Episode 4: Researchers pinpoint AI/ML training set to achieve accurate predictions

In this podcast episode, MRS Bulletin’s Sophia Chen interviews Bowen Deng, a graduate student in Gerbrand Ceder’s group at the University of California, Berkeley, about their work on increasing the accuracy of artificial intelligence/machine learning materials prediction models. The use of computer simulations to predict the interaction between atoms in a given molecule is being replaced by machine learning. Researchers describe the atoms’ collective interactions as a quantity of energy, where higher energies correspond to stronger forces holding the molecule together. Now, Deng’s research group studied three machine learning models and found that they tend to predict lower energies than what is accurate by about 20 percent. The researchers have determined that these underpredictions were caused by biased training data and they found a way to remedy the situation. This work was published in a recent issue of NPJ Computational Materials.