An anonymous reader quotes a report from MIT Technology Review: In a laboratory that overlooks a busy shopping street in Cambridge, Massachusetts, a robot is attempting to create new materials. A robot arm dips a pipette into a dish and transfers a tiny amount of bright liquid into one of many receptacles sitting in front of another machine. When all the samples are ready, the second machine tests their optical properties, and the results are fed to a computer that controls the arm. Software analyzes the results of these experiments, formulates a few hypotheses, and then starts the process over again. Humans are barely required.
The setup, developed by a startup called Kebotix, hints at how machine learning and robotic automation may be poised to revolutionize materials science in coming years. The company believes it may find new compounds that could, among other things, absorb pollution, combat drug-resistant fungal infections, and serve as more efficient optoelectronic components. The company’s software learns from 3-D models of molecules with known properties. Kebotix uses several machine-learning methods to design novel chemical compounds. The company feeds molecular models of compounds with desirable properties into a type of neural network that learns a statistical representation of those properties. This algorithm can then come up with new examples that fit the same model. To strain out potentially useless materials, Kebotix uses another neural network and “then the company’s robotic system tests the remaining chemical structures,” reports MIT Technology Review. “The results of those experiments can be fed back into the machine-learning pipeline, helping it get closer to the desired chemical properties. The company dubs the overall system a ‘self-driving lab.'”
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