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publications
Kolmogorov–Arnold neural networks for high-entropy alloys design
Published in Modelling and Simulation in Materials Science and Engineering, 2025
A wide range of deep learning-based machine learning (ML) techniques are extensively applied to the design of high-entropy alloys (HEAs), yielding numerous valuable insights. Kolmogorov–Arnold networks (KAN) is a recently developed architecture that aims to improve both the accuracy and interpretability of input features. In this work, we explore three different datasets for HEA design and demonstrate the application of KAN for both classification and regression models. In the first example, we use a KAN classification model to predict the probability of single-phase formation in high-entropy carbide ceramics based on various properties such as mixing enthalpy and valence electron concentration. In the second example, we employ a KAN regression model to predict the yield strength and ultimate tensile strength of HEAs based on their chemical composition and process conditions including annealing time, cold rolling percentage, and homogenization temperature. The third example involves a KAN classification model to determine whether a certain composition is an HEA or non-HEA, followed by a KAN regressor model to predict the bulk modulus of the identified HEA, aiming to identify HEAs with high bulk modulus. In all three examples, KAN either outperform or match the performance in terms of accuracy such as F1 score for classification and mean square error, and coefficient of determination (R2) for regression of the multilayer perceptron by demonstrating the efficacy of KAN in handling both classification and regression tasks. We provide a promising direction for future research to explore advanced ML techniques, which lead to more accurate predictions and better interpretability of complex materials, ultimately accelerating the discovery and optimization of HEAs with desirable properties. Poster
Recommended citation: Yagnik Bandyopadhyay et al 2025 Modelling Simul. Mater. Sci. Eng. 33 035005
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talks
Tutorial for setting up dynamic mesh simulation in Ansys
Published:
I presented a tutorial on setting up a six degrees of freedom (6DOF) simulation in ANSYS Fluent. The latest version of Fluent enables such simulations without relying on user-defined functions (UDFs), which are often cumbersome and computationally inefficient. This tutorial was part of my Master’s applied project and is hosted on my advisor Dr. Jeonglae Kim’s YouTube channel, where it has received over 10,000 views.
Kolmogorov–Arnold neural networks for high-entropy alloys design
Published:
I presented poster for the publication work on Kolmogorov–Arnold neural networks for high-entropy alloys design
Quantum machine learning application in material science
Published:
I gave a talk on Quantum machine learning applications in material science. This talk was in 𝟐𝟎𝟐𝟓 𝐒𝐮𝐦𝐦𝐞𝐫 𝐒𝐜𝐡𝐨𝐨𝐥 𝐟𝐨𝐫 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 M𝐚𝐭𝐞𝐫𝐢𝐚𝐥𝐬 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐂𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲 at Purdue University main campus, 𝐀𝐮𝐠𝐮𝐬𝐭 𝟏𝟐-𝟏𝟓, 𝟐𝟎𝟐𝟓.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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