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Smart, autonomous control is a method of automating a complex control loop. This requires better information collection, data analysis system modeling, and decision-making. This research area should be supported by other PROs.
1. To gather and process data effectively, advanced analysis and collection strategies must be used. This will provide the necessary insights for online control. CNO email id list

The algorithm for decision making must be pre-trained with artificial data, generated through virtual experiments, and continually improved based on the most recent physical models.

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4. A shared infrastructure should be included in the training required for AI/ML algorithms. 

The AI/ML method developments that have been identified as allowing for unprecedented levels of computational effectiveness and complex computation are essential advances. (See the section on Enabling Capabilities in Computer Sciences and Mathematics).

* Search/optimization – New developments in search/optimization/data mining techniques are required to handle the complex, large, high-dimensional and complicated spaces inherent to scientific problems. Chief Nursing Officer Email list


Advanced methods of detecting correlations could be used to predict failure in accelerators and self-calibration tools that can be used for experiments. New multimodal measurement methods could be made possible by the identification of correlations between data.

* Quantification and estimation of uncertainty Online control algorithms are required that accurately include uncertainty and cost from experiments. There are many strategies to suit the various applications, including Bayesian strategies and techniques for learning by repetition.

* Approximations are required for an autonomous experimental loop to function in real-time. 

* Physics using AI/ML. Equation-learning strategies can help in the creation of theory in both physics and chemistry. They allow direct calculation of physical equations from data. The theoretical benefits of numerical solutions that are solely mathematical may be beneficial. However, they limit further development which can be achieved with analytic solutions. ML tends not to produce numerical solutions. However, there are advancements in the art of learning equations. It is easier to extract equations from data. This makes it more intuitive and easy to understand.

Potential Impact

Intelligent automation can transform science. It allows scientists to solve more problems, and it also gives scientists the freedom to think at a deeper level. It is becoming more apparent that the current system does not fully utilize the capabilities of modern scientific instruments. The rapid growth in brightness of synchrotrons (63as well similar trends for advanced experimental tools (e.g. The current limitations of analysis pipelines might limit the use of electron microscopes that can achieve high frame rates (up to 100 000 images per seconds) but they are still very useful. Automating workflows allows

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researchers to maximize the potential of their instruments and allow them to address complex issues. Figure 3 shows the increasing output of synchrotron publications and increased brightness with time.

Figure 3. Figure 3. Right, the output of a publication

Although synchrotrons have also increased in importance, they are still not as important as the source’s properties. This indicates that existing light sources are not being used to their full potential. The efficient use of existing resources could lead to dramatic improvements in scientific research efficiency. Left image reprinted with permissions from J. Stohr, H. C. Siegmann and Magnetism:From Fundamentals to Nanoscale Dynamics (Springer 2006). Right

Image courtesy Apurva Mehta SLAC National Accelerator Laboratory

Most accelerators have more complicated installation and operation. Storage rings with high brightness often have a small safe operating area, also known as the dynamic aperture. The commissioning phase can see this aperture narrowed because of many errors that have not been corrected. A storage ring design may have limited performance due to the need to keep an overhead dynamic aperture. This could be reduced by advanced tuning techniques. The rapid implementation of complex XFEL operating modes allows for different types of scientific experiments. Users can also receive novel beam designs. Self-contained accelerators are a breakthrough in the development and operation. Advanced tuning techniques will make it possible to ensure that the accelerators perform as intended. Future accelerators will have access to AI/ML technologies that offer unimaginable capabilities. CNO email listing
The potential to revolutionize chemistry, materials, and bioscience research by discovering the most unusual and high-performance materials through advanced autonomous experimentation is huge. 

Complexity is everywhere. It is difficult to determine the potential impact and the extent of the research that will yield it. The complexity of material composition, such as biomaterials and biomimetic systems and alloys, has a significant impact on the problems. Some types of metallic glass might be able to achieve extremely high strength-to weight ratios [64]. The perfect “steel-in-the-near future” material will offer transformational improvements in those applications (e.g. Aerospace is hidden in the many alloys available. The sheer amount of processing space required to create and quash metastable states that have become glassy makes it difficult to imagine and enhance. Autonomous exploration could transform research into pathways-dependent phenomena. Self-assembling materials can reach a variety of non-equilibrium states that are only possible with correct processing history [65-66]. Researchers are sometimes able to use “pathway engineering”, where an objective that cannot be achieved using equilibrium processing techniques can be chosen and enforced according to the correct order. Online control of synthetic platforms could allow researchers to expand upon these initial breakthroughs and navigate complex assemblies. Close connections to the right material modeling can make it easier to study a wide range of functional materials. For example, design of advanced thermoelectrics would benefit from experimental searches with coupling of structural/spectroscopic probes, operando functional measurements, and structure-property modeling. Exact physical simulations have been used in studies of quantum heterostructures. These models could be integrated into the measuring loop to aid in the search for new materials geared towards quantum applications of information sciences. 

A broader research program in autonomous experiments may have both immediate and long-term advantages. In the short-term (3-5 years), dedicated research will produce a number of highly specialized tools such as AI/ML models and hardware systems that enable autonomous exploration of sample. It will be possible to create robust, generalized autonomous platforms for synthesis that can tackle many chemical, material, and bioscience problems while also uncovering the most recent physical concepts. The ultimate goal of autonomous research is to remove scientists from micromanaging experiments. This includes optimizing experimental conditions. Scientists can then solve scientific problems at a higher level.

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Many of the experimental tools developed by the DOE complex can be benefited from the most advanced AI/ML control methods.

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These AI/ML control techniques will improve efficiency and stability, provide greater availability and reliability for all users, and increase the quality of research. This will be a benefit to cutting-edge research programs. As these advanced experimental tools underlie a wide variety of modern scientific studies–from geosciences chemistry to biosciences to energy research–improvements would have broad benefits throughout the BES research program.

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“Steel for your future”

You can find the most incredible new materials. Where can they be found?

Mixing a small amount of metals together creates conventional alloys. They are only a fraction of the available alloys. High-entropy alloys contain more elements than traditional alloys. This greatly expands the range of possible compositions. These alloys will likely have record-setting physical properties (e.g. These alloys are likely to have record-setting physical properties (e.g. strength-toweight ratio), especially if they are in metastable or frustrated states such as those found in metallic glasses. These parameters are too vast to be explored using conventional methods or high-throughput search techniques. High-performance materials, however, represent an isolated island in a sea of other non-interesting materials. These parameters can be found using autonomous experimental models that use inputs from accelerated modelling. They can then identify interesting outliers and guide future research in a meaningful way. These methods, if properly implemented, could help create the future high-performance metals. They will have significant applications in transport, aerospace, and energy harvesting. Left image courtesy K. G. Yager at Brookhaven National Laboratory. Brookhaven National Laboratory distributed the right image under Creative Commons Attribution Noncommercial License CNO email listing


Structure of Intrinsically Disordered Proteins

The structural description
Biosystems that can be flexible pose
major challenge in biology
Because of the intrinsic nature of the protein.
disorder. It is essential to reach this goal.
Information about the structure
understand protein function.
To help decode the
Complexity of disordered
biomolecule, a combination
Also, neutron scattering
molecular technology for high-performance
Simulations are possible
Configure the configurational
ensemble (i.e. The collection
3D Structures
biomolecule adopts)[79].
This combination, however, is Small-angle neutron scattereding as well Hamiltonian replica interchange
It could take several weeks, but it is currently happening.

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The correct molecular dynamics simulation can be used to produce the
Details for a specific configuration of the flexible protein.

biomolecule. AI/ML could be used to combine neutron scattering data in high-performance molecular simulations which run in real time. AI/ML-driven directing simulations toward experimental neutron scattering results will be possible. This could greatly reduce the time it takes to solve. This could have a significant impact on the structure biology and biosystems that have flexible structures. Similar problems can be found in electron and xray microscopy. While current methods attempt to reconstruct the structure of the typical structure, AI/ML is beginning to discover conformational modifications [80-81]. 

Top left image reprinted from fast-facts-about-high -flux-isotope-reactor-oak-ridge-national-laboratory. Oak Ridge National Laboratory. | Bottom left image reprinted from supercomputing-neutrons-unite-unravel-structures-intrinsically-disordered-protein. Right image Reprinted by Shrestha U. R., Juneja, P., Zhang, Q., Gurumoorthy V., Borreguero J. M., Urban, V., Cheng, X., Pingali S. V., Smith J. C., O’Neill H. M., and Petridis L. “Generation the configurational ensemble of an Intrinsically Disordered Protein Using Unbiased Dynamics Simulator.” Dynamics Simulation Proc. Natl. Acad. Sci. U.S.A. 116 (2016): 20446-20452. doi: 10.1073/pnas.1907251116. Chief Nursing Officer Email list

PRO 3. Facilitate offline optimization and design of facilities. 

Key question: How do we enable virtual laboratories–offline design and optimization of facility operation and experiments–to achieve new scientific goals?


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SUFs face the main challenge of optimizing experiments and facilities to achieve scientific goals. Modern SUFs can also house complex accelerators that are costly and difficult to design, build and maintain. These are time-consuming, require careful planning of sequences of actions such as formulating the hypothesis and conducting the experiment(s). Then, analyze the results and theory-experiment with data analytics to draw conclusions. It is important to improve the design of experiments at individual and facility levels to accelerate the discovery of science and reduce redundancy, and to extract as much physics knowledge as possible from each experiment. CNO email id list

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Research at SUFs is expensive and difficult. Re-creating the entire facility and experiment can be costly and time-consuming. This could limit the number of experiments that can possibly be performed. The probe’s complete understanding (e.g. The probe’s complete understanding (e.g., on the beamline), could be used to analyze the data postexperimentally. However, this is rarely possible due to incompatibility or inaccessibility of measurements and incompatibility with experiments. Wavefronts of xrays produced by an XFEL can be helpful to users. However, it is not possible to measure the same wavefront. A mix of simulations and virtual diagnostics is important.

Modern SUFs present optimization and planning problems. These include determining the optimal conditions for the synthesis of a material, choosing the right combination of multimodal test to address a structural inversion problem, optimizing the settings of specific instruments to achieve experimental and computational objectives, and creating continuous calibrated models to assist in analysis and study. Recent research has shown the potential of AI/ML in optimizing paths in situations where there are uncertainties. The design and optimization of experiments must be done in a controlled environment, which allows for exploration of the parameters in silica. Only a few experiments can be performed in real labs, and it is costly.

This goal requires the creation of a digital copy of every SUF. It allows users to develop, run, and optimize their experiments in an uninvolved environment controlled by AI/ML. Users can then seamlessly switch to the actual facility which will accelerate the time it takes to discover science. Figure 4 shows how the virtual laboratory environment must be closely coupled to the laboratory facilities in order to allow simulations to accurately reflect real life (e.g. Online experiments can be used to simulate the SUFs. These virtual labs need high-fidelity simulations that combine speed and precision to reproduce the fundamental physics of lab measurement and synthesis.
Virtual laboratories can be used to optimize experiments and speed up training. They provide starting points and automate the creation of analysis codes, workflows, and workflows that allow scientists to carry out scientific experiments from conception to completion. | Image courtesy Rama Vasudevan, Oak Ridge National CNO email database

Laboratory Chief Nursing Officer Email list

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Directions for Research

Research aims to create physically accurate, virtual laboratories of facilities for experiments that guide the design process of synthesis or characterisation in silica. These environments are closely linked to real facilities and continuously refreshed with real-world experience to ensure exact reproduction. This will allow AI/ML-assisted, automated design of optimal strategies and analysis workflows for experiments to assist in knowledge acquisition.

Virtual laboratories environments must contain:

* Rapid simulations

Rapid, on-the-fly methods (including models that simulate surrogate), within the virtual world to simulate results

Hardware techniques are used to accelerate simulations (e.g. FPGAs, GPUs, and higher-quality numerical approximators such as DNNs).

* Accurate simulations, theory-experiment-matching

– The First Principles Model (e.g. Predictions derived from heterostructures

The proper theory-experiment match routines are used to calibrate models in physics using observations. 

* User interfaces

360deg immersive environments allow users to run and create virtual tests, analyse data and revise their scientific hypothesis.

They would be able later to create digital twins once they had been constructed.

* Planning and design experiments Chief Nursing Officer Email list

AI/ML-enabled context-specific experiments design and AI/ML-generated analyses workflows

AI/ML-guided planning using techniques such as reinforcement learning [84] to determine the best sequence of measurements required to answer a scientific inquiry

Virtual diagnostics provide real-time data to support accelerator operation and the analysis of data from user experiment data

* Facility optimization

* Accelerator design with acceleration acceleration and control 

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Statistics on the most used patterns of instruments and optimization opportunities (e.g. Co-location of multiple instruments

Instruments and staffing 

Digital twins will expand the possibilities for experiments as advanced planning may allow for larger-scale projects to be completed.

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The modern accelerators are one example of the potential benefits and difficulties of digital twins. A precise model is required to create a digital twin that can accurately predict the performance of the accelerator and the characteristics of the particle beam. The accelerator model is crucial for the design analysis, interpretation and analysis of user-created experiment and the continuous advancement of the machine. It can also be used in accelerator tuning and control research. 

Virtual laboratories are available.

They also assist in the onboarding process and provide training to improve facility operation efficiency. They assist with the design and planning of existing and new facilities.

An accelerator is designed with a design model that is Chief Nursing Officer Email list

The simulation model is built on the physical processes involved. The design model is the basis for operating the machine, such as setting the parameters for accelerator parts. The actual device often differs from the model. Accelerators are often not able to meet the desired performance due to differences between the design model, and the actual machine.

A physics model could be calibrated using measurements to bridge the gap between it and the machine. This could enable the identification and correction of errors in the machine, as well as precise predictions about the machine’s performance. Model calibration is based on minimising predictions and measuring using least-square fitting. This only applies to subsystems with high-quality measurable signals, such as the storage ring and linear optics of Linacc [85-88], which may be susceptible to significant under- or over-fitting [89] or over-fitting (89). Bayesian Inference Techniques (90) is an innovative AI/ML algorithm that may allow for precise model calibration. It covers a greater range of parameters than traditional methods. Examples include the calibration of storage rings for nonlinear beam dynamics or models from start to end of XFELs.

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AI/ML can also help predict accelerator components, particularly when it comes down to forecasting abnormalities and the likelihood of failure. Preventive maintenance can be planned using predictive models to prevent scheduled downtime. These models can also be used to quickly identify the cause of failures and speed up the recovery process, thereby decreasing the chance of the fault recurring. Both rapid tuning and prediction of faults require rapid processing of control algorithms and modeling, which can be AI/ML-accelerated. 

Important to remember that complex systems may require extensive computer simulations for physics-based modeling. Complex accelerators and experimental systems, as well as the precision required for simulations, can require high-end computing resources. Software that requires frequent model evaluations is hindered by this constraint. Studies on the LCLS-II, for example, could benefit from the prediction of the photon beam’s characteristics using this model, but it would require long hours of computer-generated simulation. The knowledge could not therefore be available in real-time. AI/ML could allow modeling that is thousands times more efficient than physics based modeling. This can be achieved by using flexible neural network as well as other models trained with simulation data or experiments to replace first-principles simulations. The surrogate models can be continuously refined and updated to make high-quality predictions about machine performance. They also provide fast predictions. Chief Nursing Officer Email list

Digital twins can also speed up the process of creating chemical and other materials with desired properties. A series of syntheses can also be done in virtual settings if a desired structure can be predicted using the first principles model. The real-world experiments will still be used to calibrate the models and ground them for synthesis. The virtual twin can identify the most efficient techniques for characterization to determine whether the structure of interest was constructed. In an ideal scenario, the analysis algorithms could also be generated by the digital twin to reduce the time. This is a vital aspect of facility operation and research and development, which allows for both to be improved simultaneously. 

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First-principles models that require quick approximations for the material’s interaction potentials used in a variety of codebases may allow for significant increases in simulation size and a more efficient integration of the real-time platforms to experimental research (PRO 2). . Complex phase space sampling is one of the greatest challenges in molecular simulations. This not only makes it difficult to perform many simulations, but also puts a limit on the future plans.

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Recent advances have been made in sampling using ML [92for instance]. Boltzmann generators, an algorithm in ML that determines invertible transformations of the Boltzmann and Gaussian distributions, are examples. This research is a proof of concept and addresses one of the greatest obstacles to molecular simulations being used to understand material behavior. These researches should be part of the capability development of the SUF community of users, as they represent a paradigm shift. Improvements are also needed in other areas of molecular simulating. Boltzmann generators aren’t yet capable of handling nonequilibrium dynamics. 

A variety of models are needed to meet the needs of SUFs. AI/ML models can detect important physical parameters and allow for changes without having to retrain the entire system. This could lead to the co-design process, where parameters of the physics model can be determined and refined together with data collection. It’s exciting to see models created using AI/ML that are predictive for a range of physical issues. Convolutional neural networks (CNNs), which combine the computational capabilities of several networks with nodes that perform local convolutions on data, are an example of a convolutional neural network. Convolution hierarchy can be used to describe complex physical phenomena. It is a natural way of aggregating elements. A CNN generalized model could represent the assembly of colloids, nanoparticles block cop-polymers and proteins. It is based on fundamental physical physics of anisotropically interacted parts. Each system would be assigned slightly different weights. It would be beneficial to map known physical parameters to CNNweights. This would allow for an easily understandable ML model, from which one can deduce the meaning of weights retrained. Research in science is most interested in AI/ML models that provide useful physical insights. Research in interpretable AI/ML models that can be applied specifically to scientific problems can be extremely beneficial. 

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Capabilities that Enable

The development and enhancements of capabilities are required to meet the demands of the digital twin paradigm. Chief Nursing Officer Email list

* The adoption of a comprehensive data management program that provides all information about the facility, from monitoring to diagnostics readbacks. The facility’s operations data will be available in an easily accessible format. This will allow for the easy use of ML techniques to create digital twins. All data must be synchronized across the facility.

* The development of AI/ML algorithms for training facility-scale models using heterogeneous inputs in various formats and sources. They can also apply physical-principle limitations to model AI/ML.

* Design of facilities control system that can accommodate AI/ML flow requirements (e.g. allowing local GPU integration or remote access to low latency GPU accessibility.

* The development and application of AI/ML techniques for accurately assessing the uncertainties in AI/ML models at a large scale. There are many input and output types that can be used to ensure the accuracy of the digital twin. 

AI/ML technology advancements

The following AI/ML developments are required to create a virtual lab environment:

* Rapid in-inverse structure prediction using scans and spectral measurements. Includes uncertainty quantification.

* AI/ML-assisted speedups for dynamical simulations that can be optimized to work with certain devices. Chief Nursing Officer Email list

* RL and Bayesian learning algorithms for efficient exploration of multidimensional parameter space with uncertainty.

* Learning by feature using realistic experimental constraints to match theory-experiment match. email marketing database 
Potential Impact

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Digital twins of SUFs may offer new ways to think in experiments in science research. These are some of the potential impacts:

* The time it takes to apply a new material phenomenon in real-world practice is shorter. A novel topological order of electrical conductivity is used to create the “synthesis” recipe to make the material. 

* A good experimental design is for specific mechanistic problems, such as the mechanism to prolong the lifetime of semiconductors’ carriers for solar photovoltaics or the high electromechanical response of ferroelectric relaxors.

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* Accelerator design cycles that are quick and thorough, as well as analysis of the parameter space, can optimize the performance of your facility.

* Non-invasive virtual diagnosis that provides real-time information to facilitate automated user experiments.

* Tests that were once impossible because of the lack of analysis routines or the complexity of the procedures. 

* Environments that train autonomous AI/ML driven robots for scientific exploration.

PRO 4. Use shared scientific data for machine learning-driven discoveries

Important question: How can we accelerate scientific discovery by using the vast array of diverse and complementary data collected by the BES facilities available to scientific users?


Although the SUFs share the most recent scientific research and operational data, the vast majority of data analytics infrastructure, workflows and infrastructure is splintered. Inseparate operations and the lack of tools to search and analyze data can result in repetitive research and unneeded experiments, as well as missed opportunities to make use of the vast amount of data facilities have. Research across facilities will be accelerated by the rapid improvement in data sharing, analysis, curation and processing. This will create innovative tools for multimodal research that involves multiple users. It also establishes an experiment platform to test AI/ML next generation applications for both SUFs and BES communities [93].

This article discusses the possibility of establishing an open data repository to store outputs from BES SUFs. The repository must have infrastructure that can support the whole lifecycle of data AI/ML and tools to automatically record and structure metadata. It also needs infrastructure that can store annotated, curated, high-quality datasets. These data sets will be useful for future applications. CNO mailing lists

A searchable repository of scientific data is a key element in experiment design. It allows for hypothesis-making and comparisons of observational data. The automatic creation of benchmark datasets based on heterogeneous science data will be possible through the integration of different data sources. This can speed up the development of AI/ML algorithm discussed in this report and aid in the advancement AI/ML capabilities throughout the DOE complex. The repository may produce both abstractions and scientific domain-specific schemas. This will allow researchers to go beyond the basic metadata exploration and focus on research that is based scientific concepts such as crack development in composite materials, or transition of phases in simulations. This could facilitate coordinated efforts to create standards, formats, and prioritize across SUFs.

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Research Directions 

BES SUFs will produce thousands of petabytes each year by 2025 and 2025. Although user groups can do research using their data sources, the scientific world has not been able to access the full range data available to improve the SUFs or increase discovery. This PRO describes the concept for a shared data repository, which covers both scientific and technical domains. The infrastructure should be available throughout the data lifecycle. It should also have the necessary capabilities to acquire data and metadata, curation of datasets with high-value search; and multimodal multiexperiment analyses. AI/ML could be used in this area to improve the process. It would use autonomous curation to gather context, provenance and quality of data. Also, it would provide tools that allow for large-scale multimodal searching and analysis. It is intended to coordinate the creation, curation, and application of large amounts of knowledge and data, as well as related models, workflows computations and experiments. After that, the byproducts of the coordination effort are discussed and reviewed. These include the creation of benchmark datasets, and coordination efforts focusing on new scientific themes. ASCR Data and Models for AI Workshop report (22The Workshop Report On the Needs for Basic Research In Scientific Machine Learning: Core Technologies for Artificial Intelligence] 94and the global solicitation for AIR (findability, interoperability and reusability) datasets.
This PRO highlights four research areas that are related to the shared data infrastructure CNO consumer email database

CNO email leads

1. To ensure that all SUF data are stored in high-quality metadata for each study and computation, automated data capture is used as well as metadata.

2. Data search tools that can identify high-value, relevant datasets.

3. Meta-analysis allows you to simultaneously analyze multimodal data sets.