New AI tool set to speed quest for advanced superconductors

Xu Chen, an Emory PhD student of theoretical chemistry, is first author of the paper. He says the team was inspired by the image-recognition training used for self-driving cars to create a powerful machine-learning framework.

Using artificial intelligence shortens the time to identify complex quantum phases in materials from months to minutes, finds a new study published in Newton. The breakthrough could significantly speed up research into quantum materials, particularly low-dimensional superconductors. 

The study was led by theorists at Emory University and experimentalists at Yale University. Senior authors include Fang Liu and Yao Wang, assistant professors in Emory’s Department of Chemistry, and Yu He, assistant professor in Yale’s Department of Applied Physics. 

The team applied machine-learning techniques to detect clear spectral signals that indicate phase transitions in quantum materials — systems where electrons are strongly entangled. These materials are notoriously difficult to model with traditional physics because of their unpredictable fluctuations. 

“Our method gives a fast and accurate snapshot of a very complex phase transition, at virtually no cost,” says Xu Chen, the study’s first author and an Emory PhD student in chemistry. “We hope this can dramatically speed up discoveries in the field of superconductivity.” 

One of the challenges in applying machine learning to quantum materials is the lack of sufficient high-quality experimental data needed to train models. To overcome this, the researchers used high-throughput simulations to generate large amounts of data. They then combined these simulation results with just a small amount of experimental data to create a powerful and efficient machine-learning framework.

Read more about the discovery.

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Chatbot opens computational chemistry to nonexperts

A new clue to how multicellular life may have evolved

The idea for the work came from watching the filter feeding of stentors — trumpet-shaped, single-celled giants that float near the surface of ponds. (Getty Images)

Life emerged on Earth some 3.8 billion years ago. The “primordial soup theory” proposes that chemicals floating in pools of water, in the presence of sunlight and electrical discharge, spontaneously formed organic molecules. These building blocks of life underwent chemical reactions, likely driven by RNA, eventually leading to the formation of single cells. 

But what sparked single cells to assemble into more complex, multicellular life forms? 

Nature Physics published a new insight about a possible driver of this key step in evolution — the fluid dynamics of cooperative feeding. 

“So much work on the origins of multicellular life focuses on chemistry,” says Shashank Shekhar, lead author of the study and assistant professor of physics at Emory University. “We wanted to investigate the role of physical forces in the process.” 

Shekhar got the idea while watching the filter feeding of stentors — trumpet-shaped, single-celled giants that float near the surface of ponds. Through microscope video, he captured the fluid dynamics of a stentor in a liquid-filled lab dish as the organism sucked in particles suspended in the liquid. He also recorded the fluid dynamics of pairs and groups of stentors clumped together and feeding. 

The videos revealed a world similar to how Van Gogh saw the night sky, swirling with stars. 

“The project started with beautiful images of the fluid flows,” Shekhar says. “Only later did we realize the evolutionary significance of this behavior.”

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Chatbot opens computational chemistry to nonexperts

The researchers hope their pioneering work to democratize computational chemistry will inspire similar initiatives across the natural sciences. (Liu Group)

Advanced computational software is streamlining quantum chemistry research by automating many of the processes of running molecular simulations. The complicated design of these software packages, however, often limits their use to theoretical chemists trained in specialized computing techniques. 

A new web platform developed at Emory University overcomes this limitation with a user-friendly chatbot. The chatbot guides nonexperts through a multistep process for setting up molecular simulations and visualizing molecules in solution. It enables any chemist — including undergraduate chemistry majors — to configure and execute complex quantum mechanical simulations through chatting. 

The free, publicly available platform — known as AutoSolvateWeb — operates primarily on cloud infrastructure, further expanding access to sophisticated computational research tools. 

The journal Chemical Science published a proof-of-concept for AutoSolvateWeb, which marks a significant step forward in the integration of AI into education and scientific research.

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