2025-04-09 09:00:00
Current material genomics approaches face challenges in efficiently exploring the high dimensional design space of polymer-bonded explosives (PBX), which play a critical role in understanding their sensitivity under shock loading conditions. These challenges lead to high computational costs and limited insights. Our innovative Bayesian…
Over 50 years into their pivotal role in scientific exploration, materials and molecular modeling encounter challenges stemming from diverse code structures, languages, and input styles. The complexity escalates with the rise of machine learning. Addressing this, the open-source community explores solutions, from scripting libraries…
With the rise of ab-initio computations, promising new materials are being predicted at an unprecedented rate. Yet, the synthesis of these materials remains challenging, often requiring months or even years of trial-and-error experimentation. In this talk, I will discuss the development of an autonomous…
Large databases of crystalline materials and associated properties, typically computed using density functional theory (DFT), have become widely available and used for training machine learning (ML) models. Thermodynamic stability, band gap, and elastic mechanical properties are now available in large volumes with sufficient accuracy…
To be presented by Tara Boland The field of two-dimensional (2D) materials has evolved with tremendous pace over the past decade and is currently impacting many contemporary research fields including spintronics, valleytronics, unconventional superconductivity, multiferroics, and quantum light sources. To support the ongoing research…
During this presentation, we’ll discuss 1) large-scale materials database development such as JARVIS-DFT, which acts as a precursor for data-informatics applications, 2) graph neural networks for property predictions and force-field development, 2) computer vision tasks for atomistic image analysis, 3) large language models for…
For the past 15 years, the OpenKIM cyberinfrastructure has provided the atomistic simulation community with a curated repository of interatomic potentials (IPs) tested for coding integrity and material property predictions, as well as an API to manage IPs and deploy them in popular simulation…
Autonomous experimentation is transforming the landscape of materials synthesis and characterization, a domain traditionally slowed by time-consuming and repetitive tasks. In extreme environments, which pose unique challenges, these systems can excel by efficiently extracting the most critical information, streamlining processes in these demanding settings….
Due to recent unprecedented innovations in computing power, data-driven methods like Machine Learning (ML) have risen in popularity in the field of solid mechanics. Specifically, ML models have been fit to material potential energy surfaces (PES) due to their ability to reach ab initio…
Hopkinson pressure bars can achieve the same mechanical pressures as falling-weight machines and have an additional advantage in that they represent a closed mechanical system that is relatively easily describable in quantitative terms. The compressive force incident on the sample can be expressed in…
