Abstract: This disclosure relates generally to method to determine an optimal set of atom centered symmetry functions. One or more parameters associated with one or more atom centered symmetry functions (ACSFs) are received. An initial set of ACSFs is generated by varying the one or more parameters. A histogram with a prespecified bin size is constructed to obtain a distribution of value of each of the initial set of ACSFs. A pruned list of ACSFs is obtained based on width and maximum value of the distribution of the value of initial set of ACSFs. The pruned list of ACSFs is sorted in decreasing order of spread to obtain a sorted list of ACSFs. An optimal set of one or more shortlisted ACSFs is determined by traversing through the sorted list of ACSFs. A high dimensional neural network potential is trained based on the optimal set of one or more shortlisted ACSFs.

Abstract: This disclosure relates to application based design of novel materials. Conventional methods utilize laborious experimentation or costly first principles calculations. Conventional data driven techniques use point cloud-based representation for crystal structures, that suffers from permutation variance which is not inbuilt in a material's representation, the DL model has to learn invariance which may be inaccurate. Other methods use image based representation for crystal structures and separate images for each element type to represent the basis, which is memory and time intensive. Since each element is represented by its own image, it is difficult for model to learn chemical environment and neighborhood pattern of each element. The embodiments used image based representation of materials consistent with physical principles. Also, embodiments utilize elements matrix to obtain atoms and their positions from basis images.