Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene- based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic- scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress-strain curves from reactive (IFF- R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure- stiffness- failure relationships. By leveraging the database and physics- and chemistry- informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than effierties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The tions, and carbon fiber cross- sections outside the training distribution. The physics- and range while XGBoost works well with limited training data inside the training range. mental and simulation data, scalable beyond 100 nm size, and extendable to chemically for applications in structural materials, nanoelectronics, and carbon- based catalysts.