Artificial neural network assisted by first-principles calculations for predicting transformation temperatures in shape memory alloys

Abstract

A key property for the design of new shape memory alloys is their working temperature range that depends on their transformation temperature T0. In previous works, T0 was predicted using a simple linear regression with respect to the energy difference between the parent and the martensitic phases, [Formula: see text]E[Formula: see text]. In this paper, we developed an accurate method to predict T0 based on machine learning assisted by the first-principles calculations. First-principles calculations were performed on 15 shape memory alloys; then, we proposed an artificial neural network method that used not only computed [Formula: see text]E[Formula: see text] but also bulk moduli as input variables to predict T0. The prediction error of T0 was improved to 49 K for the proposed artificial neural network compared with 188 K for simple linear regression.

State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning

“…These alloys have made their way into highly sensitive scientific and biomedical application in recent times from 'stents' to open up clogged arteries in human body, to the wheels of extraterrestrial rover. Since these unique materials are used in some of the most sophisticated and expensive products it goes without saying that the most important property or characteristic of these shape memory alloy is none other than transformation temperature as it dictates the limitation of their application [41].…”

Optimal Crop Selection Using Gravitational Search Algorithm

“…e effective tool for facilitating crop diversification is planners for making sound decisions before each crop season. e main goal of crop diversification is to get numerous crops instead of only one or two [31]. ereafter, a crop is selected in such a way so that it gives maximum production with minimum investment [32].…”