets to optimize their reuse. Recently, the FAIR for Research Software (FAIR4RS) working group has developed an interpretation of the FAIR principles specifically for research software [8][9][10][11], and FAIR principles have also been applied in the context of benchmarking and tool development [12], and on the creation of computational frameworks for AI models [13].…”

Section: Introductionmentioning

confidence: 99%

FAIR AI models in high energy physics

Duarte,

Li,

Roy

et al. 2023

Self Cite

The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.