A team of RIKEN chemists have developed a new algorithm that can predict the compositions of new materials. They have designed a machine-learning algorithm that has been trained with the compositions and properties of known materials can predict the properties of unknown materials, saving time in the lab.
Since there is a trade-off between voltage and current, it is tricky to discover new materials. Therefore, Kei Terayama, currently at Yokohama City University while initially at RIKEN Centre for Advanced Intelligence Project and his-coworkers have developed BLOX (BoundLess Objective free eXploration), that can locate out-of-trend materials. The team demonstrated the algorithm’s power by using it to identify eight out-of-trend molecules with a high degree of photoactivity from a drug-discovery database. The properties of these molecules exhibited good agreement with those predicted by the algorithm. “We had concerns about the accuracy of the calculation but were delighted to see that the calculation was correct. This shows the potential of computation-driven materials development,” says Terayama. BLOX uses machine learning to generate a prediction model for key material properties. It does this by combining data for materials randomly selected from a materials database with experimental or calculation results. BLOX then uses the model to predict the properties of a new set of materials. From these new materials, BLOX identifies the one that deviates the most from the overall distribution. The properties of that material are determined by experiment or calculations and then used to update the machine learning model, and the cycle is repeated. BLOX imposes no restrictions on the range of material structures and compositions that can be explored. It can thus range far and wide in its search for outlying materials.
