Method to inverse design mechanical behaviors using artificial intelligence

A method for designing and generating a target with customizable architecture including:

• a) obtaining, by a first machine learning module, a set of target stress-strain curve features characterizing a target stress-strain curve;
• b) generating a first output, by the first machine learning module trained using a first training set, based on a first query, wherein:
• i) the first query includes the set of target stress-strain curve features;
• ii) the first output includes sets of digital design parameters for printing the metamaterial target using a 3D printer, wherein each of the sets of digital design parameters includes:
• 1) for each cell, a cell type denoting a category of unit cell, and a characteristic angle specifying an orientation of struts of each cell in the category, and a radius-to-length ratio of an inclined strut representing the solid-to-air ratio of the unit cell;
• and
• 2) a design lattice structure utilizing each cell;
• and
• 3) a base material type;
• and
• 4) a gradient;
• and
• iii) the first training set includes a first plurality of training stress-strain curve features and respective associated design parameters sets;
• c) obtaining, by a second machine learning module, the sets of digital design parameters;
• d) obtaining, by the second machine learning module, a subset of the set of target stress-strain curve features;
• e) generating a second output, by the second machine learning module trained using a second training set, based on a second query, wherein:
• i) the second query includes the sets of digital design parameters and the subset of the set of target stress-strain curve features;
• ii) the second output includes respective sets of stress-strain curve features characterizing a predicted stress-strain curve;
• iii) the second training set includes a second plurality of training stress-strain curve features and respective associated design parameters sets;
• f) determining, by the second machine learning module, an optimal set of design parameters of the sets of digital design parameters;
• and
• g) printing or causing to print, by a 3D printer, the metamaterial target, based on the optimal set of design parameters.