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DOE OSTI2020

An Adaptive-Mesh-Refinement Based Computational Tool for Simulating Catalysis at Mesoscale

In this work, we present a computational tool for mesoscale applications using open-source exascale- computing compatible adaptive-mesh-refinement (AMR) library, AMReX [2]. AMReX is software library that enables development of application solvers with block-structured Cartesian AMR. Our tool has capabilities to include realistic geometry representation, chemical species transport, reactions and thermodynamics that are critical for capturing mesoscale physics. A significant achievement is the ability of our solver to automatically import electron microscopy data in the form of a stereolithography (STL) or pixelated file format (mrc, tiff) without undergoing the tedious task of unstructured mesh generation. This feature allows for rapid simulation of catalyst particles with complex morphologies using an immersed-boundary formulation. The use of AMR allows for higher resolutions at catalyst surface interfaces, which in turn provides an accurate description of surface reactions and transport. Our solver uses a hybrid distributed and shared memory parallelism (OpenMP/GPU-based) with which strong scaling up to 10,000 processors for realistic catalyst particle simulations have been demonstrated.

BIOMASS FUELS,MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A Survey of Open-Source Tools for Transmission and Distribution Systems Research

This work presents a review of open-source electric power transmission and distribution systems analysis tools suitable for use by industry professionals and academic researchers. Due to the high complexity of the electric grid, there exist numerous tools and extensive research pertaining to nearly every aspect of the design, operation, and control of transmission and distribution networks. In addition to the commercial tools, a wide range of free, open-source tools, models, and data usable by the scientific community for related research have been developed by different organizations, including both international and US universities and national laboratories. However, due to the absence of a catalog of available tools, models and data, researchers often lack a knowledge of existing capabilities and may develop duplicative software and tools. Increasing awareness of these available resources seeks to accelerate their broader use, leading to more efficient and standardized grid analysis. This review paper (which is part of a larger survey effort that studied over 400 tools in the transmission, distribution, buildings, and electric vehicles space) outlines selected open-source resources that have been developed in power transmission and distribution systems research. It is anticipated that this work can serve as a guide for industry and academic researchers alike, ensuring that research efforts are well-channeled.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Machine Learning for Automated Extraction of Building Geometry

As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.

Touzani, Samir↗

DOE OSTI2023

MOOSE: A Modular Platform for Fission and Fusion Multiphysics

The Multiphysics Object-Oriented Simulation Environment (MOOSE) Framework, as well as MOOSE-based simulation tools, have accelerated the development of fission energy and advanced reactor technologies through the United States Department of Energy, Office of Nuclear Science, Nuclear Energy Advanced Modeling & Simulation (NEAMS) Program. MOOSE contains a complete platform of multiphysics simulation capabilities, capable of running on massively parallel systems, and is developed in an open-source manner with great attention paid to high-quality software quality assurance practices. This overall approach could greatly benefit the fusion energy community, which requires rapid design iteration and improvement in order to facilitate the successful development of fusion as an alternative energy source to fossil fuels. In the first half of this talk, applications of MOOSE and MOOSE-based tools for advanced reactor designs will be showcased, as well as MOOSE ecosystem infrastructure (such as the NEAMS Virtual Test Bed) that enables and accelerates fission reactor design. In the second half, a discussion of how the MOOSE approach to modeling and simulation is currently being applied internationally in fusion energy research and development at the United Kingdom Atomic Energy Authority will be discussed, and ongoing/future domestic research efforts will be highlighted.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Simulation of double buffer layer on CIGS solar cell with SCAPS software

In a CIGS thin film solar cell, the buffer layer is interposed between the absorber layer and the window layer, which plays an important role in interface electricity. CdS is often used as a buffer layer in CIGS cells. Beside the toxicity of Cd, the main drawback of CdS is its relatively narrow band gap (2.4 eV), which can lead to the current loss due to parasitic absorption. In order to reduce the use of CdS, we try other buffer layers instead of CdS and optimize buffer layer structure. Numerical simulation has been used to explore the effect of different buffer layer materials (CdS and ZnSe) on the performance of CuInGaSe{sub 2} solar cells with SCAPS-1D software. The main photovoltaic parameters of the analog device: open circuit voltage, short circuit current density, fill factor and conversion efficiency (η) were compared and analyzed. The results show that different buffer materials and thickness have great influence on the performance of the cell. In this simulation, CdS buffer layer was replaced by ZnSe material, which has a wider band gap. However, the efficiency of this kind of cell is lower than that of CIGS device with CdS buffer layer because of the bad energy band alignment. In order to improve the short-wave absorption and the efficiency of CIGS cell, we simulated a novel CdS/ZnSe double buffer layer structure. The efficiency of the optimized cell with double buffer layer increases from 18.47% (conventional cell with the same CdS buffer layer thickness) to 19.01%.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

SPADES (Scalable Parallel Discrete Events Simulation) [SWR-24-99]

SPADES (Solver for PArallel Discrete Event Simulation) is an open-source parallel discrete event simulation (PDES) package built on the AMReX library. Targeted at solving discrete event systems in parallel, this software package aims to be performance portable and scalable on heterogeneous computing architectures, e.g., graphic processing units (GPU). SPADES implements optimistic synchronization with rollback through an implementation of the Time Warp algorithm. An alternative conservative synchronization approach is also implemented using the Lower Bound on Incoming Time Stamp. In our implementation, logical processes are represented as cells in a grid and event messages are represented as particles. SPADES supports various parallel decomposition strategies, including the use of the Message Passing Interface (MPI) and OpenMP threading. All major GPU architectures (e.g., Intel, AMD, NVIDIA) are supported through the use of performance portability functionalities implemented in AMReX. The SPADES software is released in NREL Software Record SWR-24-99 “SPADES (Scalable Parallel Discrete Events Simulation)”.

Henry de Frahan, Marc [National Renewable Energy L↗

DOE OSTI2022

Memory and CPU efficient coherent mode decomposition of partially coherent synchrotron radiation with subtraction of common quadratic phase terms

Application examples of a memory and CPU efficient coherent mode decomposition (CMD) method for wave-optics based simulation of the partially coherent undulator radiation propagation through a hard X-ray beamline in a 3rd generation synchrotron radiation source are presented. The high efficiency of the method is achieved thanks to the analytical treatment of the common quadratic phase terms that are developed in the phase of cross-spectral density (CSD) of partially coherent radiation at a distance from source. This treatment allows for a considerable, several orders of magnitude, reduction of the 4D CSD mesh density (and the memory occupied by the CSD) required for ensuring sufficient accuracies of wavefront propagation simulations with the modes produced by the CMD at a beamline entrance. This method, implemented in the “Synchrotron Radiation Workshop” open-source software, dramatically increases the feasibility of the CMD of 4D CSD for producing 2D coherent modes for a large variety of applications at storage rings and other types of radiation sources.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Understanding Bifacial PV Modeling: Raytracing and View Factor Models

As part of the PV Magazine Webinar 'View-factor vs. ray tracing - which bifacial modelling techniques should you use?,' the second part lead by NREL will explore the different rear-irradiance calculation software, which fall into two categories: view-factor and ray-tracing models. View factor models assume isotropic scattering of reflected rays, allowing for calculation of irradiance by integration. Due-diligence software such as PVSyst or SAM use the view-factor model. Ray-tracing models simulate multipath reflection and absorption of individual rays entering a scene. Raytracing software such as bifacial_radiance, which is the only available open-source toolkit, offers the possibility of reproducing complex scenes, including shading or finite-system edge effects. This can be useful to evaluate specific system geometries, shading and obstructions, areas of different albedo and electrical mismatch, all topics of interest to the prediction of bifacial performance. Model agreement for view factors and bifacial_radiance software is improving and has been shown to be between 2% (absolute) when compared with measured results.

14 SOLAR ENERGY↗

DOE OSTI2021

Modeling Induction Stirring and Particle Tracking in Molten Uranium

Two independent numerical models have been developed to simulate the behavior of carbon impurities in molten uranium metal. Informed by experimental parameters, one model was created using the commercial software Star-CCM+ and compared with another developed using open-source codes, including OpenFOAM, Finite Element Method Magnetics (FEMM) and a First Passage Kinetic Monte Carlo (FPKMC) approach. The target experimental system features a 404g uranium metal charge containing an average carbon concentration of 139 ppm which was melted in a vacuum induction furnace at 1400° C then resolidified. The microstructures of the uranium and its impurities before and after melting have been characterized and reported separately. Prior to simulating the uranium-carbon system described, the numerical models were validated using a previously published nonradioactive experimental system to ensure agreement with expected output values. Focus has been placed on modeling velocity fields under induction stirring and impurity particle trajectories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Flexible Service Contracting for Risk Management within Integrated Transmission and Distribution Systems

The general objective of our project has been to investigate the ability of Independent Distribution System Operators (IDSOs), functioning as linkage agents for Integrated Transmission and Distribution (ITD) systems, to facilitate the flexible availability and usage of reserve in support of ITD system operations. This general objective is in accordance with Order 2222 of the U.S. Federal Energy Regulatory Commission (FERC), titled “Participation of Distributed Energy Resource Aggregations in Markets Operated by Regional Transmission Organizations and Independent System Operators”. The Final Rule for FERC Order 2222 was issued on September 17, 2020. The primary contribution of our project is that we have formulated an innovative energy management Transactive Energy Design (TES) approach for ITD systems that provides promising support for our project objective. Specifically, we have developed a new type of IDSO-managed TES design for distribution system operations, as well as new types of swing contracts permitting IDSOs to participate in transmission system operations as providers of reserve harnessed from distribution system resources in return for appropriate compensation. Together, these design elements constitute a scalable market-based approach facilitating efficient reserve procurement for ITD system operations from a fuller range of power resources. The efficacy of our approach has been demonstrated by means of detailed conceptual analyses as well as test case simulations. To implement the latter, we have developed the ITD TES Platform V2.0, a computational platform that permits the modeling and software implementation of ITD system operations over time. The Electric Reliability Council of Texas (ERCOT) energy region has been used as the empirical anchor for the development of this platform. Our conceptual and test-case work has been reported in a Wiley/IEEE Press book, two refereed book chapters, and seven refereed journal articles. The key components comprising the ITD TES Platform V2.0 have been released as documented open-source software at online GitHub repositories.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Implementation Aspects of Smart Grids Cyber-Security Cross-Layered Framework for Critical Infrastructure Operation

Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.

cross-layered↗

DOE OSTI2025

Software Quality Assurance for the MOOSE-Based Open-Source Multiphysics Code Cardinal - An Expanded CI Testing Suite

Cardinal is a wrapping of the GPU-oriented spectral element Computational Fluid Dynamics (CFD) code NekRS and the Monte Carlo particle transport code OpenMC within the Multiphysics Object-Oriented Simulation Environment (MOOSE). Cardinal provides high-resolution thermal-hydraulics and/or radiation transport feedback to MOOSE multiphysics simulations. Multiphysics feedback is implemented in a geometry-agnostic manner which eliminates the need for rigid one-to-one mappings. A generic data transfer implementation also allows NekRS and OpenMC to couple to any MOOSE application, enabling a broad set of multiphysics capabilities. Cardinal simulations can also leverage combinations of MPI, OpenMP, and GPU resources. Cardinal continuous development and improvement efforts have led to the software being considered as a high-fidelity design and licensing tool for key areas of nuclear reactor relevant physics, including neutron transport, fluid flow, heat transfer, and mechanical processes. The fast development and expansion of the software from a pure R&D framework towards its application in the nuclear industry and regulation require a focus on developing, enhancing and, maintaining Cardinal’s software quality through strict adherence to a Software Quality Assurance (SQA) framework and SQA program. To facilitate compliance with SQA standards, the Cardinal SQA Program has been initiated during Fiscal Year 2023 (FY23). During the development of the Cardinal SQA Program, multiple gaps have been identified. These gaps are primarily related to model verification and code pedigree as they relate to the use of Cardinal as a safety analysis tool. These gaps have been captured in a report published in 2023. A second report highlighted the progress made during Fiscal Year 2024 (FY24) and described Argonne’s effort to document and integrate software verification within Cardinal’s software development process. This report documents a snapshot of the verification test cases currently available for Cardinal and NekRS in their assimilation into a Continuous Integration (CI) platform. Following the CI practice permits the integrating of source code changes frequently and ensuring that the integrated codebase clears the verification testing for the software. It should be noted that the SQA program itself, including the program plans, procedures, configuration management, and testing strategies, need to be developed in a future step of this task.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Software For Advanced Large-scale Analysis Of Magnetic Confinement For Numerical Design, Engineering & Research (salamander)

As magnetic confinement fusion energy gains traction internationally to enable abundant energy production, designing components for fusion systems is a pressing challenge. During the planned lifetime of a fusion device, components evolve in extreme environments and must withstand large, repeated thermal loads and bombardment by 14 MeV neutrons, plasma ions, and neutral particles (deuterium, tritium, and helium), corrosive conditions, etc. All these physical processes take place simultaneously, interact in intricate ways, and impose important constraints that can affect performance. Experimental data is rare and costly to obtain, making design particularly challenging. Predictive computational frameworks must be an integral part of an accelerated and cost-effective design process by modeling fusion system performance in simulated environments. To better understand component degradation and operational impacts on their performance, the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER) is designed as an open-source, fully integrated, multiphysics, multiscale, NQA-1 compliant framework facilitating 3D, high-fidelity fusion system modeling. To that end, SALAMANDER is a MOOSE-based framework, and therefore leverages MOOSE upstream libraries such as PETSc and libMesh to deliver sophisticated finite element, finite volume, and nonlinear solver technology for fusion energy simulations. SALAMANDER couples MOOSE physics module capabilities—such as thermal hydraulics, heat conduction, Navier-Stokes, and thermomechanics—with tritium transport via TMAP8, neutronics via Cardinal, and nascent particle-in-cell capabilities. Direct simulation Monte Carlo methods will be used to address neutral transport near the walls. By coupling all these physics in an integrated application, SALAMANDER will enable high-fidelity modeling of irradiation levels and plasma exposure conditions of plasma facing components and their impact on heat and tritium distributions, as well as the resulting mechanical constraints experienced by the plasma facing components and performance of blanket systems. Furthermore, SALAMANDER will be particularly suited for engineering studies thanks to the stochastic tool module readily available in MOOSE, allowing for extended uncertainty quantification and risk analysis studies. It is also able to use computer-aided design (CAD) meshes to model complex geometries, which is indispensable for fusion systems. SALAMANDER therefore supports design, safety, engineering, and research projects for magnetic confinement fusion systems

Simon, Pierre-Clement [Idaho National Laboratory (↗

DOE OSTI2020

Open Source Platform for Live Processing of High Data Rate Electron Microscopy (SBIR Phase I Award Topic 15, Subtopic B, Final Scientific / Technical Report)

4D scanning transmission electron microscopy (STEM) is experiencing a revolution both in terms of data size, and data veracity, as are many other areas of experimental data acquisition. This presents enormous opportunities to make new scientific discoveries, but is also hamstrung by aging software that is no longer fit for purpose. Experimental data acquisition platforms must make the same investments in software infrastructure that the simulation community made decades ago to take advantage of high-performance computing (HPC) if they hope to realize the full potential of the latest advances made in detector technology. This requires rethinking the design and deployment of hardware, moving away from small, fixed commodity computing capabilities with closed interfaces to open, modular, scalable systems designed for parallelism and throughput. The technical approach will leverage best-in-class open source technologies, and develop new approaches strategically to build an open source platform for experimental data acquisition targeting high data rate electron microscopy. The project will develop a software platform that uses the power and performance of modern HPC resources to ingest the large volume of data produced by electron microscopes using direct electron detectors. A set of configurable pipelines will be used to enable the processing to be tailored to the problem being investigated with builtin visualization of data at interactive rates. This project will leverage investments made by the DOE and other agencies to provide a powerful software platform for the experimental data community working with most powerful electron microscopes, such as 4D STEM detectors. It will create a platform that serves as a reference for the community. Permissive open source licensing will make the platform available to all, with a simple deployment strategy offering the ability to keep data close to where it is generated. This advanced software platform will require customization for each deployment, creating opportunities for software services that are typically offered to extend open source software platforms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning

Spiking Neural Networks (SNNs) are computational models inspired by the event-driven communication and connectivity patterns of biological neural circuits. They enable high energy efficiency and natural support for diverse architectures ranging from layered networks to small-world and graphstructured topologies. In this work, we introduce NeuroCoreX, an open-source, FPGA-based spiking neural network emulator that provides real-time, on-chip learning and flexible network organization. NeuroCoreX supports both feedforward sensory inputs streamed directly from sensors or PCs via UART and recurrent on-chip connectivity, enabling simultaneous processing and learning from external stimuli and internal network dynamics-capabilities rarely available in existing FPGA SNN platforms. The system implements a Leaky Integrate-and-Fire (LIF) neuron model with current-based synapses and supports pair-based STDP learning on both feedforward and recurrent synapses. A lightweight Python interface enables interactive configuration, live monitoring, weight read-back, and experiment control. Importantly, NeuroCoreX is tightly integrated with the SuperNeuroMAT simulator, allowing SNN models to be transferred seamlessly from software to hardware for hardware-in-the-loop development. By combining real-time plasticity, flexible connectivity, and an open-source VHDL implementation, NeuroCoreX provides an extensible and accessible platform for neuromorphic research, algorithm-hardware co-design, and energy-efficient edge intelligence.

Gautam, Ashish [ORNL]↗

DOE OSTI2024

Guest Editorial

The design of X-ray optical systems is very different from that of conventional optical systems, whether it be for the peculiarities of synchrotron radiation or the grazing incidence operation of most X-ray optical components. Further, specific simulation tools are thus required to ensure that designs work as expected, and there is now a vibrant landscape of open-source, freely available software that can be used to that end, and explore new design concepts.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Capability in Theory, Modeling, and Validation for a Range of Innovative Fusion Concepts using High-Fidelity Moment-Kinetic Models

A computational modeling capability is created and available to the fusion community to understand and design lower-cost and innovative fusion concepts. The approach uses high- fidelity kinetic, moment-kinetic, and moment models and includes sophisticated plasma- boundary interactions. A majority of fusion-relevant simulations are performed with magnetohydrodynamic models and hybrid particle-in-cell codes, with limited-fidelity electron and kinetic physics. However, in fusion configurations like Z-pinches, field-reversed- configurations, plasma jet magneto-inertial fusion, spinning mirrors, and others, kinetic effects (both electron and ions) are critical to understand the physics and design scaling into the highly kinetic regime of a burning fusion plasma. Furthermore, as present fusion machines move towards a burning plasma regime, liquid-metal blankets are needed to handle first-wall heat- flux, reduce erosion, and eventually for energy conversion and fuel breeding. The work performed under this ARPA-E BETHE Capability Team advances the state-of-the-art in modeling and understanding plasma dynamics in fusion devices and its coupling with liquid-metal dynamics. These are critical areas of research for fusion energy to become realizable. To address these complex problems, we have leveraged and extended computational capabilities through the code, Gkeyll (developed jointly with Princeton Plasma Physics Laboratory and academic partners), for kinetic and moment modeling of fusion plasmas. The Concept Teams supported by this Capability Team include the Wisconsin High-field Axisymmetric Mirror (WHAM), Centrifugal Mirror Experiment (CFME), Plasma-Jet Magneto- Inertial Fusion (PJMIF), and solid and liquid wall plasma-material interaction studies relevant to a number of fusion concepts including Zap Energy’s Z-pinch. This software is open-source and available to the fusion community as a high-fidelity tool for the design of lower-cost fusion experiments. 3D gyrokinetic simulations of WHAM are now possible for long enough time scales to understand the evolution of interchange instabilities. 3D multi-fluid simulations of CMFE at higher Mach numbers are now possible for detailed design iterations with the goal of stability. The state-of-the-art in understanding shock formation and shock mitigation regimes in merging liners for PJMIF have been furthered by our kinetic simulations. Our novel models and frameworks studying plasma-material interaction by incorporating wall emission for various solid wall materials of relevance to pulsed and steady fusion concepts have advanced the state-of-the-art in our understanding of particle fluxes, heat fluxes, and other quantities at cathodes and anodes. The results from this work may explain discrepancies between experimental and theoretical predictions of achieved current densities in pulsed concepts such as Z-pinches. Another significant contribution of this Capability Team is the development and deployment of a novel experimental platform, LEX (Liquid Electrode eXperiment), at Virginia Tech to understand liquid metal free-surface response to electromagnetic pulses. The novel experiments along with model validation quantified the effect of different materials and sizes of liquid metal droplets on the radiative power balance of fusion plasmas for pulsed concepts. Furthermore, these experiments provided mitigation strategies for violent liquid metal response for high current pulses as would be expected in fusion regimes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage.

Broughton, Michael↗