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DOE OSTI2022

Emerging Themes and Future Directions of Multi-Sector Nexus Research and Implementation

Water, energy, and food are all essential components of human societies. Collectively, their respective resource systems are interconnected in what is called the “nexus”. There is growing consensus that a holistic understanding of the interdependencies and trade-offs between these sectors and other related systems is critical to solving many of the global challenges they present. While nexus research has grown exponentially since 2011, there is no unified, overarching approach, and the implementation of concepts remains hampered by the lack of clear case studies. Here, we present the results of a collaborative thought exercise involving 75 scientists and summarize them into 10 key recommendations covering: the most critical nexus issues of today, emerging themes, and where future efforts should be directed. We conclude that a nexus community of practice to promote open communication among researchers, to maintain and share standardized datasets, and to develop applied case studies will facilitate transparent comparisons of models and encourage the adoption of nexus approaches in practice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Release of a High Temperature Engineering Test Reactor (HTTR) Steady State Multiphysics Model to the Virtual Test Bed

In response to climate change, global governments and private industry have established a common goal of achieving net-zero emissions by 2050 \cite{osti_1865910}. This goal requires a reassessment of current energy demands and production methods. Reducing emissions at an affordable cost while maintaining grid reliability requires a nationwide collaborative effort among government and industry in the United States. Nuclear power is the leading low-carbon electricity generation method. In the past 50 years, the use of nuclear power has reduced carbon dioxide emissions by over 60 gigatons and has played a crucial role in the security of energy supply~\cite{IEA}. In the U.S., nuclear power accounts for 20\% of the electrical supply and provides energy reliably. Advanced reactors will operate at higher temperatures, operate more efficiently, utilize more energy stored within fuel, and reduce the amount of waste produced \cite{osti_1616270}. To face these challenges and goals, the U.S. Department of Energy has created an initiative to focus on the modeling and simulation tools to support future nuclear power plant design, licensing, and operations. The Virtual Test Bed (VTB)~\cite{vtb2023} was launched by the National Reactor Innovation Center (NRIC) in collaboration with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to support the advanced nuclear reactor community. The VTB involves teams from both Idaho National Laboratory and Argonne National Laboratory and aims to provide example models for a broad range of both current and future advanced reactor designs. A feature of the VTB is the automatic testing of these models to ensure continued functionality as simulation tools are further developed. The VTB and the advanced reactor models documented there are important resources for this initiative. This work describes the inclusion of a new model on the VTB---a High Temperature Engineering Test Reactor (HTTR) steady-state model \cite{LABOURE2023109838}.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

On Control Schemes for Grid-Forming Inverters

Grid-forming inverters can collaboratively regulate the voltage and frequency of a microgrid. These inverters are expected to be seen more in the future microgrid, undertaking the roles of the conventional power generators. Like conventional power generators, they must balance supply and demand, contribute to power-sharing between inverters and other power generators, and restore the voltage and frequency after any power changes. These technical challenges are typically achieved through different primary and secondary controllers. This article reviews some state-of-the-art primary and secondary control schemes for the emerging technology of grid-forming inverters. First, the typical control schemes for grid-following inverters are briefly presented for the continuity of the discussion on the roles of the grid-forming inverters in future power-grid, followed by two basic control schemes for the grid-forming inverters. Then, two decentralized power-sharing techniques, i.e., droop, and virtual inertia, are analyzed. The technical challenges for inverter synchronization and some control methods are discussed afterward. Finally, several secondary control methods for the voltage and frequency restoration in grid-forming inverters are reviewed.

Sadeque, Fahmid↗
DOE OSTI2025

Do Research Software Engineers and Software Engineering Researchers Speak the Same Language?

Anecdotal evidence suggests that research software engineers (RSEs) and software engineering researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how software engineering fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. In conclusion, our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowdsourced extension and validation in the future.

Kehrer, Timo [Univ. of Bern (Switzerland)] (ORCID:↗
DOE OSTI2021

Proceedings of RIKEN BNL Research Center Workshop: Small-x Physics in the EIC Era [Slides]

Understanding the high energy limit of hadronic and nuclear collisions is at the forefront of nuclear and particle physics. When boosted to ultrahigh energies, all hadrons and nuclei eventually transform into a universal form of matter called the Color Glass Condensate (CGC). The CGC is characterized by the high density (saturation) of small-x gluons which leads to distinct experimental signatures. Tantalizing hints of the CGC have been observed at HERA, RHIC and the LHC, but the prospects for the discovery of the CGC are more promising at future experiments such as the Electron-Ion Collider (EIC) at BNL Indeed, according to the National Science Academy report published in 2019, one of the three major goals of the EIC is to address the nature of the gluon saturation. (The other two are the mass and spin structure of the nucleons.) With this in mind, the small-x community is gearing up to meet the challenges of the EIC era. Over the past several years, there has been impressive progress in the next-to-leading order (NLO) calculations in the CGC framework of various observables such as single hadron production in proton-nucleus collisions, inclusive and exclusive dijet and trijet production in Deep Inelastic Scattering (DIS), jet-plus-photon production in DIS, etc. We expect that NLO calculations will be the standard tool to confront future experimental data at the EIC. Another emerging trend of the community is the interplay between small-x physics and spin physics. The RHIC result for the gluon helicity ΔG has underscored the necessity to understand the longitudinal spin structure of the proton at small-x. There have been theoretical indications that a significant fraction of spin and orbital angular momentum is stored in the small-x region. As for the transversely polarized proton, a surprising new connection between the gluon Sivers function at small-x and the QCD Odderon has been pointed out and its implications at the EIC has been discussed. In view of these developments, we think it is timely to organize a dedicated workshop on small-x physics to summarize the present status of the field and to discuss future directions. A major focus of this workshop will be to identify outstanding problems that could significantly benefit from collaborative efforts amongst scientists working on formal, phenomenological, and computational aspects of small-x physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Integrated Design of Ultradurable, Low CO 2 Alternative Binder Systems via Machine Learning

This ARPA-E project developed a machine learning tool to use in formulation design of cementitious binders for concrete having 50% less embodied CO 2 and possessing twice the durability compared to concrete based on ordinary portland cement (OPC) binders. The technical focus was on limestone/calcined clay cement (LC3), the leading replacement for OPC. Here, hierarchical machine learning (HML) was used to model the flowability, set time, strength, and durability of LC3 concrete. This methodology identifies latent variables derived from domain knowledge and empirical models that develop an accurate model for a response surface from small datasets. For the flowability metric, particle packing was a dominant factor, while strength and durability were both strongly determined by the fraction of metakaolin and the water:solids ratio. Under constraints of water:binder ratio, material performance metrics, embodied CO 2 , and cost per tonne of OPC, multi-objective optimization was used to design binders parameterized by the mineral composition replacing OPC, particle size distributions, and water:solids ratio. The trained algorithm was able to predict multiple mixes met these performance criteria, and experimental testing validated the predictions. The model demonstrated here is relevant for North America, where pure kaolin deposits are found broadly. The approach is being taken forward into commercial application by Ansatz AI, a materials informatics company founded by PI Washburn and co-PI Poczos. Through collaborations with the cement and concrete industry, and funding from SBIR programs, a commercial software will be developed in future research.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Impact of Reconstruction in Pandora on Sensitivity to the Low-Energy Excess Signal at MicroBooNE

With the MicroBooNE collaboration’s first low-energy excess (LEE) searches complete and twice the amount of data to analyse in future iterations, it is imperative that we begin considering the potential analysis upgrades that can be achieved, such as those in the event reconstruction level. This note identifies the areas in Pandora reconstruction framework that could be improved upon, and evaluates their impact through cheated selections using MicroBooNE’s exclusive pionless LEE search as a benchmark. Vertexing and proton reconstruction improvements are demonstrated to have significant potential benefits in terms of sensitivity to the LEE signal and thus should be prioritised. Sensitivity projections for the full 10.1e20 POT are 3.2σ and 2.3σ for vertex and proton cheated selections respectively, and 3.3σ for the combined effect of both, compared to 2.1σ in the absence of the cheated selections.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

ORNL Portable Diagnostic Package for Evaluating Mirror Performance

Oak Ridge National Laboratory (ORNL) and Realta Fusion successfully executed a one-year collaborative effort to deploy the Portable Diagnostic Package (PDP) on the Wisconsin HTS Axisymmetric Mirror (WHAM) and to assess future diagnostic needs. The PDP consists of an optical emission spectroscopy (OES) subsystem and a Thomson scattering (TS) subsystem. Within the first two months, the PDP OES subsystem was installed, integrated with WHAM timing, and handed over to trained Realta personnel. It immediately delivered multi-channel impurity surveys and the first spatially resolved plasma-rotation profiles inferred from C III Doppler shifts. The TS subsystem was commissioned within the first six months; transportation of the class IV laser system, approval of the safety plan, and the integration with WHAM timing with 100 ns resolution. The TS subsystem provided the first localized electron temperature measurements in WHAM commissioning shots. Leveraging these results, the team has discussed a diagnostic concept map for Realta’s next-step facility, Anvil, identifying line-of-sight constraints, required temporal resolution, and hardware upgrades.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher-dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. Additionally, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.

42 ENGINEERING↗
DOE OSTI2026

Extensive analysis of reconstruction algorithms for DESI 2024 baryon acoustic oscillations

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ∼2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the “standard” reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove non-linearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Roadmap on basic research needs for laser technology

Motivated by the profound impact of laser technology on science, arising from an increase in focused light intensity by seven orders of magnitude and flashes so short electron motion is visible, this roadmap outlines the paths forward in laser technology to enable the next generation of science and applications. Despite remarkable progress, the field confronts challenges in developing compact, high-power sources, enhancing scalability and efficiency, and ensuring safety standards. Future research endeavors aim to revolutionize laser power, energy, repetition rate and precision control; to transform mid-infrared sources; to revolutionize approaches to field control and frequency conversion. These require reinvention of materials and optics to enable intense laser science and interdisciplinary collaboration. The roadmap underscores the dynamic nature of laser technology and its potential to address global challenges, propelling progress and fostering sustainable development. Ultimately, advancements in laser technology hold promise to revolutionize myriad applications, heralding a future defined by innovation, efficiency, and sustainability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

First Estimation of Model Parameters for Neutrino-Induced Nucleon Knockout Using Simulation-Based Inference

To enable an accurate determination of oscillation parameters, accelerator-based neutrino experiments require detailed simulations of nuclear interaction physics in the GeV regime. While substantial effort from both theory and experiment is currently being invested to improve the fidelity of these simulations, their present deficiencies typically oblige experimental collaborations to resort to empirical tuning of simulation model parameters. As the precision requirements of the field continue to become more stringent, machine learning techniques may provide a powerful means of handling corresponding growth in the complexity of future neutrino interaction model tuning exercises. To study the suitability of simulation-based inference (SBI) for this physics application, in this paper we revisit a tuned configuration of the GENIE neutrino event generator that was originally developed by the MicroBooNE collaboration. Despite closely reproducing the adopted values of four physics parameters when confronted with the tuned cross-section predictions as input, we find that our trained SBI algorithm prefers modestly different values (within MicroBooNE's assigned uncertainties) and achieves slightly better goodness-of-fit when inference is run on the experimental data set originally used by MicroBooNE. We also find that our trained algorithm can create a fair approximation of an alternative neutrino scattering simulation, NuWro, that shares only a subset of its physics model parameters with GENIE.

Tame-Narvaez, Karla [Fermilab] (ORCID:000000022249↗
DOE OSTI2023

Energy Flexibility-Environmental Outcomes Tradeoffs Workshop Report and Research Roadmap

The U.S. Department of Energy (DOE) and Norway’s Royal Ministry of Petroleum and Energy signed an Annex to a previously signed memorandum of understanding (MOU) in February 2020 to collaborate on hydropower research and development (R&D). This MOU Annex has brought together the DOE’s Office of Energy Efficiency and Renewable Energy Water Power Technology Office and the Norwegian Research Center for Hydropower Technology (HydroCen) to plan and coordinate hydropower R&D activities to increase our understanding of hydropower’s role in the future energy grid and how to minimize and mitigate the subsequent environmental impacts. As part of this MOU Annex, hydropower researchers from the U.S. and Norway have come together to conduct collaborative research on hydropower markets and value, hydropower plant capabilities and constraints, monitoring and control technologies, environmental design solutions, environmental impacts and tradeoffs, flexible operation and planning, and technology innovations. This report presents background information on hydropower environmental regulation in the U.S. and Norway and summarizes content and conclusions from this series of two, three-hour workshops on hydropower generation flexibility and environmental outcomes, that included structured discussions used to identify research priorities and collaborative research opportunities.

13 HYDRO ENERGY↗
DOE OSTI2021

Integrated TBC/EBC for SiC Fiber Reinforced SiC Matrix Composites for Next Generation Gas Turbines (Final Report)

In this ambitious project, a significant challenge preventing widespread use of ceramic matrix composites in hot section components of gas power generating turbines was addressed. Hot section components made from SiC fiber reinforced SiC matrix composites (SiC/SiC composites), have the potential to achieve a combined cycle energy efficiency of above 65%. To achieve this efficiency, the temperature of the combustion gases on the turbine component surface is expected to reach as high as ~1700ºC (3100ºF). However, SiC/SiC composites suffer from volatilization at high temperature under high velocity steam environment – typical in a turbine hot chamber. This integrated collaborative project, between two research groups at Clemson University and a team of engineers and scientists at GE Gas Power, focused on design, fabrication and evaluation of a new graded environmental barrier coating. Using the excellent compositional tailorability of polymer derived ceramics (PDCs), we developed an integrated EBC/BC that smoothly transitions from bottom SiC/SiC bond interface to hermetic sesquioxide (Y 2 O 3 ) top surface. This smooth compositional transition avoided the sharp CTE mismatch at interfaces to greatly reduce the thermal stress. The developed EBC is compatible with and bonds well with the bond coat that GE uses. The top hermetic oxide surface ensures low oxygen transportation and volatilization rates under the high velocity steam environment. The graded microstructure with increasing sesquioxide concentration also decreases the amount of Si and C elements near the top hermetic sesquioxide layer. Coatings made using a process suitable for manufacturing – atmospheric plasma spraying was demonstrated. The microstructure of coatings made using this process were fully characterized. Finally, the performance of these coatings was investigated under the most demanding conditions for this application. Specifically, high temperature, high velocity steam exposure; thermal shock test; and thermal cycling tests. The graded coatings performed exceptionally well in all these tests. Specific suggestions for future development in this area have also been identified. In addition to the excellent technical progress, this project helped initiate a robust collaboration between the research groups at Clemson and GE Gas Power. It also provided a high-quality learning and mentoring opportunity for the three post-doctoral research associates and two UG students at Clemson University who worked on this project. All proposed tasks, sub-tasks were completed, and all milestones achieved.

03 NATURAL GAS↗
DOE OSTI2022

Intentional and Responsible Energy Transitions: Integrating Design Choices in the Pursuit of Carbon-Neutral Futures

The transitions of energy systems to carbon neutrality are among is one of the most ambitious, complex, uncertain, significant, and transformative projects in human history. They will therefore demand extraordinarily thoughtful, informed, and careful leadership. In recent years, this project has reached an important turning point, with widespread agreement to decarbonize energy systems by 2050. We argue that the next step is to engage leaders in doing the work necessary to achieve that goal. Specifically, we argue that regional leaders, across sectors, should build collaborative initiatives to intentionally and responsibly design and advance regional energy transitions. These transitions will entail navigating uncertain and unknown terrain, requiring diverse organizations and communities to work together to chart and construct potential pathways, assess their implications, and collectively work to make progress. To address these challenges, we recommend adopting region-first, anticipatory, justice-oriented approaches. In such approachesthis process, regions can collaboratively identify, deliberate, design, and navigate toward endpoints along complex and shifting pathways that recognize that design will matter. Efuture energy systems will deeply shape and be shaped by the futures of both societies and economies. Transitions will therefore need to be inclusive, to provide broad public input into both how to proceed with transitions and the goals that guide their design.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Collaboration and Negotiation

Collaboration and Negotiation is a critical high-level function of an Autonomous Intelligent Cyber-Defense Agent (AICA) that enables communication among agents, central cyber C2, and human operators. Maintaining the Confidentiality, Integrity, and Availability (CIA) triad while achieving mission goals requires stealthy AICA agents to exercise: 1) minimal communication as needed for avoiding detection, 2) verification of information received with possibly limited resources, and 3) active learning during operations to address dynamic conditions. Moreover, negotiations to jointly identify and execute a Course of Action (COA) solution will require building consensus under distributed and/or decentralized multiagent settings with information uncertainties. This chapter presents algorithmic approaches for enabling the collaboration and negotiation function. Strengths and limitations of potential techniques are identified, and a representative example is illustrated. Recommendations for future development are also discussed.

Chatterjee, Samrat↗
DOE OSTI2021

Data Science Meets Physical Organic Chemistry

At the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate. In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔ G ‡ = - RT ln[( S )/( R )], where ( S ) and ( R ) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way. Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design. In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗