214 citations · 482 across the 14 of their papers we have counts for
14 papers
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali +50
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To…
Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder
Takuya Kurihana, James Franke, Ian Foster +2
Clouds play a critical role in the Earth's energy budget and their potential changes are one of the largest uncertainties in future climate projections. However, the use of satelli…
Cloud Classification with Unsupervised Deep Learning
Takuya Kurihana, Ian Foster, Rebecca Willett +6
We present a framework for cloud characterization that leverages modern unsupervised deep learning technologies. While previous neural network-based cloud classification models hav…
AICCA: AI-driven Cloud Classification Atlas
Takuya Kurihana, Elisabeth Moyer, Ian Foster
Clouds play an important role in the Earth's energy budget and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help…
3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization
Jim James, Nathan Pruyne, Tiberiu Stan +6
Dendritic microstructures are ubiquitous in nature and are the primary solidification morphologies in metallic materials. Techniques such as x-ray computed tomography (XCT) have pr…
Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing
Logan Ward, Ganesh Sivaraman, J. Gregory Pauloski +9
Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform. Methods that use…
Principles of the Battery Data Genome
Logan Ward, Susan Babinec, Eric J. Dufek +24
Electrochemical energy storage is central to modern society -- from consumer electronics to electrified transportation and the power grid. It is no longer just a convenience but a…
KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networks
J. Gregory Pauloski, Qi Huang, Lei Huang +4
Kronecker-factored Approximate Curvature (K-FAC) has recently been shown to converge faster in deep neural network (DNN) training than stochastic gradient descent (SGD); however, K…
BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes
Zhengchun Liu, Rajkumar Kettimuthu, Michael E. Papka +1
Supercomputer FCFS-based scheduling policies result in many transient idle nodes, a phenomenon that is only partially alleviated by backfill scheduling methods that promote small j…
Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval
Zhengchun Liu, Ahsan Ali, Peter Kenesei +11
Extremely high data rates at modern synchrotron and X-ray free-electron laser light source beamlines motivate the use of machine learning methods for data reduction, feature detect…
Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder
Takuya Kurihana, Elisabeth Moyer, Rebecca Willett +2
Advanced satellite-born remote sensing instruments produce high-resolution multi-spectral data for much of the globe at a daily cadence. These datasets open up the possibility of i…
Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
E. A. Huerta, Asad Khan, Xiaobo Huang +10
The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astro…
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
Dipendra Jha, Logan Ward, Zijiang Yang +5
Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applyi…
Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations
Logan Ward, Ben Blaiszik, Ian Foster +3
Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost. Here, we ass…