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In the summer and fall of 2023, the University of Texas (UT) Deepwater Hydrate Coring Expedition (UT-GOM2-2) drilled, cored, made downhole measurements, and analyzed samples from the seafloor to the base of the gas hydrate stability zone at Site H, in the Walker Ridge Protracted Area Block 313 (Site H, WR313), in the Terrebonne Basin, deepwater Gulf of America (Gulf of Mexico).
The National Aeronautics and Space Administration (NASA) is a United States (US) federal agency that oversees US space exploration and aeronautical research. NASA's primary launch site, Kennedy Space Center (KSC) is located along the east coast of Florida, on Cape Canaveral and the western Atlantic Ocean. The natural environment within KSC's large land boundaries, not only functions as an extensive safety buffer-area, it performs simultaneously as a wildlife refuge and a national seashore. In the early 1960s, NASA was developing KSC for rocket launches and the US was establishing an awareness of, and commitment to protecting the environment. The US began creating regulations that required the consideration of the environment when taking action on federal land or with federal funds. The timing of the US Endangered Species Act (1973), the US National Environmental Policy Act (1972), coincided with the planning and implementation of the US Space Shuttle Program. This resulted in the first efforts to evaluate the impacts of space launch operation operations on waterways, air quality, habitats, and wildlife. The first KSC fauna and flora baseline studies were predominantly performed by University of Central Florida (then Florida Technological University). Numerous species of relative importance were observed and sea turtles were receiving regulatory review and protection as surveys by Dr. L Ehrhart (UCF) from 1973-1978 described turtles nesting along the KSC beaches and foraging in the KSC lagoon systems. These data were used in the first NASA Environmental Impact Statement for the Space Transportation System (shuttle program) in 1980. In 1982, NASA began a long term ecological monitoring program with contracted scientists on site. This included efforts to track sea turtle status and trends at KSC and maintain protective measures for these species. Many studies and collaborations have occurred on KSC over these last 45 years with agencies (USFWS, NOAA, NAVY), students, and universities (UCF, University of Toronto, Texas A&M, UF). This presentation will review the various studies and collaborations on sea turtles at KSC that include: nest distributions and success, stranding network development, aerial survey testing for nest counts, predator control assessments, the earliest baseline blood chemistry health determinations on nesting females, stress hormones in nesting females, multi-year study of hatchling sex ratios, genetics, species composition, abundance and distribution of in-water juveniles, turtle cold stun response, exterior lighting impacts and control, and satellite tag tracking of post-nesting turtles in the vicinity of near shore shoals and sand mining sites. Through these studies, monitoring, and recommendations, KSC has provided excellent stewardship and protection of the local environment. While conducting its space program mission, KSC has also made significant contributions of information for agencies charged with the conservation and management of these species
Light- and elevated-temperature-induced degradation (LeTID) was first discovered in multicrystalline Si (mc-Si) solar cells and was initially attributed to metal impurities. Later, LeTID was reported in Czochralski (Cz) and float-zone (FZ) Si, and is considered as an important efficiency loss mechanism in p-type passivated emitter rear contact (p-PERC) Cz Si solar cells. LeTID causes ~10% relative and permeant efficiency losses in these cells in warmer climate regions where the module temperature is > 50 °C. Unlike light-induced degradation (LID), which is also observed in p-PERC cells, LeTID is slower and takes weeks to months in the field to saturate. Another difference compared to LID is that regeneration in LeTID proceeds very slowly, and field regeneration could take > 25 years — essentially the life of the module. Unlike B-O defects that are responsible for LID, neither B nor O impurities are directly involved in LeTID. LeTID appears to be unique to p-type Si, and is also observed in Ga-doped Si. Currently, most experimental evidence relates LeTID to the injection of hydrogen present in the dielectric surface passivation layers, such as SiN x and Al 2 O 3 , into the monocrystalline Si (c-Si) bulk during the fast-firing step. The involvement of hydrogen is further strengthened by controlled studies that show that increasing the amount of hydrogen in the dielectric during fast-firing increases the degree of LeTID. Similar to LID, a regeneration process has been discovered for LeTID. Regeneration of LeTID defects occurs when samples are exposed to 2–4 Suns illumination at elevated temperatures of 140–220 °C for 2–15 hr. Given the slower kinetics of LeTID and sample regeneration compared to LID, this poses a challenge for the manufacturing and field reliability of p-PERC cells, which will be the leading photovoltaic technologies over the next decade. Therefore, there is a need to understand LeTID and develop strategies to mitigate this effect. The defect responsible for LeTID has been extensively studied with over 100 publications, but direct spectroscopic evidence of this defect’s structure is lacking. Without an atomistic understanding of the LeTID defect, it is difficult to assess the long-term efficacy of the current industrial mitigation strategies. This, in turn, has implications on energy production for tens of gigawatts of these cells that will be deployed yearly worldwide. Using electron paramagnetic resonance, we identified a defect associated with LeTID with a g-value of 2.006, which we attribute to an Si dangling bond in an extended defect such as a vacancy agglomerate with H possibly within or in close vicinity. These vacancy agglomerates are likely created during the firing process, during which time H atoms are also injected into the bulk from the hydrogenated SiN x dielectric layer. Our atomistic-level insight shows that the LeTID defect can be mitigated by targeted intrinsic defect engineering of the c-Si material through a slower pull rate of the Cz ingot or 1000 °C oxygen ambient processing of the Si wafer to reduce the vacancy concentration. This project was a collaborative effort between the Colorado School of Mines and the National Renewable Energy Laboratory.
Meeting the increased demand for rare earth elements and beyond primary mined ore. Secondary sources like phosphogypsum, coal fly ash, used nuclear fuel, and electronic waste are concentrated sources of critical minerals. One strategy for a resilient supply chain is to decentralize sourcing and hydrometallurgical processing of metals. Rotating packed beds (RPBs) for solid-phase extraction and liquid-liquid extraction are gaining momentum for their ability to increase process throughput by 5-10x the state of the art. In conclusion, this review discusses advances in hydrometallurgical RPB processes and opportunities for implementation in distributed metals production.
Recent advances in environmental genomics have provided unprecedented opportunities for the investigation of viruses in natural settings. Yet, our knowledge of viral biogeographic patterns and the corresponding drivers is still limited. Here, we perform metagenomic deep sequencing on 90 acid mine drainage (AMD) sediments sampled across Southern China and examine the biogeography of viruses in this extreme environment. The results demonstrate that prokaryotic communities dictate viral taxonomic and functional diversity, abundance and structure, whereas other factors especially latitude and mean annual temperature also impact viral populations and functions. In silico predictions highlight lineage-specific virus-host abundance ratios and richness-dependent virus-host interaction structure. Further functional analyses reveal important roles of environmental conditions and horizontal gene transfers in shaping viral auxiliary metabolic genes potentially involved in phosphorus assimilation. Our findings underscore the importance of both abiotic and biotic factors in predicting the taxonomic and functional biogeographic dynamics of viruses in the AMD sediments.
Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multistage machine learning (ML) approach to streamline the discovery and optimization of complex multimetallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then, the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we demonstrate an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially benchmark in electrocatalysts research.
Molten Salt Thermal energy storage (TES) is essential for concentrating solar power (CSP) plants, enhancing their capacity factor and dispatchability to ultimately reduce electricity costs. This technology is becoming more important for other technologies including nuclear power, thermal-electrical storage, and concentrating solar thermal (CST). While molten salt TES tanks have been successfully deployed in lower-temperature parabolic-trough plants, the current state-of-the-art central receiver systems operating at temperatures up to 565 degrees Celsius have experienced costly and frequent failures. These failures are associated with the immaturity of the technology and problems in tank design, fabrication, commissioning and, sometimes, aggressive operation. A primary contributing factor is the absence of a dedicated technical standard for high-temperature molten nitrate TES tanks; designers must currently rely on existing codes like API 650 and ASME Section II, which are insufficient because they fail to account for the unique demands of high temperatures, thermal cycling, and transient conditions.
Achieving full decarbonization of all economic sectors remains a challenge, especially in niche markets. For example, remote communities and industrial or mining activities detached from the main electric grid heavily rely on fossil fuels, similar to urban and industrial microgrids with combined heat and power needs. A combination of renewables and energy storage is often not suitable due to cost, reliability, intermittency, and large storage requirements. Small nuclear reactors with a flexible purpose could serve these applications. Microreactors (MR) are a class of reactors that are compact, factory manufactured, transportable, and self-regulating. Typically, they generate much less power than their large reactor counterparts. The main advantages of microreactors include the versatile nature of the energy produced, the reliability of supply, and freedom from having to transport and store large quantities of fuels on-site, coupled with the absence of dependence on an electrical grid. A strong business case is needed to move from the microreactor prototype to the commercialization phase. In fact, fossil fuels are still relatively inexpensive, and in the near term, carbon credits will be available to virtually compensate for emissions. For microreactors, one of the main costs in operation and maintenance (O&M) is their staffing levels. In this study, we investigate how to optimize the number (and thus the cost) of workers, moving from a traditional, fully manned, on-site personnel approach to an unmanned, remote personnel approach. We examine four different staffing models that can be implemented as the technology matures and evolves. We estimate the staffing needs of each model and build a business case to justify the substitution of on-site personnel with adequate technologies. To do so, we propose a cost model to quantify potential cost reductions from automating O&M activities. The model accounts for both the reduction in cost derived from the reduced number of full-time-equivalent (FTE) employees and the increase in cost derived from the need to buy new control hardware as needed. Applying the cost model that we created to different scenarios, an on-site O&M cost reduction exceeding 80% can be expected. Additionally, we found that it is more impactful to focus on automating routine O&M tasks rather than attempting to automate transient management (shutdowns, restarts, monitoring condition deviations). In fact, transients typically account for less than 1% of the total FTE time spent on the reactors.
Abstract Investigations of abiotic and biotic contributions to dissolved organic carbon (DOC) are required to constrain microbial habitability in continental subsurface fluids. Here we investigate a large (101–283 mg C/L) DOC pool in an ancient (>1Ga), high temperature (45–55 °C), low biomass (10 2 −10 4 cells/mL), and deep (3.2 km) brine from an uranium-enriched South African gold mine. Excitation-emission matrices (EEMs), negative electrospray ionization (–ESI) 21 tesla Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), and amino acid analyses suggest the brine DOC is primarily radiolytically oxidized kerogen-rich shales or reefs, methane and ethane, with trace amounts of C 3 –C 6 hydrocarbons and organic sulfides. δ 2 H and δ 13 C of C 1 –C 3 hydrocarbons are consistent with abiotic origins. These findings suggest water-rock processes control redox and C cycling, helping support a meagre, slow biosphere over geologic time. A radiolytic-driven, habitable brine may signal similar settings are good targets in the search for life beyond Earth.
The sizes of the basins of attraction on the potential energy surface are helpful indicators in determining the experimental synthesizability of metastable phases. In principle, these basins can be controlled with changes in thermodynamic conditions such as composition, pressure, and surface energy. Herein, we use random structure sampling to computationally study how alloying smoothly perturbs basin of attraction sizes. The TaC 1-x N x pseudobinary is an ideal test system given the structural and polymorphic contrast of its parent compounds and their technological relevance as epitaxial substrates for Al 1-x Ga x N. While we find limited thermodynamic stability across all computationally observed phases, random structure sampling shows a significant composition region where the rocksalt basin dominates. As such, we predict the potential for the nonequilibrium synthesis of metastable rocksalt TaC 1-x N x alloys as substrates for Al 1-x Ga x N. At higher nitrogen concentrations, other low-energy metastable polymorphs emerge that continue to retain the hexagonal close packing suitable for III-N growth. Confidence in these trends was established through uncertainty quantification of the basin sizes and energy distributions; such analysis utilized the Beta and Dirichlet distributions. In conclusion, we also find (a) polymorph basin sizes can be rationalized in terms of energetic preferences for different coordination environments; and (b) basin sizes universally shrink with increasing nitrogen content, making the system more prone to amorphous growth.
In the United States, hundreds of thousands of undocumented orphan wells have been abandoned, leaving the burden of managing environmental hazards to governmental agencies or the public. These wells, a result of over a century of fossil fuel extraction without adequate regulation, lack basic information like location and depth, emit greenhouse gases, and leak toxic substances into groundwater. For most of these wells, basic information such as well location and depth is unknown or unverified. Addressing this issue necessitates innovative and interdisciplinary approaches for locating, characterizing, and mitigating their environmental impacts. Our survey of the United States revealed the need for tools to identify well locations and assess conditions, prompting the development of technologies including machine learning to automatically extract information from old records (95%+ accuracy), remote sensing technologies like aero-magnetometers to find buried wells, and cost-effective methods for estimating methane emissions. Notably, fixed-wing drones equipped with magnetometers have emerged as cost-effective and efficient for discovering unknown wells, offering advantages over helicopters and quadcopters. Efforts also involved leveraging local knowledge through outreach to state and tribal governments as well as citizen science initiatives. These initiatives aim to significantly contribute to environmental sustainability by reducing greenhouse gases and improving air and water quality.
Triboelectrically-charged objects may create threshold sparks, electrostatic discharge (ESD) events, to equilibrate charge between themselves and other relatively charged objects. ESD events exhibit many complex physical phenomena. They are a nexus of several fields of physics with disparate characteristic scales: plasma physics, chemical kinetics, hydrodynamics, circuit models, etc. These scales can span many orders of magnitude from the varied collisions thermalizing information within a plasma on the $\mathcal{O}(fs/ps)$ to the physical size of the plasma channel on the $\mathcal{O}(100µm)$, to the speed of a nonlinear hydrodynamic wave propagating at $\mathcal{O}(µm, ns)$. These threshold ESD events may occur in situations of programmatic importance, delivering energy and power profiles to a “victim load” generating deleterious consequences. To predict and mitigate these consequences we must answer questions about the spark’s energy budget: how much energy goes into producing the spark channel; how much gets radiated away; how much energy is advected away into the hydrodynamics; and how much energy is delivered to a victim load. An ESD simulation toolset has been created and evolved in order to answer these questions. An appropriate, physics guided implementation for simulation can be done by gaining insight into its constituent physics and leveraging that intuition to choose a suitable numerical operator. We examine in detail the chemical kinetics, circuit discharge, and hydrodynamics to deter mine dominant regimes, values, timescales, and interactions to uncover the underlying physical dynamics. We also examine and propose model reduction schemes for high-dimensional chemical kinetics. We use past and current work with experimentally validated and theoretically-verified hydrodynamics to calculate applicability limits of the non-ionizing strong shock limit. We quantify the energy budget from a hydrodynamic perspective and demonstrate that a significant fraction of the stored energy is “earmarked” for hydrodynamic advection as an energy terminus. Lastly, we combine the constituent physics of an ESD event (chemical kinetics, circuit model, and hydrodynamics) into a cohesive, actionable toolset and obtain promising results from an isothermal test case. We then propose a viable, modular evolution of the ESD toolset based upon the performed examination of the physics uncovering dominant physical scales and the stiffness of the compositional differential system.
Why are Space Resources and Their Use Important? There are many reasons why people and nations want to explore space, and there are many different ways in which space can be explored. A critical interface linking both the ‘why’ and the ‘how’ of space exploration is the identification, extraction, and use of resources in space. In both NASA’s Journey to Mars: Pioneering Next Steps in Space Exploration released in October of 2015 and the Global Exploration Roadmap released in August 2013, the ability to find, quantify, extract, process, and use space resources was identified as a critical objective by NASA and over 14 other space agencies for achieving affordable and sustainable human exploration beyond Earth’s orbit, encouraging and creating new commercial entities and markets based on space activities, and increasing the terrestrial economy and quality-of-life benefits for all humankind. Robotic and especially human space exploration up to this point in time can be considered to be ‘Earth reliant’ in that everything needed to support and enable the mission is launched from Earth. The past Apollo missions to the Moon and the International Space Station currently in orbit above the Earth all rely on hardware, habitats, power, transportation, and life support consumables sent from Earth to keep the crew alive and working. As the distance from Earth increases, the cost of transportation and the risks due to failures and logistics disruptions also increases. By using resources found at the site of exploration to make mission critical consumables (such as propellants, fuel cell reactants and life support commodities), spare parts, and infrastructure needed to support surface activities (such as landing pads, roads, habitats/shelters, and power/thermal systems) commonly referred to as In Situ Resource Utilization, a significant amount of launch mass and cost can be saved and risk reduced. The ability to do these things also changes how exploration is performed from Earth Reliant to NASA’s goal of ‘Earth Independent’ exploration. After the US Apollo program was over, people began to recognize that it had been an inspiration for a generation of young students to go into Science, Technology, Engineering and Math (STEM) fields, and that the US industry and economy had significantly grown and lived off these students and the technological advances made to achieve these missions for decades. Space exploration should no longer be focused solely on scientific advancement or national pride, but for economic growth and population standard of living advancement. Considering the economic value of human exploration and how it could expand the economy of the US was brought to full light in a speech by John Marburger, the director of US Office of Science and Technology Policy under George W Bush at the Goddard Symposium in 2006. In his speech he highlighted that the ultimate goal was not to just Explore space but to Use space for the benefit of mankind. He further stated that the use of off-planet resources, in this case from the Moon, should be a critical architectural consideration for human space exploration to make it more affordable and sustainable. This could be achieved in two ways. The first is to start by encouraging and commercializing the extraction and production of transportation and life support related products from space resources, which could than lead to other resource uses once an affordable transportation architecture is established. The second is to encourage the spin-in of terrestrial technologies into space applications and spin-off of space technologies into terrestrial applications to increase the efficiency and profitability of terrestrial industries.
Rare earth elements (REEs) are critical to numerous technologies; however, a combination of increasing demand, environmental concerns, and monopolistic marketplace conditions has spurred interest in boosting the domestic REE production from sources such as coal utilization byproducts. The economic viability of this approach requires rapid, inexpensive, and sensitive analytical techniques capable of characterizing the REE content during resource exploration and downstream REE processing (e.g., analyzing REE separation, concentration, and purification production steps). Luminescence-based sensors are attractive because many REEs may be sensitized to produce element-specific emission. Hence, a single material may simultaneously detect and distinguish multiple REEs. Metal–organic frameworks (MOFs) can sensitize multiple REEs, but their viability has been hindered by sensitivity and selectivity challenges. Understanding how the MOF structure impacts the REE sensing efficacy is critical to the rational design of new sensors. Here, we evaluate the sensing performance of seven different anionic zinc-adeninate MOFs with different organic linkers and/or structures for the visible-emitting REEs Tb, Dy, Sm, and Eu. The choice of a linker determines which REEs are sensitized and significantly influences their sensitivity and selectivity against competing species (here, Fe(II) and HCl). For a given linker, structural changes to the MOF can further fine-tune the performance. The MOFs produce some of the lowest detection limits (sub-10 ppb for Tb) reported for the aqueous sensitization-based REE detection. Importantly, the most selective MOFs demonstrated the ability to sensitize the REE signal at sub-ppm levels in a REE-spiked acid mine drainage matrix, highlighting their potential for use in real-world sensing applications.
A strong societal and political drive is motivating the development and optimization of novel energy conversion and storage systems for decarbonization. The successful implementation of solid state devices such as fuel cells and secondary batteries depends, however, on achieving ambitious targets in terms of performance, reliability and cost competitiveness. Research and technology are addressing these needs through a holistic approach including exploration of new materials and nanoarchitectures, as well as system engineering. These significant efforts require the support of appropriate characterization tools capable of assessing nanometer-scale phenomena such as concentration profiles of ionic and electronic charges, local chemical compositions and their evolution over time across interfaces. This roadmap provides an overview of selected advanced characterization techniques for energy materials and devices. Specific focus is put on in situ/operando methods for probing electrochemical phenomena in real-time under realistic working conditions. Experts in the field provide an extensive review of the current state of the art in 2025 and the current and future challenges for the characterization of local chemistry and kinetics in the bulk of the material, in nanoarchitectures (e.g. thin films) and at the interfaces (e.g. grain boundaries, phase contacts, solid/liquid and solid/gas interfaces) . The aim is to provide a detailed guide to the techniques, describing opportunities and bottlenecks for their practical deployment and examples of successful applications.
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.
The nature of connectivity between constituent atomic or molecular building blocks is fundamental in shaping the properties and functionality of materials. The extrapolation of emergent interatomic interactions to enable functional materials has driven transformative technological advancements. However, the bonding interactions used in material design have been largely static since the emergence of dynamic covalent chemistry ~30 years ago. Here we demonstrate that non-covalent chalcogen bonding (Ch-bonding) is a distinct mode of interatomic connectivity for constructing functional materials by design. This is established by leveraging self-complementary assembly of 1,2,5-telluradiazole moieties to construct a honeycomb-type permanently porous Ch-bonded organic framework, assembled and stabilized solely through non-covalent Te...N contacts. Empirical and computational studies of electronic structure, structural healing and lattice dynamics highlight the pi-type electronic communication, controlled assembly and modulated lattice dynamics in Trip3Tez-I arising directly from the unique nature of the Te...N Ch-bonding that holds substantial implications for next generation crystalline semiconductors. In addition to introducing a distinct class of permanently porous frameworks, this work establishes Ch-bonding as a programmable molecular tool for constructing functional materials with distinct properties.
Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.