The algorithm predicts inverted new material compositions

credit : expertsystem.com

Trick-defying is a machine learning algorithm that can reverse new material compositions developed by chemists RIKEN. It is useful for finding materials for applications that are traded between two or more desirable properties.

Artificial intelligence has the great potential to help scientists discover new materials with the desired properties. A machine learning algorithm trained with the composition and properties of known materials can predict the properties of unknown materials, saving more time in the laboratory.

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It is very difficult to find new materials for applications, because often the trade between two or more physical properties is stopped. Organic materials for organic solar cells are an example, where they want to increase both voltage and current, the key terayama at the Raiken Center for the Advance Intelligence Project, now at Yokohama City University. "There is a trade-off between voltage and current: the material that exhibits high voltage has the lowest current, the high current has the lowest voltage."

Physicists often try to find "outside trend" material that shuts down normal trade. But unfortunately traditional machine learning algorithms are much better at observing trends than searching for materials that go against them.

Now, Terayama and his colleagues have developed a machine learning algorithm, BLOX (Boundless Objective Free Exploration), which can detect substances outside the trend.

The team demonstrated the power of an algorithm to detect eight outside molecules with high levels of photoactivity from drug-discovery databases. The properties of these molecules demonstrated good agreement with the estimation of the algorithm. "We are concerned about the accuracy of the calculation, but it is nice to see that the calculation is correct," Tarayam said. "It shows the potential for computational-based material development."

BLOX uses machine learning to design a model attendance model for key physical properties. It does this by combining data for randomly selected materials from the content database with experimental or calculated results. BLOX then uses the model to evaluate the properties of new materials. From these new materials, BLOX identifies the person who will most likely deviate from the overall distribution. The properties of that material are determined by experiment or calculation and then used to update the machine learning model and the cycle is repeated.

Importantly, unlike many previous algorithms, BLOX places no limits on the range of searchable content structures and compositions. Hence it may be too far in search of external materials.

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