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Finding better photovoltaic materials faster with AI

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Finding better photovoltaic materials faster with AI


Finding better photovoltaic materials faster with AI

by Robert Schreiber

Berlin, Germany (SPX) Jan 24, 2025






Researchers at the Karlsruhe Institute of Technology (KIT) and the Helmholtz Institute Erlangen-Nurnberg (HI ERN) have developed a novel AI-driven workflow that dramatically accelerates the discovery of high-efficiency materials for perovskite solar cells. By synthesizing and testing just 150 targeted molecules, the team achieved results that would typically require hundreds of thousands of experiments. “The workflow we have developed will open up new ways to quickly and economically discover high-performance materials for a wide range of applications,” said Professor Christoph Brabec of HI ERN. One of the newly identified materials enhanced the efficiency of a reference solar cell by approximately two percentage points, reaching 26.2 percent.

The research began with a database containing the structural formulas of about one million virtual molecules, each potentially synthesizable from commercially available compounds. From this pool, 13,000 molecules were randomly selected. KIT researchers applied advanced quantum mechanical methods to evaluate key properties such as energy levels, polarity, and molecular geometry.

Training AI with Data from 101 Molecules

Out of the 13,000 molecules, the team chose 101 with the most diverse properties for synthesis and testing at HI ERN’s robotic systems. These molecules were used to fabricate identical solar cells, enabling precise comparisons of their efficiency. “The ability to produce comparable samples through our highly automated synthesis platform was crucial to our strategy’s success,” Brabec explained.

The data obtained from these initial experiments were used to train an AI model. This model then identified 48 additional molecules for synthesis, focusing on those predicted to offer high efficiency or exhibit unique, unforeseen properties. “When the machine learning model is uncertain about a prediction, synthesizing and testing the molecule often leads to surprising results,” said Tenure-track Professor Pascal Friederich from KIT’s Institute of Nanotechnology.



The AI-guided workflow enabled the discovery of molecules capable of producing solar cells with above-average efficiencies, surpassing some of the most advanced materials currently in use. “We can’t be sure we’ve found the best molecule among a million, but we are certainly close to the optimum,” Friederich commented.

AI Versus Chemical Intuition

The researchers also gained valuable insights into the AI’s decision-making process. The AI identified chemical groups, such as amines, that are associated with high efficiency but had been overlooked by traditional chemical intuition. This capability underscores the potential of AI to uncover previously unrecognized opportunities in materials science.



The team believes their AI-driven strategy can be adapted for a wide range of applications beyond perovskite solar cells, including the optimization of entire device components. Their findings were achieved in collaboration with scientists from FAU Erlangen-Nurnberg, South Korea’s Ulsan National Institute of Science, and China’s Xiamen University and University of Electronic Science and Technology. The research was published in the journal Science.



Research Report:Inverse design of molecular hole-transporting semiconductors tailored for perovskite solar cells


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Machine Learning Enhances Solar Power Forecast Accuracy

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Machine Learning Enhances Solar Power Forecast Accuracy


Machine Learning Enhances Solar Power Forecast Accuracy

by Simon Mansfield

Sydney, Australia (SPX) Feb 18, 2025






As solar power becomes a more significant component of the global energy grid, improving the accuracy of photovoltaic (PV) generation forecasts is crucial for balancing supply and demand. A recent study published in Advances in Atmospheric Sciences examines how machine learning and statistical techniques can enhance these predictions by refining errors in weather models.

Since PV forecasting depends heavily on weather predictions, inaccuracies in meteorological models can impact power output estimates. Researchers from the Institute of Statistics at the Karlsruhe Institute of Technology investigated ways to improve forecast precision through post-processing techniques. Their study evaluated three methods: adjusting weather forecasts before inputting them into PV models, refining solar power predictions after processing, and leveraging machine learning to predict solar power directly from weather data.



“Weather forecasts aren’t perfect, and those errors get carried into solar power predictions,” explained Nina Horat, lead author of the study. “By tweaking the forecasts at different stages, we can significantly improve how well we predict solar energy production.”



The study found that applying post-processing techniques to power predictions, rather than weather forecasts, yielded the most significant improvements. While machine learning models generally outperformed conventional statistical methods, their advantage was marginal in this case, likely due to the constraints of the available input data. Researchers also highlighted the importance of including time-of-day information in models to enhance forecast accuracy.



“One of our biggest takeaways was just how important the time of day is,” said Sebastian Lerch, corresponding author of the study. “We saw major improvements when we trained separate models for each hour of the day or fed time directly into the algorithms.”



A particularly promising approach involves bypassing traditional PV models altogether by using machine learning algorithms to predict solar power directly from weather data. This technique eliminates the need for detailed knowledge of a solar plant’s configuration, relying instead on historical weather and performance data for training.



The findings pave the way for further advancements in machine learning-based forecasting, including the integration of additional weather variables and the application of these methods across multiple solar installations. As renewable energy adoption accelerates, improving solar power forecasting will be key to maintaining grid stability and efficiency.



Research Report:Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning


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The next-generation solar cell is fully recyclable

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The next-generation solar cell is fully recyclable


The next-generation solar cell is fully recyclable

by Robert Schreiber

Berlin, Germany (SPX) Feb 18, 2025






Researchers at Linkoping University have developed a groundbreaking method for recycling all components of a perovskite solar cell without the use of hazardous solvents. The process ensures that recycled solar cells maintain the same efficiency as newly manufactured ones, marking a significant step toward sustainable solar technology. The primary solvent used in this method is water, offering an environmentally friendly alternative to conventional recycling processes.

With the anticipated surge in electricity demand due to the expansion of artificial intelligence and the electrification of transportation, sustainable energy sources must advance to prevent further environmental impact. Solar power has long been considered a viable renewable energy source, with silicon-based panels dominating the market for over three decades. However, as first-generation silicon panels reach the end of their lifespan, waste management poses a major challenge.



“There is currently no effective technology to handle the waste from silicon solar panels. As a result, outdated panels are being discarded in landfills, leading to vast amounts of electronic waste,” explained Xun Xiao, postdoctoral researcher at Linkoping University’s Department of Physics, Chemistry, and Biology (IFM).



Feng Gao, a professor of optoelectronics at the same department, emphasized the importance of considering recyclability in emerging solar technologies: “If we don’t have a recycling solution in place, perhaps we shouldn’t introduce new solar cell technologies to the market.”



Perovskite solar cells are among the most promising alternatives for next-generation solar technology. These cells are lightweight, flexible, and transparent, making them suitable for various surfaces, including windows. Additionally, they achieve energy conversion efficiencies of up to 25 percent, rivaling silicon-based solar cells.



“Many companies are eager to commercialize perovskite solar cells, but we must ensure that they do not contribute to landfill waste. Our project introduces a method where all components of perovskite solar cells can be reused without sacrificing performance,” said Niansheng Xu, postdoctoral researcher at Linkoping University.



Although perovskite solar cells have a shorter lifespan than their silicon counterparts, it is crucial to develop an efficient and environmentally friendly recycling process. Additionally, these cells contain a small amount of lead, essential for high efficiency but requiring proper handling to prevent environmental contamination. In many parts of the world, manufacturers are legally obligated to recycle end-of-life solar cells sustainably.



Existing recycling methods for perovskite solar cells often rely on dimethylformamide, a toxic and potentially carcinogenic solvent commonly found in paint removers. The Linkoping researchers have devised an innovative approach that replaces this hazardous chemical with water, significantly reducing environmental risks. This method enables the recovery of high-quality perovskite materials from the water-based solution.



“We can recover every component-the glass covers, electrodes, perovskite layers, and charge transport layers,” Xiao added.



The next phase of research will focus on scaling up this process for industrial applications. In the long term, scientists believe that perovskite solar cells will become a key component of the global energy transition, particularly as supporting infrastructure and supply chains evolve.




Research Report:Aqueous based recycling of perovskite photovoltaics


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China to further shrink renewables subsidies in market reform push

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China to further shrink renewables subsidies in market reform push


China to further shrink renewables subsidies in market reform push

by AFP Staff Writers

Shanghai (AFP) Feb 9, 2025






China’s top economic planner said on Sunday it would reduce some renewable energy subsidies in reforms intended to open the booming sector to market forces.

China has sought to scale back government support for renewable energy companies in recent years as the sector reaches critical mass.

It installed a record amount of renewable energy last year and has already surpassed a target to have at least 1,200 gigawatts of solar and wind capacity installed by 2030.

New clean energy projects completed after June 1 must sell electricity at rates determined by the market rather than at preferential rates previously used to support China’s energy transition, the National Development and Reform Commission (NDRC) said in a statement.

The NDRC urged energy producers to “push forward clean energy’s participation in market transactions”.

The commission also said it “encourages electricity providers and electricity buyers to sign multi-year purchase agreements and pre-emptively manage market risks”.

Beijing invested more than $50 billion in new solar supply capacity from 2011 to 2022, according to the International Energy Agency.

It has built almost twice as much wind and solar capacity as every other country combined, according to research published last year.

However, China’s grid is struggling to keep up.

Renewable supply is increasingly being blocked to prevent the grid from becoming overwhelmed, a process known as curtailment.

Beijing has rolled out a series of measures over the past decade aimed at weaning renewable energy providers off state financial support.

It ended subsidies for new solar power stations and onshore wind power projects in 2021.

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