West Bengal, India: Researchers from IIT Kharagpur in collaboration with the Indo-Korea Science and Technology Center (IKST) have now developed a method called CrysXPP to predict the properties of crystalline materials through machine learning. Until now, crystalline materials have been difficult to test on a large scale. Determining the electronic, magnetic and elastic properties of a crystal, is often time-consuming, expensive and calculation-intensive as it requires extensive experimentation. Machine learning algorithms are data-intensive. It involves large amount of data from the source materials which are labelled with property labels to accurately predict properties of new crystals. With these shortcomings in mind, IIT KGP researchers developed CrysXPP, a machine learning system that enables rapid prediction of various material properties with high precision.
Prof Niloy Ganguly, Computer Science and Engineering, IIT Kharagpur and Visiting Professor at L3S Research Centre, Germany stated that, “The published work addresses the important problem of sparse and opaque data, which are the main obstacles in predicting the properties of crystals quickly and accurately.” CrysXPP takes advantage of this because the individual atoms and their interconnections in the crystal structure are also responsible for the specific properties of the crystal. Associate Professor Pawan Goyal, Computer Science and Engineering, IIT Kharagpur says, “Machine learning methods are fast and do not involve costly calculations. But the problem with machine learning algorithms are, they are data intensive that require to be trained with a large amount of property-tagged data of stock materials to make accurate property predictions of new crystals. Such property-tagged data is not available sufficiently. Moreover, whatever is available is not experimentally derived property rather theoretically (DFT) calculated, hence training with theoretically derived data can lead to biases and inaccuracies within the system. To overcome these limitations, CrysXPP was developed.” The result was recently published in the NPJ Computational Materials, a journal of the prestigious Nature Publishing Group.
