title: ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ²Ð¾Ð¹ÑÑÐ² Ð°Ð¼Ð¾ÑÑÐ½ÑÑ Ð¼ÐµÑÐ°Ð»Ð»Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐ¿Ð»Ð°Ð²Ð¾Ð² Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Predicting properties of amorphous metal alloys using machine learning
Creators: name: Ð¡Ð¸Ð»ÐµÐ½ÐºÐ¾Ð², ÐÐ¸ÑÐ°Ð¸Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2025
Subjects: Ð°Ð¼Ð¾ÑÑÐ½ÑÐµ Ð¼ÐµÑÐ°Ð»Ð»Ð¸ÑÐµÑÐºÐ¸Ðµ ÑÐ¿Ð»Ð°Ð²Ñ
ÑÑÐµÐºÐ»Ð¾Ð¾Ð±ÑÐ°Ð·ÑÑÑÐ°Ñ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÑ
Ð¼Ð°ÐºÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÐ¹ Ð´Ð¸Ð°Ð¼ÐµÑÑ Ð°Ð¼Ð¾ÑÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÑÐ°Ð·ÑÐ°
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐºÑÑÑÐ°-Ð´ÐµÑÐµÐ²ÑÑ
SHAP Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ
amorphous metallic alloys
glass-forming ability
maximum diameter of amorphous sample
machine learning
extra-trees
SHAP values

description: Ð Ð°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¼Ð°ÐºÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð´Ð¸Ð°Ð¼ÐµÑÑÐ° Ð°Ð¼Ð¾ÑÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÑÐ°Ð·ÑÐ° (Dmax) ÐºÐ°Ðº ÐºÐ»ÑÑÐµÐ²Ð¾Ð³Ð¾ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ñ ÑÑÐµÐºÐ»Ð¾Ð¾Ð±ÑÐ°Ð·ÑÑÑÐµÐ¹ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÐ¸ (GFA) Ð°Ð¼Ð¾ÑÑÐ½ÑÑ Ð¼ÐµÑÐ°Ð»Ð»Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐ¿Ð»Ð°Ð²Ð¾Ð² (ÐÐÐ¡). ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1) Ð¾Ð±Ð·Ð¾Ñ Ð»Ð¸ÑÐµÑÐ°ÑÑÑÑ Ð¿Ð¾ Ð°Ð¼Ð¾ÑÑÐ½ÑÐ¼ Ð¼ÐµÑÐ°Ð»Ð»Ð¸ÑÐµÑÐºÐ¸Ð¼ ÑÐ¿Ð»Ð°Ð²Ð°Ð¼, Ð¸Ñ ÑÐ²Ð¾Ð¹ÑÑÐ²Ð°Ð¼, Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ð°Ð¼ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²Ð° Ð¸ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼ Ð¾ÑÐµÐ½ÐºÐ¸ GFA; 2) ÑÐ¾ÑÐ¼Ð°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Dmax ÐºÐ°Ðº Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸; 3) ÑÐ±Ð¾Ñ Ð¸ Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð´Ð°Ð½Ð½ÑÑ Ð¾ ÑÐ¾ÑÑÐ°Ð²Ðµ Ð¸ ÑÐµÑÐ¼Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐ²Ð¾Ð¹ÑÑÐ²Ð°Ñ ÐÐÐ¡, Ð²ÐºÐ»ÑÑÐ°Ñ Ð½Ð¾ÑÐ¼Ð°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¿ÑÐ¾Ð¿ÑÑÐµÐ½Ð½ÑÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹; 4) Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð²Ð·Ð°Ð¸Ð¼Ð¾ÑÐ²ÑÐ·ÐµÐ¹ Ð¼ÐµÐ¶Ð´Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ°Ð¼Ð¸ Ð¸ Ð¿Ð¾ÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½ÑÑ Ð²ÑÐ±ÑÐ¾ÑÐ¾Ð²; 5) Ð²ÑÐ±Ð¾Ñ Ð¸ Ð¾Ð±Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Dmax Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸; 6) Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð¸ Ð¾ÑÐµÐ½ÐºÐ° Ð²ÑÐ±ÑÐ°Ð½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÑÐ¸Ðº ÐºÐ°ÑÐµÑÑÐ²Ð° MSE Ð¸ R2, Ð° ÑÐ°ÐºÐ¶Ðµ K-ÐºÑÐ°ÑÐ½Ð¾Ð¹ Ð¿ÐµÑÐµÐºÑÐµÑÑÐ½Ð¾Ð¹ Ð¿ÑÐ¾Ð²ÐµÑÐºÐ¸; 7) Ð°Ð½Ð°Ð»Ð¸Ð· Ð²Ð°Ð¶Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ ÐºÐ»ÑÑÐµÐ²ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð², Ð²Ð»Ð¸ÑÑÑÐ¸Ñ Ð½Ð° Dmax; 8) Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð², ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸Ð·ÑÑÑÐ¸Ñ GFA, ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½Ð½Ð°Ñ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÐ° ÑÑÐµÐºÐ»Ð¾Ð²Ð°Ð½Ð¸Ñ (Trg) Ð¸ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÐ½ÑÐ¹ Ð¸Ð½ÑÐµÑÐ²Ð°Ð» ÑÑÐ°Ð±Ð¸Ð»ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÐµÑÐµÐ¾ÑÐ»Ð°Ð¶Ð´ÐµÐ½Ð½Ð¾Ð¹ Ð¶Ð¸Ð´ÐºÐ¾ÑÑÐ¸ (âTx); 9) Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ SHAP Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ Ð²ÐºÐ»Ð°Ð´Ð° ÐºÐ°Ð¶Ð´Ð¾Ð³Ð¾ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ° Ð² Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Dmax Ð¸ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð·Ð°ÐºÐ¾Ð½Ð¾Ð¼ÐµÑÐ½Ð¾- ÑÑÐµÐ¹ Ð²Ð»Ð¸ÑÐ½Ð¸Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð½Ð° GFA. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Tg, Tl, Tx, TEN, VA, Sm, d Ð¸ Dmax Ð±ÑÐ»Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÐºÑÑÑÐ°-Ð´ÐµÑÐµÐ²ÑÐµÐ². Ð­ÑÐ° Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¿ÑÐ¾Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð»Ð° Ð»ÑÑÑÐ¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð´ÑÑÐ³Ð¸Ð¼Ð¸ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð°Ð¼Ð¸. ÐÐ½Ð°Ð»Ð¸Ð· Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ SHAP Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ð» Ð½Ðµ ÑÐ¾Ð»ÑÐºÐ¾ Ð¾Ð¿ÑÐµÐ´ÐµÐ»Ð¸ÑÑ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð²Ð°Ð¶Ð½ÑÐµ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Dmax, Ð½Ð¾ Ð¸ Ð²ÑÑÐ²Ð¸ÑÑ Ð·Ð°ÐºÐ¾Ð½Ð¾Ð¼ÐµÑÐ½Ð¾ÑÑÐ¸ Ð¸Ñ Ð²Ð»Ð¸ÑÐ½Ð¸Ñ Ð½Ð° GFA, ÑÑÐ¾ Ð¿Ð¾Ð´ÑÐ²ÐµÑÐ¶Ð´Ð°ÐµÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð¾Ð¹ÑÑÐ² Ð°Ð¼Ð¾ÑÑÐ½ÑÑ Ð¼ÐµÑÐ°Ð»Ð»Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐ¿Ð»Ð°Ð²Ð¾Ð².
description: This work is devoted to the development and application of machine learning models for predicting the maximum diameter of an amorphous sample (Dmax) as a key indicator of glass forming ability (GFA) of amorphous metal alloys. The objectives of the study were: 1) literature review on amorphous metal alloys, their properties, production problems and GFA evaluation methods; 2) formalisation of the Dmax prediction problem as a regression problem; 3) Collection and preprocessing of data on the composition and thermal properties of amorphous metal alloys, including normalisation and analysis of missing values; 4) data visualisation to identify relationships between features and potential emissions; 5) selection and validation of an optimal machine learning model for Dmax prediction based on a comparative analysis of different regression algorithms; 6) training and evaluating the selected model using MSE and R2 quality metrics and K-fold cross-validation; 7) analysing the importance of features to identify key parameters, affecting Dmax; 8) Investigation of additional parameters that characterise GFA, such as the reduced glass transition temperature (Trg) and the temperature interval of the of supercooled liquid stability (âTx); 9) model interpretation using SHAP values to evaluate the contribution of each trait to the prediction of Dmax and to identify patterns of parameter effects on GFA. Based on the parameters Tg, Tl, Tx, TEN, VA, Sm, d and Dmax, a regression model was developed using the extra-tree algorithm. This model showed better results compared to other approaches. The analysis of SHAP values allowed not only to identify the most important parameters for Dmax prediction, but also to identify patterns of their influence on the GFA, which confirms the possibility of using machine learning methods to predict the properties of amorphous metal alloys.