Predicting Macroscopic Properties of Amorphous Monolayer Carbon via Pair Correlation Function

Abstract Establishing the structure-property relationship in amorphous materials has been a long-term grand challenge due to the lack of a unified description of the degree of disorder. In this work, we develop SPRamNet, a neural network based machine-learning pipeline that effectively predicts structure-property relationship of amorphous material via global descriptors. Applying SPRamNet on the recently discovered amorphous monolayer carbon, we successfully predict the thermal and electronic properties. More importantly, we reveal that a short range of pair correlation function can readily encode sufficiently rich information of the structure of amorphous material. Utilizing powerful machine learning architectures, the encoded information can be decoded to reconstruct macroscopic properties involving many-body and long-range interactions. Establishing this hidden relationship offers a unified description of the degree of disorder and eliminates the heavy burden of measuring atomic structure, opening a new avenue in studying amorphous materials.