More and more materials researchers in industry and academia are starting to use data-driven methods to predict materials properties, but peer-reviewed, published data of experimentally validated predictions remain scarce.
20 Examples of Success
Citrine’s External Research Department, collaborating with Anton Oliynyk of Manhattan College, compiled over 20 prominent examples of materials informatics predictions that have been validated by experiment or physics-based simulation. The work was published in Annual Reviews of Materials Research.
What makes them successful?
Across published case studies, several key ingredients for successful, validated materials informatics efforts were observed:
- Using Domain Knowledge
Complementing materials data with existing physical knowledge (in the form of known empirical models or simulations) can improve the predictive capability of machine learning models
Informed representations of materials data (e.g., deriving elemental weighted averages of bulk moduli in alloys from composition, or bond angles from molecular structures) can improve model performance
- Quality over Quantity in Data
Quality and self-consistency of training data is more valuable than the sheer quantity of data
The outcomes from this project will advance energy-efficient, sustainable methods of chemical production across a broad range of industries.
Published Example from Citrine
One successful case is a collaboration between researchers at Citrine Informatics and Panasonic to predict molecules with high hole mobility for organic semiconductor applications. A homogenous, high-quality training dataset of hole mobilities was generated by physics-based density functional theory (DFT) simulations. Predictive models for hole mobility were developed using physical features derived from the molecular structures. These models were then used to predict novel, high-mobility molecules, which were assessed by DFT and used to update the models. Within 35 iterations, this process resulted in a molecule with 25% higher hole mobility than the best in the training set, verified by DFT. The results of the project appeared in the Journal of Physical Chemistry A and the molecules are now patent pending.
Find out more
You can read more about the Panasonic case study here.
You can find out how the Citrine Platform encourages domain knowledge integration here.