{'fraction_non_alphanumeric': 0.03292383292383293, 'fraction_numerical': 0.007862407862407862, 'mean_word_length': 4.9186046511627906, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Machine learning assisted first-principles calculation of multicomponent solid solutions: estimation of interface energy in Ni-based superalloys

A disordered configuration of atoms in a multicomponent solid solution presents a computational challenge for first-principles calculations using density functional theory (DFT). The challenge is in identifying the few probable (low energy) configurations from a large configurational space before DFT calculation can be performed. The search for these probable configurations is possible if the configurational energy E ( σ ) can be calculated accurately and rapidly (with a negligibly small computational cost). In this paper, we demonstrate such a possibility by constructing a machine learning (ML) model for E ( σ ) trained with DFT-calculated energies. The feature vector for the ML model is formed by concatenating histograms of pair and triplet (only equilateral triangle) correlation functions, g ( 2 ) ( r ) and g ( 3 ) ( r , r , r ) , respectively. These functions are a quantitative ‘fingerprint’ of the spatial arrangement of atoms, familiar in the field of amorphous materials and liquids. The ML model is used to generate an accurate distribution P ( E ( σ ) ) by rapidly spanning a large number of configurations. The P ( E ) contains full configurational information of the solid solution and can be selectively sampled to choose a few configurations for targeted DFT calculations. This new framework is employed to estimate (100) interface energy ( σ IE ) between γ and γ ′ at 700 °C in Alloy 617, a Ni-based superalloy, with composition reduced to five components. The estimated σ IE ≈ 25.95 mJ m−2 is in good agreement with the value inferred by the precipitation model fit to experimental data. The proposed new ML-based ab initio framework can be applied to calculate the parameters and properties of alloys with any number of components, thus widening the reach of first-principles calculation to realistic compositions of industrially relevant materials and alloys.