🦾 AI helps 1200 researchers develop new materials in global hackathon

• Over 1,200 researchers and developers participated in a global hackathon to use AI in materials science and drug development.
• More than 100 teams developed AI tools that can predict crystal structures, track medicines, and design new chemicals in just two days.
• Participants showed that large language models can accelerate materials research by analyzing data and generating research hypotheses automatically.

Global competition attracts thousands of participants

Ben Blaiszik, data scientist at the University of Chicago, organized his global hackathon for AI in materials science for the third time. The event attracted over 1,200 researchers and developers from around the world who worked both virtually and at physical locations for 48 hours, writes Science.

Participants competed for small cash prizes by developing AI tools based on large language models for materials science and drug development. More than 100 teams submitted 2-minute videos showcasing their projects.

Crystal structures predicted with AI

Daniel Speckhard, PhD student in physics at Humboldt University, collaborated with a data scientist, a computer scientist, and a mathematician despite having no prior experience with large language models. The team tested how well AI could predict how the crystal structure of a material relaxes to its lowest energy configuration.

They trained Google's T5 model with crystallography data containing examples of crystal structures in different energy configurations. The model could then predict how a new crystal would relax based solely on patterns in data, without any underlying physics theory.

The team managed in two days to accomplish something that would have taken Speckhard more than a month to do himself. Large language models helped them write code to import and analyze data.

AI agents track medicines

Some teams developed specialized AI agents for quality control in drug and materials manufacturing. One agent helps track where and when ingredients were manufactured by extracting information from scattered documents. The AI agent can create unique digital tags to follow a drug's chain in case of safety recalls.

Other teams showed how existing language models can help design new drugs after being fed data such as 3D structures of different molecules and training to predict which chemicals to synthesize.

One team built a chatbot that functions as a "co-pilot" and uses existing AI tools for chemistry to help scientists generate new research hypotheses.

Comprehensive database supports development

Pepe Márquez, materials scientist at Humboldt University, develops the NOMAD platform which over the past decade has become the largest centralized repository for materials science data. The database contains more than 19 million entries from the research community for over 4 million materials.

During the hackathon, some teams designed chatbots to guide scientists through the NOMAD platform. Other teams developed data extraction pipelines to help researchers automatically populate NOMAD databases with laboratory results.

Ana Velázquez works as a "data steward" at Hemholtz-Zentrum Berlin and helps scientists use NOMAD. She collaborates with chemists and physicists to assess how AI tools can accelerate workflows and develops customized NOMAD setups for automated data analysis.

Expert jury evaluates contributions

32 expert judges from academic institutions and companies are currently reviewing the competition's submissions. Blaiszik plans to connect winning teams to mentors and venture capitalists to help them develop their prototypes into full-scale products.

Kutlualp Tazefidan, software developer at Germany's Federal Institute for Materials Research and Testing, worked on the quality control app and emphasizes the importance of collaboration. His team used ChatGPT to help with documentation when a teammate became too tired after eating too much cake. AI can be used for many different things...

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