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On Wed, 9 Oct, 12:04 AM UTC
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AI speeds up the discovery of energy and quantum materials
Understanding the optical properties of materials is essential for developing optoelectronic devices, such as LEDs, solar cells, photodetectors, and photonic integrated circuits. These devices are pivotal in the semiconductor industry's current resurgence. Traditional means of calculation using the basic laws of physics involve complex mathematical calculations and immense computational power, rendering it difficult to quickly test a large number of materials. Overcoming this challenge could lead to the discovery of new photovoltaic materials for energy conversion and a deeper understanding of the fundamental physics of materials through their optical spectra. A team led by Nguyen Tuan Hung, an assistant professor at the Frontier Institute for Interdisciplinary Science (FRIS), Tohoku University, and Mingda Li, an associate professor at MIT's Department of Nuclear Science and Engineering (NSE), did just that, introducing a new AI model that predicts optical properties across a wide range of light frequency, using only a material's crystal structure as an input. Lead author Nguyen and his colleagues recently published their findings in an open-access paper in Advanced Materials. "Optics is a fascinating aspect of condensed matter physics, governed by the causal relationship known as the Kramers-Krönig (KK) relation," says Nguyen. "Once one optical property is known, all other optical properties can be derived using the KK relation. It is intriguing to observe how AI models can grasp physics concepts through this relation." Obtaining optical spectra with complete frequency coverage in experiments is challenging due to the limitations of laser wavelengths. Simulations are also complex, requiring high convergence criteria and incurring significant computational costs. As a result, the scientific community has long been searching for more efficient methods to predict the optical spectra of various materials. "Machine-learning models utilized for optical prediction are called graph neural networks (GNNs)," points out Ryotaro Okabe, a chemistry graduate student at MIT. "GNNs provide a natural representation of molecules and materials by representing atoms as graph nodes and interatomic bonds as graph edges." Yet, while GNNs have shown promise for predicting material properties, they lack universality, especially in representations of crystal structures. To work around this conundrum, Nguyen and others devised a universal ensemble embedding, whereby multiple models or algorithms are created to unify the data representation. "This ensemble embedding goes beyond human intuition but is broadly applicable to improve prediction accuracy without affecting neural network structures," explains Abhijatmedhi Chotrattanapituk, an electrical engineering and computer science graduate student at MIT. The ensemble embedding method is a universal layer that can be seamlessly applied to any neural network model without modifying the neural network structures. "This implies that universal embedding can readily be integrated into any machine learning architecture, potentially making a profound impact on data science," says Mingda Li. This method enables highly precise optical prediction based solely on crystal structures, making it suitable for a wide variety of applications, such as screening materials for high-performance solar cells and detecting quantum materials. Looking ahead, the researchers aim to develop new databases for various material properties, such as mechanical and magnetic characteristics, to enhance the AI model's capability to predict material properties based solely on crystal structures.
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New AI tool may lead to faster discovery of energy, quantum materials
Interestingly, it can predict the optical properties of materials with the same accuracy as quantum simulations, but at a speed that is a "million times faster." This is a significant development that could accelerate the development of new photovoltaic and quantum materials. The semiconductor industry's recent growth is fueled by the development of optoelectronic devices, which require an extensive understanding of materials' optical properties. These devices include LEDs, solar cells, photodetectors, and photonic integrated circuits. Standard methods of calculating the optical properties of materials are time-consuming and computationally intensive. This new AI model, however, can predict these properties with incredible accuracy in a fraction of the time. Notably, the AI model uses a material's crystal structure as input and can predict its optical properties across a wide range of light frequencies. "Optics is a fascinating aspect of condensed matter physics, governed by the causal relationship known as the Kramers-Krönig (KK) relation," said Nguyen Tuan Hung, an assistant professor at the Frontier Institute for Interdisciplinary Science (FRIS), Tohoku University.
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Researchers develop an AI model that can predict optical properties of materials a million times faster than traditional methods, potentially revolutionizing the discovery of new energy and quantum materials.
In a groundbreaking development, researchers have introduced an artificial intelligence (AI) model capable of predicting the optical properties of materials with unprecedented speed and accuracy. This innovation promises to accelerate the discovery of new energy and quantum materials, potentially transforming industries reliant on optoelectronic devices 1.
Understanding the optical properties of materials is crucial for developing various optoelectronic devices, including LEDs, solar cells, and photonic integrated circuits. Traditionally, calculating these properties required complex mathematical computations and immense computational power, making it difficult to rapidly test a large number of materials 1.
A team led by Nguyen Tuan Hung from Tohoku University and Mingda Li from MIT has developed an AI model that can predict optical properties across a wide range of light frequencies using only a material's crystal structure as input. The model's speed is particularly impressive, performing calculations a million times faster than conventional quantum simulations while maintaining comparable accuracy 2.
The researchers employed graph neural networks (GNNs) as the foundation for their machine-learning model. GNNs represent molecules and materials as graphs, with atoms as nodes and interatomic bonds as edges. To overcome the limitations of GNNs in representing crystal structures, the team devised a universal ensemble embedding method 1.
This AI model's ability to rapidly and accurately predict optical properties has far-reaching implications:
Accelerated material discovery: The model can significantly speed up the screening process for high-performance solar cell materials 1.
Quantum material detection: The AI tool shows promise in identifying new quantum materials 1.
Semiconductor industry boost: The innovation could contribute to the current resurgence in the semiconductor industry by facilitating the development of advanced optoelectronic devices 2.
The research team aims to expand their work by developing new databases for various material properties, such as mechanical and magnetic characteristics. This expansion would enhance the AI model's capability to predict a wider range of material properties based solely on crystal structures 1.
As the field of AI-assisted material science continues to evolve, this breakthrough represents a significant step towards more efficient and innovative approaches in discovering and developing advanced materials for energy and quantum applications.
Reference
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MIT researchers have developed an AI model that can accurately predict the structure of crystalline materials, potentially accelerating materials discovery and design. This breakthrough could have significant implications for various industries, from electronics to energy storage.
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MIT researchers have created a new photonic chip that can perform all key computations of a deep neural network optically, achieving ultrafast speeds and high energy efficiency. This breakthrough could revolutionize AI applications in various fields.
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Scientists discover luminescent nanocrystals with unique optical bistability, potentially revolutionizing AI and data processing through faster, more energy-efficient optical computing methods.
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Scientists combine AI with electron microscopy to visualize atomic-level dynamics of nanoparticles, potentially revolutionizing various industries including pharmaceuticals and electronics.
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Researchers at the Max Planck Institute for the Science of Light have developed XLuminA, an AI-driven framework that autonomously discovers new experimental designs in microscopy, operating 10,000 times faster than traditional methods.
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