Thomas Edison famously conducted extensive experiments before discovering that carbonized cotton thread could be used in an incandescent light bulb. The process took 14 months and cost approximately $850,000 in today’s money.
Developing quantum materials for modern electronics and computing is even more time-consuming and expensive.
Researchers rely on detailed databases as virtual laboratories to enable the discovery of new quantum materials that have the potential to surpass Edison’s lightbulb. A team of researchers at Pacific Northwest National Laboratory (PNNL) has created a database of underexplored quantum materials, opening up possibilities for the development of powerful gadgets.
Beyond Edisonian Trial and Error
“We aimed to understand a specific group of materials with the same crystal structure but different properties depending on their combination and growth process,” said materials scientist Tim Pope. These materials, known as transition metal dichalcogenides (TMDs), consist of thousands of potential combinations, each requiring weeks of reaction to produce small flakes of material.
Creating the material is only the first step in exploring its capabilities. As PNNL computational scientist Micah Prange explains, each flake is incredibly delicate and requires studying at super-low temperatures to observe quantum features. Understanding each flake’s characteristics can be an entire research program in itself.
Despite the challenges involved, each combination holds the potential to revolutionize electronics, batteries, pollution remediation, and quantum computing devices. According to Prange, the flakes can be considered “fancier graphene with a richer phenomenology and more practical possibilities.”
“By better understanding the diverse properties of this group of materials, we can select the most suitable combination for specific purposes or even discover entirely new applications,” added Pope.
The Future of Quantum Material Development
The creation of the database began with PNNL’s Chemical Dynamics Initiative, which utilizes data science to fill knowledge gaps caused by measurement challenges and experimental limitations.
These quantum materials consist of varying proportions of 38 transition metals combined with elements from the sulfur family. They can also be grown in three different crystal structures, resulting in thousands of potential combinations with distinct properties. The researchers used density functional theory modeling to compute the properties of 672 unique structures, comprising a total of 50,337 individual atomic configurations. Prior to this research, there were fewer than 40 studied configurations, with limited understanding of their properties.
“Models allow us to analyze the quantum mechanics of atomic arrangement, enabling predictions about electrical conductivity, transparency, and material hardness,” explained Prange.
Using the database, the PNNL researchers uncovered significant differences in electrical and magnetic behaviors among different combinations. They also gained new insights into transition metal chemistry at the quantum level as they varied the transition metal used.
Quantum Combinations for Machine Learning
“When we overlaid the crystal structure with the database, they matched perfectly,” said Pope, referring to the PNNL-grown flakes that are validating the modeling results.
The goal was to create a large dataset of theoretical simulations that could be analyzed using data analytics to understand these materials. The open-source dataset, published in Scientific Data, serves as a foundation for researchers to explore the relationships between initial structures and corresponding properties. This information can then be used to select specific materials for further study.
“This project demonstrates how computational datasets can guide experimental research. It provides valuable data to the machine learning community and has the potential to streamline materials development. We are excited about what we still need to discover to enable the precise synthesis of these materials,” said CDI Chief Scientist Peter Sushko.
More information:
Scott E. Muller et al, An open database of computed bulk ternary transition metal dichalcogenides, Scientific Data (2023). DOI: 10.1038/s41597-023-02103-4
Citation:
Virtual laboratory opens possibility for machine learning to understand promising class of quantum materials (2023, June 14)
retrieved 14 June 2023
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Jessica Irvine is a tech enthusiast specializing in gadgets. From smart home devices to cutting-edge electronics, Jessica explores the world of consumer tech, offering readers comprehensive reviews, hands-on experiences, and expert insights into the coolest and most innovative gadgets on the market.
