activity
20122023
most citedA General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials

1.8k citations · 2.6k across the 21 of their papers we have counts for

collaborators

23 papers

cond-mat.mtrl-sci2023★ 214 cited

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…

cond-mat.mtrl-sci2023★ 24 cited

Machine Learning Prediction of Critical Cooling Rate for Metallic Glasses From Expanded Datasets and Elemental Features

Benjamin T. Afflerbach, Carter Francis, Lane E. Schultz +9

We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model wa…

cs.DC2023★ 1 cited

Accelerating Communications in Federated Applications with Transparent Object Proxies

J. Gregory Pauloski, Valerie Hayot-Sasson, Logan Ward +5

Advances in networks, accelerators, and cloud services encourage programmers to reconsider where to compute -- such as when fast networks make it cost-effective to compute on remot…

cs.DC2023

Workflows Community Summit 2022: A Roadmap Revolution

Rafael Ferreira da Silva, Rosa M. Badia, Venkat Bala +102

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex s…

cs.DC2023★ 11 cited

Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources

Logan Ward, J. Gregory Pauloski, Valerie Hayot-Sasson +7

Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supe…

cond-mat.mtrl-sci2022★ 48 cited

Quantifying the performance of machine learning models in materials discovery

Christopher K. H. Borg, Eric S. Muckley, Clara Nyby +4

The predictive capabilities of machine learning (ML) models used in materials discovery are typically measured using simple statistics such as the root-mean-square error (RMSE) or…