Workshop for AI-Powered Materials Discovery at Great Plains

Session

Parallel Session G: AI and Data-Driven Design of Functional Materials and Interfaces

Jun 24, 2025, 1:30 PM

Room 216 MUC

Conveners

Parallel Session G: AI and Data-Driven Design of Functional Materials and Interfaces

• Chaoyang Jiang

10. Fundamental Insights into Charge TransferAcross 2D-Bulk Heterostructure Interfaces Using Machine Learning.

Mubin Md Al Furkan (Department of Physics, Arizona State University, Tempe, Arizona, USA)

6/24/25, 1:30 PM

Physics-Informed Machine Learning and Quantum Computing for Advanced Material Design

Two-dimensional (2D) materials placed on top of bulk substrates form complex heterostructures with rich interfacial physics with potential use in various applications such as nanoelectronics, sensing, and energy conversion. While numerous prior works have studied 2D-2D heterostructures, there have been relatively fewer studies that explore interfaces of 2D-bulk material heterostructures. In...

14. Gradient Boosting Regression Models for Predicting Fouling Release Performance Based on PDMS and SBMA Molecular Weights

Gerardo Casanola (North Dakota State University)

6/24/25, 1:54 PM

The Role of LLMs, Scientific ML, and Data-Driven Approaches in Materials Innovation

Fouling release performance is a critical factor in marine coatings, influencing their effectiveness in preventing biofouling. In this study, Gradient Boosting Regressor (GBM) models were developed to predict fouling release properties based on experimental data for assays performed for C. lytica at 10 psi and 20 psi and N. incerta at 20 psi. The coating systems analyzed consisted of SBMA...

29. High-Throughput Calculations of Phase Change Materials for Energy-Efficient Computing

Gabriella Townsley (University of Kansas)

6/24/25, 2:18 PM

The Role of LLMs, Scientific ML, and Data-Driven Approaches in Materials Innovation

Phase change materials (PCMs), which can be reversibly switched between their high-resistance and low-resistance phases, are promising for non-volatile, high-density data storage and research in non-von Neumann computing architectures. Recent discovery and development of novel PCM superlattices consisting of various layers have demonstrated unprecedented low power and high-density at...

30. Machine-Learning-Driven Identification of Materials Dimension from Structural and Physical Features

Naina Maharjan (University of South Dakota)

6/24/25, 2:42 PM

Automation in AI-Driven Laboratories and Material Discovery

Abstract
Identification of materials with two-dimensional properties is crucial for the creation of next-generation technologies, yet remains a computationally intensive task. The study presents an automated pipeline designed to predict if a material has two-dimensional characteristics from its physical and structural characteristics. Two-dimensional (2D) materials are a unique category of...

45. First-principles study of penta-CN2 quantum dots for efficient Hydrogen Evolution Reaction

Rupali Jindal (university of arizona)

6/24/25, 3:06 PM

AI Applications in Healthcare, Clean Energy, and Semiconductor Materials

The objective of our research is to investigate the electrocatalytic properties of novel metal-free quantum dots (QDs) composed of the recently discovered 2D material penta-CN2, with the aim of replacing costly and scarce catalysts such as Pt and Pd. Employing a first-principles density functional theory (DFT) based approach, the geometries of the three penta-CN2 quantum dots (QDs) of...