Dennis Schroeder/National Renewable Energy Laboratory
From ‘The Terminator’ to ‘The Matrix’, Hollywood has taught us to be wary of artificial intelligence. But rather than sealing our doom on the big screen, algorithms could be the solution to at least one issue presented by the climate crisis.
Researchers at the ARC Centre of Excellence in Exciton Science have successfully created a new type of machine learning model to predict the power-conversion efficiency (PCE) of materials that can be used in next-generation organic solar cells, including ‘virtual’ compounds that don’t exist yet.
Unlike some time-consuming and complicated models, the latest approach is quick, easy to use and the code is freely available for all scientists and engineers.
The key to developing a more efficient and user-friendly model was to replace complicated and computationally expensive parameters, which require quantum mechanical calculations, with simpler and chemically interpretable signature descriptors of the molecules being analysed. They provide important data about the most significant chemical fragments in materials that affect PCE, generating information that can be used to design improved materials.
The new approach could help to significantly speed up the process of designing more efficient solar cells at a time when the demand for renewable energy, and its importance in reducing carbon emissions, is greater than ever. The results have been published in the Nature journal Computational Materials.
After decades of relying on silicon, which is relatively expensive and lacks flexibility, attention is increasingly turning to organic photovoltaic (OPV) solar cells, which will be cheaper to make by using printing technologies, as well as being more versatile and easier to dispose of.
A major challenge is sorting through the huge volume of potentially suitable chemical compounds that can be synthesised (tailor-made by scientists) for use in OPVs.
Researchers have tried using machine learning before to address this issue, but many of those models were time consuming, required significant computer processing power and were difficult to replicate. And, crucially, they did not provide enough guidance for the experimental scientists seeking to build new solar devices.
Now, work led by Dr Nastaran Meftahi and Professor Salvy Russo of RMIT University, in conjunction with Professor Udo Bach’s team at Monash University, has successfully addressed many of those challenges.
“The majority of the other models use electronic descriptors which are complicated and computationally expensive, and they’re not chemically interpretable,” Nastaran said.
“It means that the experimental chemist or scientist can’t get ideas from those models to design and synthesise materials in the lab. If they look at my models, because I used simple, chemically interpretable descriptors, they can see the important fragments.”
Nastaran’s work was strongly supported by her co-author Professor Dave Winkler of CSIRO‘s Data 61, Monash University, La Trobe University, and the University of Nottingham. Professor Winkler co-created the BioModeller program which provided the basis for the new, open source model.
By using it, the researchers have been able produce results that are robust and predictive, and generate, among other data, quantitative relationships between the molecular signatures under examination and the efficiency of future OPV devices.
Nastaran and her colleagues now intend to extend the scope of their work to include bigger and more accurate computed and experimental datasets.
The Latest Updates from Bing News & Google News
Go deeper with Bing News on:
Clean energy generation
- North County’s new kid on the energy block: Three-city community choice program launches Saturdayon May 1, 2021 at 6:00 am
A new community choice energy program encompassing three North County cities — Carlsbad, Del Mar and Solana Beach — begins enrolling the first of about 58,000 customers on Saturday, offering an ...
- South Asia Renewable Energy Market Report 2020 and Future Opportunity Assessment, Size, Share Forecast to 2025on May 1, 2021 at 5:57 am
Comserve / -- The report analyze market size, share, growth, trends, segmentation, top key players, strategies, demand, statistics, competitive landscape and forecast. South Asia renewable energy ...
- UK’s net zero push undermined by energy grid that holds back renewableson May 1, 2021 at 5:00 am
Renewable energy providers call for reform of system that reserves capacity for gas power stations, even when they’re laying idle, finds Ben Chapman ...
- Look inside this lab where scientists are recreating the energy of the sun to produce nearly unlimited clean energyon April 30, 2021 at 8:34 am
At TAE Technologies lab in California, scientists are building a nuclear fusion machine to recreate the energy of the sun, stars and lightening here on Earth.
- Defining clean energy standardon April 30, 2021 at 7:00 am
Delivered daily by 10 a.m., Morning Energy examines the latest news in energy and environmental politics and policy. With help from Annie Snider, Kelsey Tamborrino, Alex Guillén, Eric Wolff, Eric ...
Go deeper with Google Headlines on:
Clean energy generation
Go deeper with Bing News on:
New type of machine learning model
- Understanding the Art of Machine Learningon April 30, 2021 at 1:05 pm
Though neural-network-based machine learning is escalating in popularity, the mechanics behind it tend to be misconstrued or simply not known at all.
- Understanding AI And Machine Learning Concepts To Build Your AI Leadership Brain Trust.on April 29, 2021 at 6:40 pm
This blog is the latest in the AI Leadership Brain Trust series which defines 50 key competencies required to build a world-class AI center of excellence in a large enterprise. This blog defines basic ...
- Machine Learning Analyzes EHR Data to Uncover Kidney Diseaseon April 29, 2021 at 11:32 am
A machine learning algorithm automatically scans EHR data to alert providers to patients with early-stage chronic kidney disease.
- Membership inference attacks detect data used to train machine learning modelson April 29, 2021 at 12:21 am
In membership inference attacks, the adversary can work back from the machine learning algorithm to discover the training data used.
- Researchers develop new protocols to validate integrity of machine-learning modelson April 28, 2021 at 1:32 pm
Machine learning is widely used in various applications such as image recognition, autonomous vehicles and email filtering. Despite its success, concerns about the integrity and security of a model's ...