Lattice Thermal Conductivity and Elastic Modulus of Xn4 (X=Be, Mg and Pt)

When Machine Learning Meets 2D Materials: A Review

“…[91] However, given the excessively complex and unique relationship between target properties and features, it is essential to conduct a detailed analysis of each property of 2D materials individually. The following sections introduce some of the advances made by ML-based predictions of a wide range of 2D material properties (Table 2), such as thermal stability, [17] thermal conductivity, [33][34][35][36][37][38][39][40][41][42][43] thermal expansion, [44][45][46] energy band structure, [127][128][129] bandgap, [10,[47][48][49][50][51][52][53][54][55] shear modulus, [56] fracture toughness, [57][58][59] exfoliation energy, [130] binding energy, [131][132] adsorption energy, [133][134][135][136] magnetic properties, [137][138][139] T C [140,141] an...…”

“…At present, ML has seen increasing popularity in 2D materials, with research directions expanding from merely discovering and predicting properties to preparing, characterizing, and exploring new physical phenomena. In studies related to material properties, ML has been combined with DFT and MD to explore the thermal properties, [17,[33][34][35][36][37][38]46] bandgaps, [47][48][49][50][51][52][53][54][55] and mechanical properties [56][57][58][59] of various materials, thereby accelerating the pace of research in this field. Furthermore, ML has also been utilized for the discovery of novel 2D materials, including catalytic, [60][61][62][63][64][65][66][67][68] photoelectric, [69][70][71][72][73][74][75][76][77] and magnetic materials.…”