Solar Energy

Engineers apply physics-informed machine learning to solar cell production

Published

4 years ago

June 10, 2021

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Engineers apply physics-informed machine learning to solar cell production

Today, solar energy provides 2% of U.S. power. However, by 2050, renewables are predicted to be the most used energy source (surpassing petroleum and other liquids, natural gas, and coal) and solar will overtake wind as the leading source of renewable power. To reach that point, and to make solar power more affordable, solar technologies still require a number of breakthroughs. One is the ability to more efficiently transform photons of light from the Sun into useable energy.

Organic photovoltaics max out at 15% to 20% efficiency – substantial, but a limit on solar energy’s potential. Lehigh University engineer Ganesh Balasubramanian, like many others, wondered if there were ways to improve the design of solar cells to make them more efficient?

Balasubramanian, an associate professor of Mechanical Engineering and Mechanics, studies the basic physics of the materials at the heart of solar energy conversion – the organic polymers passing electrons from molecule to molecule so they can be stored and harnessed – as well as the manufacturing processes that produce commercial solar cells.

Using the Frontera supercomputer at the Texas Advanced Computing Center (TACC) – one of the most powerful on the planet – Balasubramanian and his graduate student Joydeep Munshi have been running molecular models of organic solar cell production processes, and designing a framework to determine the optimal engineering choices. They described the computational effort and associated findings in the May issue of IEEE Computing in Science and Engineering.

“When engineers make solar cells, they mix two organic molecules in a solvent and evaporate the solvent to create a mixture which helps with the exciton conversion and electron transport,” Balasubramanian said. “We mimicked how these cells are created, in particular the bulk heterojunction – the absorption layer of a solar cell. Basically, we’re trying to understand how structure changes correlate with the efficiency of the solar conversion?”

Balasubramanian uses what he calls ‘physics-informed machine learning’. His research combines coarse-grained simulation – using approximate molecular models that represent the organic materials – and machine learning. Balasubramanian believes the combination helps prevent artificial intelligence from coming up with unrealistic solutions.

“A lot of research uses machine learning on raw data,” Balasubramanian said. “But more and more, there’s an interest in using physics-educated machine learning. That’s where I think lies the most benefit. Machine learning per se is simply mathematics. There’s not a lot of real physics involved in it.”

Writing in Computational Materials Science in February 2021, Balasubramanian and Munshi along with Wei Chen (Northwestern University), and TeYu Chien (University of Wyoming) described results from a set of virtual experiments on Frontera testing the effects of various design changes. These included altering the proportion of donor and receptor molecules in the bulk heterojunctions, and the temperature and amount of time spent in annealing – a cooling and hardening process that contributes to the stability of the product.

They harnessed the data to train a class of machine learning algorithms known as support vector machines to identify parameters in the materials and production process that would generate the most energy conversion efficiency, while maintaining structural strength and stability. Coupling these methods together, Balasubramanian’s team was able to reduce the time required to reach an optimal process by 40%.

“At the end of the day, molecular dynamics is the physical engine. That’s what captures the fundamental physics,” he said. “Machine learning looks at numbers and patterns, and evolutionary algorithms facilitate the simulations.”

Trade-Offs and Limitations

Like many industrial processes, there are trade-offs involved in tweaking any facet of the manufacturing process. Faster cooling may help increase power efficiency, but it may make the material brittle and prone-to-break, for instance. Balasubramanian and his team employed a multi-objective optimization algorithm that balances the benefits and drawbacks of each change to derive the overall optimal manufacturing process.

“When you try to optimize one particular variable, you are looking at the problem linearly,” he said. “But most of these efforts have multi-pronged challenges that you’re trying to solve simultaneously. There are trade-offs that you need to make, and synergistic roles that you must capture, to come to the right design.”

Balasubramanian’s simulations matched experimental results. They determined that the make-up of the heterojunction and the annealing temperature/timing have the largest effects on overall efficiency. They also found what proportion of the materials in the heterojunction is best for efficiency.

“There are certain conditions identified in literature which people claim are the best conditions for efficiency for those select molecules and processing behavior,” he said. “Our simulation were able to validate those and show that other possible criteria would not give you the same performance. We were able to realize the truth, but from the virtual world.”

With an award of more time on Frontera in 2021-22, Balasubramanian will add further layers to the machine learning system to make it more robust. He plans to add experimental data, as well as other modalities of computer models, such as electronic structure calculations.

“Heterogeneity in the data will improve the results,” he said. “We plan to do first principle simulations of materials and then feed that data into the machine learning model, as well as data from coarse-grained simulations.”

Balasubramanian believes that current organic photovoltaics may be reaching the limits of their efficiency. “There’s a wall that’s hard to penetrate and that’s the material,” he said. “These molecules we’ve used can only go so far. The next thing to try is to use our framework with other molecules and advanced materials.”

His team mined the literature to understand the features that increase solar efficiency and then trained a machine learning model to identify potential new molecules with ideal charge transport behaviors. They published their research in the Journal of Chemical Information and Modeling. Future work on Frontera will use Balasubramanian’s framework to explore and computationally test these alternative materials, assuming they can be produced.

“Once established, we can take realistic molecules that are made in the lab and put them in the framework we’ve created,” he said. “If we discover new materials that perform well, it will reduce the cost of solar power generation devices and help Mother Earth.”

Balasubramanian’s research harnesses the two things that computer simulations are critical for, he says. “One is to understand the science that we cannot study with the tools that we have in the real world. And the other is to expedite the science – streamline what we really have to do, which reduces our cost and time to make things and physically test them.”

Research paper

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Solar Energy

Vietnam ups wind, solar targets as energy demand soars

Published

2 days ago

April 20, 2025

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Vietnam ups wind, solar targets as energy demand soars

by AFP Staff Writers

Hanoi (AFP) April 17, 2025

Vietnam has dramatically increased its wind and solar targets as it looks to up its energy production by 2030 to meet soaring demand, according to a revised version of its national power plan.

The Southeast Asian country has committed to reaching net-zero carbon emissions by 2050 and the latest edition of its Power Development Plan 8 (PDP8), as it is known, maps out how it will reach those goals.

The manufacturing powerhouse has been heavily reliant on coal to meet its rapidly expanding energy needs. But now it wants to “strongly develop renewable energy sources”, according to the plan, which was published Wednesday on the government’s news portal.

With targets set at 73 gigawatts (GW) for solar and 38 GW for onshore wind energy by 2030 — and a significant increase to 296 GW and 230 GW by 2050 — the plan looks “really ambitious”, said Andri Prasetiyo, senior researcher at Senik Centre Asia.

The 2023 version of the PDP8 aimed for 12.8 GW for solar and 21 GW for wind by the end of the decade.

“I think this sends a clear message, Vietnam is positioning itself to maintain leadership in Southeast Asia’s clean energy transition, (even) taking a more prominent role in the region,” he told AFP.

Solar power grew rapidly in Vietnam until 2020 but its success hit a roadblock due to infrastructure limitations.

Prasetiyo said Vietnam’s new targets were “increasingly feasible”, although they far outstrip market projections of what the country can achieve.

– Coal, nuclear –

The latest version of the PDP8, which was approved this week, also re-emphasises the country’s 2023 pledge to end the use of coal by 2050.

Coal will represent nearly 17 percent of its energy mix by the end of the decade, down from a target of 20 percent set in 2023.

Meanwhile, solar will account for 31 percent of the country’s energy by 2030, while onshore wind will be 16 percent.

More than $136 billion will be needed if Vietnam is to get there, the document said.

Under the new plan, the country also aims to open its first nuclear power plant by 2035 at the latest.

It comes after Vietnam and Russia signed an agreement on nuclear energy in January, with Hanoi saying Russian nuclear giant Rosatom was “very interested” in cooperating on a project in central Ninh Thuan province.

Overall, as Vietnam targets an ambitious 10 percent economic growth rate by the end of the decade, it wants to raise its total installed capacity to a maximum of 236 GW by that date.

That’s up by more than 80 GW from the figure outlined in 2023.

Hanoi is also eager to avoid a repeat of the rolling blackouts and sudden power outages in summer 2023 that led to losses among manufacturers. They also prompted massive disruption for residents, as intensely hot weather and unprecedented drought strained energy supplies in northern Vietnam.

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Solar Energy

New system offers early warning of dust storms to protect solar power output

Published

6 days ago

April 16, 2025

sarosh

New system offers early warning of dust storms to protect solar power output

by Simon Mansfield

Sydney, Australia (SPX) Apr 10, 2025

A new predictive platform called iDust is poised to transform dust storm forecasting and improve solar energy output in dust-prone regions. Developed by researchers at the Chinese Academy of Sciences, iDust offers high-resolution, fast-turnaround dust forecasts that could help mitigate power losses across solar farms, particularly in arid zones.

The tool was created under the leadership of Dr. Chen Xi from the Institute of Atmospheric Physics and detailed in the Journal of Advances in Modeling Earth Systems (JAMES).

“Dust storms not only block sunlight but also accumulate on solar panels, decreasing their power output.” said Chen, outlining the motivation behind the project. With China’s rapid expansion of solar installations in desert areas, the need for precise and timely dust forecasts has become increasingly urgent to avoid operational disruptions and revenue shortfalls.

Traditional systems like those from the European Centre for Medium-Range Weather Forecasts (ECMWF) often lack the spatial resolution and processing speed needed for optimal solar planning. iDust addresses these limitations by embedding dust-related dynamics directly into its forecast engine. This allows the system to generate forecasts with 10-kilometer resolution-a fourfold improvement over previous models-while maintaining near-parity in computational load. Crucially, iDust can deliver 10-day forecasts within six hours of initial observations.

The effectiveness of iDust was put to the test on April 13, 2024, when it successfully tracked a severe dust storm over Bayannur in northern China. Such storms can distort solar energy projections by as much as 25% if unaccounted for, underscoring the value of integrating dust modeling into energy planning.

Designed for practical deployment, iDust aims to assist solar facility operators and grid managers in optimizing power production and reducing losses due to airborne particulates. As China pushes toward its carbon neutrality goals, innovations like iDust will be central to achieving sustainable energy reliability.

Researchers plan to expand the system for global application, allowing other countries with desert-based solar assets to benefit from enhanced dust forecasting.

Research Report:The Efficient Integration of Dust and Numerical Weather Prediction for Renewable Energy Applications

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Solar Energy

Going green with fluoride-enhanced perovskite solar cells

Published

7 days ago

April 15, 2025

sarosh

Going green with fluoride-enhanced perovskite solar cells

by Simon Mansfield

Sydney, Australia (SPX) Apr 15, 2025

A team of scientists from Queensland University of Technology (QUT) has unveiled a sustainable method for fabricating perovskite solar cells (PSCs) by using a fluoride-based additive in a water-only solution. This innovation replaces hazardous solvents typically used in PSC production, achieving a notable power conversion efficiency exceeding 18%.

Perovskite solar cells have emerged as a promising technology for the future of solar energy, thanks to their high efficiency and cost-effectiveness. Yet, their commercial scalability has been hindered by the environmental and health hazards posed by conventional toxic solvents used during manufacturing. While water-based methods offer a more sustainable route, they have so far underperformed in delivering high-efficiency devices.

To overcome this barrier, QUT researchers introduced lead(II) fluoride (PbF2) into the aqueous precursor mix. This additive plays a dual role: it speeds up the formation of the light-absorbing phase and aligns the crystallization process to optimize light conversion. The fluoride ions also passivate surface defects in the perovskite grains, minimizing charge loss and improving conductivity.

“With the PbF2 additive, we achieved a power conversion efficiency of 18.1%, compared to 16.3% in the control device,” said Dr. Minh Tam Hoang, a postdoctoral researcher at QUT and lead author of the study. “Even more exciting is the improved operational and environmental stability, which brings us closer to scalable, green manufacturing of PSCs.”

This advancement signals a meaningful shift in perovskite solar cell development, offering a pathway to produce efficient and durable solar modules through eco-friendly processes. The results demonstrate the value of fluoride-based chemistry in advancing both performance and sustainability in solar technologies.

The findings were published in the journal Materials Futures, underscoring the growing role of green additives in next-generation clean energy solutions.

Research Report:Lead (II) fluoride additive modulating grains growth of water-processed metal halide perovskites for enhanced efficiency in solar cells