Title: Using Computation to Accelerate Materials Engineering, from the Atomistic to Device Scale
Abstract: Computation is a third "pillar of science" beyond the centuries-old approaches of experiment and theory. In a sense, computation allows us to perform experiments directly on theories, understanding what the world would look like if a particular theory or approximation held exactly (or at least within numerical precision). Often, however, the size of that computational world is limited by the computing power available. The art and the power of computation comes, then, in convincing ourselves, and eventually our colleagues, that the hypothetical world we simulate is still similar enough to reality to tell us something meaningful about it.
In this talk, I will showcase some of my work over the past decade in utilizing computational science to advance photovoltaic materials and devices. First, I will dive down to the atomistic scale to discuss quantum mechanical simulation of individual point defects to design "perovskite-inspired" materials. Next, we will move out to the device scale, highlighting work on high-throughput simulation of full cells to infer properties of their layers and interfaces from just device-level measurements, avoiding the need for separate costly and time-consuming sample preparation and characterization. Throughout, I will highlight the importance of uncertainty quantification to robust collaboration across research paradigms.
Title: The Promise and Challenges of CdTe PV
Abstract: : CdTe photovoltaics serve as a successful existence proof to emerging PV technologies as the most scaled thin-film PV technology with low costs at utility-scale, impressive stability, and low embodied carbon. While perusing the record cell efficiency chart shows long periods without significant gains may cause some outside the CdTe community to think CdTe has stagnated, there has been significant scientific progress in the last decade. Historically, CdTe devices have had Voc < 900 mV. At the end of 2015, a record of 22.1% was reached that held for over seven years. Around the same time, NREL provided an experimental demonstration of Voc > 1V using a single crystal model system suggesting a new defect chemistry could address the long-standing photovoltage deficit. Transitioning this to scalable polycrystalline thin-film devices requires numerous simultaneous advances to improve over the historic Cu-based defect chemistry. This talk will discuss some of the changes and understanding that have led to the recent progress in efficiency gains as well as the outstanding needs and challenges the community still faces as it works to realize the voltage entitlement of CdTe and approach the detailed balance limit.
Title: Thermal Batteries with Thermophotovoltaic Heat Engines for Industrial Decarbonization
Abstract: Antora Energy is addressing the challenge of decarbonizing the industrial sector, which accounts for 30% of global carbon dioxide emissions. The sector's reliance on fossil fuels for continuous heat and power presents a significant barrier to reducing these emissions. Antora’s solution is a thermal battery system that charges with extremely cheap and abundant wind and solar electricity, stores the energy as heat in carbon blocks, and outputs reliable, on-demand heat and power for industrial facilities. This system features a solid-state heat engine comprising high-efficiency, thin-film, III-V thermophotovoltaic (TPV) cells coupled with a 1500°C thermal emission source. Antora has focused on the thermophotovoltaic performance and more recently on its cost-reduction, assembly into high-power density TPV modules, and integration into a large-scale thermal battery demonstration system that is now operational at an industrial site in California. This presentation will review the latest learnings and achievements on the path to market for this unique application of optoelectronics to energy storage and climate change mitigation.
Title: Pathways to Efficient and Stable Perovskite/Silicon Tandem Photovoltaics
Abstract: In this presentation I will discuss the multiple ways how monolithic perovskite/silicon can be fabricated, built from textured silicon heterojunction solar cells, with an emphasis on solution of the perovskite top cell. Bulk and contact passivation of the perovskite are instrumental to obtain a high performance, which can be obtained through molecular additive engineering. Combined, this recently led to a certified power conversion efficiency of 33.7% for a monolithic perovskite/silicon tandem solar cell. This will be followed by a discussion about the outdoor performance of such tandems and the need for robust and perovskite-compatible encapsulation to do so. I will then move on to discuss reliability aspects of such tandems under accelerated degradation tests such as damp-heat testing and reverse-bias degradation. I will conclude my talk with arguing how bifacial perovskite/silicon tandems aid in improved performance as well as stability, thanks to their reliance on narrow-bandgap perovskites for optimal performance.
Title: An Introduction to Corona Oxide Semiconductor Measurements
Abstract: Corona Oxide Semiconductor (COS) techniques were developed by IBM researchers in the 80’s and 90’s to characterize oxides, interface states and properties of the underlying silicon. KLA-Tencor developed the technology further, and it was deployed to leading semiconductor manufacturers in the timeframe 1997-2007. With modifications, it is possible that this technique could be helpful in the development of modern solar cells. We will review the basic source and measurement components used in COS and discuss how these might be used in PV.
Title: Rapid Advances in Inverted Perovskite Solar Cells
Abstract: Organic-inorganic hybrid halide perovskites have attracted significant R&D attention in the photovoltaic (PV) community as a competitive technology for future PV applications. Perovskite solar cells (PSCs) with an inverted (often referred to as p-i-n) structure are becoming increasingly attractive for commercialization due to the rapid increase in power conversion efficiency (PCE), easily scalable fabrication, reliable operation, and compatibility with various perovskite-based tandem device configurations. In this talk, I will discuss the key factors leading to recent rapid advances in developing efficient and stable p-i-n PSCs. Although there is still room for improvement in perovskite bulk materials optimization, interface engineeringtargeting either the bottom (buried) or top perovskite surface layerhas become the most critical and effective approach for advancing PSC performance. In the second part of my talk, I will discuss our recent progress toward understanding the link between indoor and outdoor PSC degradation behaviors. To push PSCs toward commercialization, it is critical to understand device reliability under real-world outdoor conditions where multiple stress factors (e.g., light, heat, humidity) coexist. Understanding the link between indoor and outdoor behaviors is necessary to help identify accelerated indoor testing protocols to quickly guide PSC development.
Title: Data-centric AI Foundation Models for Fleet-wide Data Imputation, Performance and Degradation Analysis of PV Systems
Abstract: PV systems represent a complex interplay of materials and the environment, involving intrinsically multi-modal datatypes. By leveraging existing data streams, technologically informed “AI for PV” can be developed using a “data-centric AI'' approach, as opposed to the “model centric AI” of things such as Large Language Models. Data-centric AI creates models which naturally address real-world questions of PV systems’ design, performance, and degradation. Data-centric AI challenges can be addressed by CRADLE distributed and high performance computing, data FAIRification, and recognizing that real-world PV fleets are a network of geospatiotemporal systems, best represented using spatiotemporal graph neural network (st-GNN) models with metadata feature vectors. A st-GNN model trained on geospatially distributed, time-series data, will utilize the intrinsic spatial and temporal coherence of the real-world PV systems. The model therefore encapsulates all the information about the PV fleet; it is both an st-graph and a knowledge graph (k-graph). This trained PV k/st-graph foundation model is literally a data-driven Digital Twin of the fleet of PV systems it trained on, and encompasses the full lifecycle of the PV systems. Data-centric k/st-graph deep learning AI enables generative data imputation, power forecasting, and performance loss rate determination. And it can be used to predict performance of PV systems not yet constructed.
Title:Scaling PV Inspections for a Growing Solar Industry
Abstract: Solar farm panels and components with aberrant heating patterns (“thermal anomalies”) are an important diagnostic tool for asset management. Solar PV inspections with detailed information about thermal anomalies can help in understanding a solar farm's health through time, with the optimization of remediation strategies, and even the prevention of catastrophic fires. But developing the processes and systems that can find anomalies from aerial thermography consistently and reliably at scale presents multiple challenges. For one, there is limited availability of domain expertise, compounded by the difficulty of making the raw imagery easy to explore, tag, and integrate with other data. Overcoming the scarcity of human expertise by leveraging machine learning (ML) and artificial intelligence (AI) methods brings its own set of issues, including how to ensure robustness to variability in the inputs, accuracy of the outputs, and computational efficiency. In this talk, we provide an overview of how Raptor Maps combines industry-expertise, digital tools, and ML/AI technologies to enable the delivery of solar PV inspections at scale.
Title: Grid Integration of Renewable Energy and Energy Storage
Abstract: Grid integration of renewable energy requires forward-looking planning process and increased emphasizes on reliability, resilience, and equity. Power-electronics based energy generation including solar, wind, distributed energy resources (DERs), and various types of grid-tied energy storage and emerging loads, are reshaping grid operator’s understanding on interconnection level performance and responses. Dr. Xiaoyuan Fan will present the ongoing work at PNNL related to power electronics, energy modeling and analysis, and a wide spectrum of grid stability evaluation tools (software and hardware) and technologies (sensor, wired/wireless communication, grid monitoring and control) in support of grid integration of solar, wind, energy storage.
Title: Weather is the New Fuel: Lessons Learnt from Tracking the World's Clouds, Smoke, Dust and More in the Quest for Improved Irradiance Data
Abstract: As solar PV emerges as the world's leading power source, understanding its rapidly fluctuating fuel, the weather, becomes increasingly important. At more than a terawatt of solar, irradiance and weather data are already driving design, investment decisions, performance analysis, asset, and grid forecasting for solar PV and increasingly for the associated growth in energy storage. Exponential growth of solar has required that irradiance data and models rapidly catch up in terms of their quality, availability, and scope. This presentation shares key learnings made across data science and meteorology in the pursuit of this goal. Science, data and practical considerations are reviewed for key physical processes including clouds, atmospheric content, aerosols (smoke, dust, etc.) and terrain. The impacts of snow soiling, dust soiling and solar eclipses are also reviewed.
Title: On the Role of Solar PV for the Energy-Industry Transition in the Americas
Abstract: With the growth of solar photovoltaics (PV) in recent years as the largest power source by capacity added, the energy-industry transition towards high sustainability is accelerating. However, the energy-industry systems of the Americas are largely lagging as fossil fuels still dominate the electricity generation mix and the system as a whole. Energy-industry transition pathways are developed for all countries of the Americas reaching 100% renewable energy (RE) by 2050 for all sectors including power, heat, transport, industry, and desalination. To benchmark the transition to 100% RE, the results are compared to current energy policies across the Americas. The results for the Americas indicate the significant potential to expand RE, especially solar PV, to reach the 100% RE target and fully defossilize each region’s economy. The levelized cost of electricity (LCOE) can be reduced from its current level of 72 €/MWh to 22 €/MWh in 2050, and the levelized cost of final energy (LCOFE) sees reductions from 52 to 31 €/MWh from 2020 to 2050. Widespread electrification across energy-industry sectors requires significant expansion of solar PV, which accounts for 80% of all electricity supply, leading to 14.1 TW of installed capacity as electricity generation increases from 5440 TWh in 2020 to 30,778 TWh in 2050. The dominating role of solar PV thus indicates that the future Americas energy-industry system can be characterized as a Solar-to-X Economy.
PLENARY SPEAKERS
Area 1: Rachel C. Kurchin, Carnegie Mellon University, USA
Rachel C. Kurchin is an Assistant Research Professor of Materials Science and Engineering at Carnegie Mellon University, where she leads the Accelerated Computation of Materials for Energy group. She performs both first-principles- and data-driven computation from the atomistic to the device scale with the goal of accelerating the development of new materials and devices to combat the climate crisis, particularly the electrification transition.Title: Using Computation to Accelerate Materials Engineering, from the Atomistic to Device Scale
Abstract: Computation is a third "pillar of science" beyond the centuries-old approaches of experiment and theory. In a sense, computation allows us to perform experiments directly on theories, understanding what the world would look like if a particular theory or approximation held exactly (or at least within numerical precision). Often, however, the size of that computational world is limited by the computing power available. The art and the power of computation comes, then, in convincing ourselves, and eventually our colleagues, that the hypothetical world we simulate is still similar enough to reality to tell us something meaningful about it.
In this talk, I will showcase some of my work over the past decade in utilizing computational science to advance photovoltaic materials and devices. First, I will dive down to the atomistic scale to discuss quantum mechanical simulation of individual point defects to design "perovskite-inspired" materials. Next, we will move out to the device scale, highlighting work on high-throughput simulation of full cells to infer properties of their layers and interfaces from just device-level measurements, avoiding the need for separate costly and time-consuming sample preparation and characterization. Throughout, I will highlight the importance of uncertainty quantification to robust collaboration across research paradigms.
Area 2: Matthew Reese, National Renewable Energy Laboratory, USA
Dr. Matthew Reese received his B.S. from Caltech and Ph.D. from Yale and is now a senior scientist and Distinguished Member of Research Staff at the National Renewable Energy Laboratory. His expertise spans diverse thin-film materials sets, including CdTe, organic PV, perovskites, and lightweight/flexible packaging solutions, as well as understanding roles of interfaces and morphology in thin-film devices, which he leverages to lead NREL’s CdTe team.Title: The Promise and Challenges of CdTe PV
Abstract: : CdTe photovoltaics serve as a successful existence proof to emerging PV technologies as the most scaled thin-film PV technology with low costs at utility-scale, impressive stability, and low embodied carbon. While perusing the record cell efficiency chart shows long periods without significant gains may cause some outside the CdTe community to think CdTe has stagnated, there has been significant scientific progress in the last decade. Historically, CdTe devices have had Voc < 900 mV. At the end of 2015, a record of 22.1% was reached that held for over seven years. Around the same time, NREL provided an experimental demonstration of Voc > 1V using a single crystal model system suggesting a new defect chemistry could address the long-standing photovoltage deficit. Transitioning this to scalable polycrystalline thin-film devices requires numerous simultaneous advances to improve over the historic Cu-based defect chemistry. This talk will discuss some of the changes and understanding that have led to the recent progress in efficiency gains as well as the outstanding needs and challenges the community still faces as it works to realize the voltage entitlement of CdTe and approach the detailed balance limit.
Area 3: Leah Y. Kuritzky, Antora Energy, USA
Leah Kuritzky (PhD Materials, UC Santa Barbara) is Head of Photovoltaics Operations at Antora Energy, and has focused her career on engineering solutions to climate change. She has a background in optoelectronics R&D, manufacturing, and reliability, and at Antora she puts her optoelectronics experience to use in thermal batteries. Antora uses renewable electricity to heat blocks of graphite to glowing hot temperatures >1500°C. Dr. Kuritzky has contributed to the R&D and manufacturing of Antora's high-efficiency thermophotovoltaics (TPV) that convert the stored heat back into electricity to deliver zero-carbon, on-demand power to customers.Title: Thermal Batteries with Thermophotovoltaic Heat Engines for Industrial Decarbonization
Abstract: Antora Energy is addressing the challenge of decarbonizing the industrial sector, which accounts for 30% of global carbon dioxide emissions. The sector's reliance on fossil fuels for continuous heat and power presents a significant barrier to reducing these emissions. Antora’s solution is a thermal battery system that charges with extremely cheap and abundant wind and solar electricity, stores the energy as heat in carbon blocks, and outputs reliable, on-demand heat and power for industrial facilities. This system features a solid-state heat engine comprising high-efficiency, thin-film, III-V thermophotovoltaic (TPV) cells coupled with a 1500°C thermal emission source. Antora has focused on the thermophotovoltaic performance and more recently on its cost-reduction, assembly into high-power density TPV modules, and integration into a large-scale thermal battery demonstration system that is now operational at an industrial site in California. This presentation will review the latest learnings and achievements on the path to market for this unique application of optoelectronics to energy storage and climate change mitigation.
Area 4: Stefaan De Wolf, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Stefaan De Wolf is a professor at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, where he focuses on high-efficiency silicon and perovskite solar cells, their combinations in perovskite/silicon tandem solar cells, as well as photovoltaics for sunny and hot climates. He is Clarivate Highly Cited Researcher since 2019Title: Pathways to Efficient and Stable Perovskite/Silicon Tandem Photovoltaics
Abstract: In this presentation I will discuss the multiple ways how monolithic perovskite/silicon can be fabricated, built from textured silicon heterojunction solar cells, with an emphasis on solution of the perovskite top cell. Bulk and contact passivation of the perovskite are instrumental to obtain a high performance, which can be obtained through molecular additive engineering. Combined, this recently led to a certified power conversion efficiency of 33.7% for a monolithic perovskite/silicon tandem solar cell. This will be followed by a discussion about the outdoor performance of such tandems and the need for robust and perovskite-compatible encapsulation to do so. I will then move on to discuss reliability aspects of such tandems under accelerated degradation tests such as damp-heat testing and reverse-bias degradation. I will conclude my talk with arguing how bifacial perovskite/silicon tandems aid in improved performance as well as stability, thanks to their reliance on narrow-bandgap perovskites for optimal performance.
Area 5: Greg Horner, Tau Science Corporation, USA
Dr. Greg Horner completed his thesis work at NREL and C.U. Boulder on the characterization of III-V materials and then worked for ten years in the semiconductor industry developing non-contact electrical test methods at Keithley Instruments and KLA-Tencor. He founded Tau Science in 2008 to design metrology systems specifically for PV.Title: An Introduction to Corona Oxide Semiconductor Measurements
Abstract: Corona Oxide Semiconductor (COS) techniques were developed by IBM researchers in the 80’s and 90’s to characterize oxides, interface states and properties of the underlying silicon. KLA-Tencor developed the technology further, and it was deployed to leading semiconductor manufacturers in the timeframe 1997-2007. With modifications, it is possible that this technique could be helpful in the development of modern solar cells. We will review the basic source and measurement components used in COS and discuss how these might be used in PV.
Area 6: Kai Zhu, National Renewable Energy Laboratory, USA
Kai Zhu is currently a principal scientist and distinguished member research staff in the Chemistry and Nanoscience Center at the National Renewable Energy Laboratory (NREL). His current research interests are focused on both basic and applied research on perovskite solar cells, including perovskite material development, device fabrication and characterization, and basic understanding of charge carrier dynamics in these cells.Title: Rapid Advances in Inverted Perovskite Solar Cells
Abstract: Organic-inorganic hybrid halide perovskites have attracted significant R&D attention in the photovoltaic (PV) community as a competitive technology for future PV applications. Perovskite solar cells (PSCs) with an inverted (often referred to as p-i-n) structure are becoming increasingly attractive for commercialization due to the rapid increase in power conversion efficiency (PCE), easily scalable fabrication, reliable operation, and compatibility with various perovskite-based tandem device configurations. In this talk, I will discuss the key factors leading to recent rapid advances in developing efficient and stable p-i-n PSCs. Although there is still room for improvement in perovskite bulk materials optimization, interface engineeringtargeting either the bottom (buried) or top perovskite surface layerhas become the most critical and effective approach for advancing PSC performance. In the second part of my talk, I will discuss our recent progress toward understanding the link between indoor and outdoor PSC degradation behaviors. To push PSCs toward commercialization, it is critical to understand device reliability under real-world outdoor conditions where multiple stress factors (e.g., light, heat, humidity) coexist. Understanding the link between indoor and outdoor behaviors is necessary to help identify accelerated indoor testing protocols to quickly guide PSC development.
Area 7: Roger French, Case Western Reserve University, USA
Roger H. French, is the Kyocera Professor, Director of the SDLE Research Center and Faculty Director of the Applied Data Science Program, at Case Western Reserve University, Cleveland OH, USATitle: Data-centric AI Foundation Models for Fleet-wide Data Imputation, Performance and Degradation Analysis of PV Systems
Abstract: PV systems represent a complex interplay of materials and the environment, involving intrinsically multi-modal datatypes. By leveraging existing data streams, technologically informed “AI for PV” can be developed using a “data-centric AI'' approach, as opposed to the “model centric AI” of things such as Large Language Models. Data-centric AI creates models which naturally address real-world questions of PV systems’ design, performance, and degradation. Data-centric AI challenges can be addressed by CRADLE distributed and high performance computing, data FAIRification, and recognizing that real-world PV fleets are a network of geospatiotemporal systems, best represented using spatiotemporal graph neural network (st-GNN) models with metadata feature vectors. A st-GNN model trained on geospatially distributed, time-series data, will utilize the intrinsic spatial and temporal coherence of the real-world PV systems. The model therefore encapsulates all the information about the PV fleet; it is both an st-graph and a knowledge graph (k-graph). This trained PV k/st-graph foundation model is literally a data-driven Digital Twin of the fleet of PV systems it trained on, and encompasses the full lifecycle of the PV systems. Data-centric k/st-graph deep learning AI enables generative data imputation, power forecasting, and performance loss rate determination. And it can be used to predict performance of PV systems not yet constructed.
Area 8: Matthew Millendorf, Raptor Maps, USA
Matthew Millendorf is the lead machine learning engineer at Raptor Maps. A 2019 graduate of Brandeis University, Matthew holds a B.A. in Computer Science and a B.A. in Economics. With a strong interest in image processing and computer vision, Matthew is focused on the research and development of Raptor Maps’ automated inspection initiatives.Title:Scaling PV Inspections for a Growing Solar Industry
Abstract: Solar farm panels and components with aberrant heating patterns (“thermal anomalies”) are an important diagnostic tool for asset management. Solar PV inspections with detailed information about thermal anomalies can help in understanding a solar farm's health through time, with the optimization of remediation strategies, and even the prevention of catastrophic fires. But developing the processes and systems that can find anomalies from aerial thermography consistently and reliably at scale presents multiple challenges. For one, there is limited availability of domain expertise, compounded by the difficulty of making the raw imagery easy to explore, tag, and integrate with other data. Overcoming the scarcity of human expertise by leveraging machine learning (ML) and artificial intelligence (AI) methods brings its own set of issues, including how to ensure robustness to variability in the inputs, accuracy of the outputs, and computational efficiency. In this talk, we provide an overview of how Raptor Maps combines industry-expertise, digital tools, and ML/AI technologies to enable the delivery of solar PV inspections at scale.
Area 9: Xiaoyuan Fan, Pacific Northwest National Laboratory, USA
Dr. Xiaoyuan Fan ([email protected]) is currently a senior staff engineer and Power Electronics Team Leader at PNNL. Serving as a project manager, principal investigator/co-principal investigator and key contributor, he has been managing and supporting multiple research projects funded by the Department of Energy, Department of State, Department of Homeland Security, ARPA-E, Bonneville Power Administration, and other industrial collaborators. His research interests focus on data analytics for power system reliability, wireless communication, multi-discipline resilience analysis, and high-performance computing. He is a senior member of IEEE and serves as a volunteer reviewer of 20+ top-level journals and conferences in power systems and signal processing. He is the recipient of the 2024 Secretary of Energy’s Honor Award, 2021 Federal Laboratory Consortium for Technology Transfer Award, and four Energy and Environment Directorate Outstanding Performance Awards. He received his PhD in electrical engineering from the University of Wyoming and MS and BS degrees in electrical engineering from Huazhong University of Sciences and Technology.Title: Grid Integration of Renewable Energy and Energy Storage
Abstract: Grid integration of renewable energy requires forward-looking planning process and increased emphasizes on reliability, resilience, and equity. Power-electronics based energy generation including solar, wind, distributed energy resources (DERs), and various types of grid-tied energy storage and emerging loads, are reshaping grid operator’s understanding on interconnection level performance and responses. Dr. Xiaoyuan Fan will present the ongoing work at PNNL related to power electronics, energy modeling and analysis, and a wide spectrum of grid stability evaluation tools (software and hardware) and technologies (sensor, wired/wireless communication, grid monitoring and control) in support of grid integration of solar, wind, energy storage.
Area 10: Hugh Cutcher, Solcast, A DNV Company, Australia
Hugh Cutcher has been at Solcast, a DNV Company for over 4 years where he leads the data science team, helping improve the quality and availability of solar resource data. He earned his B.Eng. and Ph.D. in Mechanical Engineering from the University of Sydney.Title: Weather is the New Fuel: Lessons Learnt from Tracking the World's Clouds, Smoke, Dust and More in the Quest for Improved Irradiance Data
Abstract: As solar PV emerges as the world's leading power source, understanding its rapidly fluctuating fuel, the weather, becomes increasingly important. At more than a terawatt of solar, irradiance and weather data are already driving design, investment decisions, performance analysis, asset, and grid forecasting for solar PV and increasingly for the associated growth in energy storage. Exponential growth of solar has required that irradiance data and models rapidly catch up in terms of their quality, availability, and scope. This presentation shares key learnings made across data science and meteorology in the pursuit of this goal. Science, data and practical considerations are reviewed for key physical processes including clouds, atmospheric content, aerosols (smoke, dust, etc.) and terrain. The impacts of snow soiling, dust soiling and solar eclipses are also reviewed.
Area 11: Christian Breyer, LUT University, Finland
Christian Breyer is Professor for Solar Economy at LUT University, Finland. His major expertise is the integrated research of technological and economic characteristics of renewable energy systems specialising in energy system modeling for 100% renewable energy, on a local but also global scale.Title: On the Role of Solar PV for the Energy-Industry Transition in the Americas
Abstract: With the growth of solar photovoltaics (PV) in recent years as the largest power source by capacity added, the energy-industry transition towards high sustainability is accelerating. However, the energy-industry systems of the Americas are largely lagging as fossil fuels still dominate the electricity generation mix and the system as a whole. Energy-industry transition pathways are developed for all countries of the Americas reaching 100% renewable energy (RE) by 2050 for all sectors including power, heat, transport, industry, and desalination. To benchmark the transition to 100% RE, the results are compared to current energy policies across the Americas. The results for the Americas indicate the significant potential to expand RE, especially solar PV, to reach the 100% RE target and fully defossilize each region’s economy. The levelized cost of electricity (LCOE) can be reduced from its current level of 72 €/MWh to 22 €/MWh in 2050, and the levelized cost of final energy (LCOFE) sees reductions from 52 to 31 €/MWh from 2020 to 2050. Widespread electrification across energy-industry sectors requires significant expansion of solar PV, which accounts for 80% of all electricity supply, leading to 14.1 TW of installed capacity as electricity generation increases from 5440 TWh in 2020 to 30,778 TWh in 2050. The dominating role of solar PV thus indicates that the future Americas energy-industry system can be characterized as a Solar-to-X Economy.