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Last week, a team of researchers from the University of California, Berkeley published a highly anticipated paper in the journal Nature describing an “autonomous laboratory” or “A-Lab” that aimed to use artificial intelligence (AI) and robotics to accelerate the discovery and synthesis of new materials.
Dubbed a “self-driving lab,” the A-Lab presented an ambitious vision of what an AI-powered system could achieve in scientific research when equipped with the latest techniques in computational modeling, machine learning (ML), automation and natural language processing.
However, within days of publication, doubts began to emerge about some of the key claims and results presented in the paper.
Robert Palgrave is an inorganic chemistry and materials science professor at University College London. He has decades of experience in X-ray crystallography. Palgrave raised a series of technical concerns on X (formerly Twitter) about inconsistencies he noticed in the data and analysis provided as evidence for the A-Lab’s purported successes.
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In particular, Palgrave argued that the phase identification of synthesized materials conducted by the A-Lab’s AI via powder X-ray diffraction (XRD) appeared to be seriously flawed in several cases and that some of the newly synthesized materials were already discovered.
AI’s promising attempts — and their pitfalls
Palgrave’s concerns, which he aired in an interview with VentureBeat and a pointed letter to Nature, revolve around the AI’s interpretation of XRD data – a technique akin to taking a molecular fingerprint of a material to understand its structure.
Imagine XRD as a high-tech camera that can snap pictures of atoms in a material. When X-rays hit the atoms, they scatter, creating patterns that scientists can read, like using shadows on a wall to determine a source object’s shape.
Similar to how children use hand shadows to copy the shapes of animals, scientists make models of materials and then see if those models produce similar X-ray patterns to the ones they measured.
But Palgrave pointed that the AI’s models didn’t match the actual patterns, suggesting the AI might have gotten a bit too creative with its interpretations.
Palgrave argued that the AI’s misinterpretation represented such a fundamental failure to meet basic standards of evidence for identifying new materials. These doubts mean that the paper’s central thesis, that 41 novel synthetic inorganic solids had been produced, could not be upheld.
In a letter to Nature, Palgrave detailed a slew of examples where the data simply did not support the conclusions drawn. In some cases, the calculated models provided to match XRD measurements differed so dramatically from the actual patterns that “serious doubts exist over the central claim of this paper,