107 citations · 120 across the 11 of their papers we have counts for
12 papers
Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets
Ali Ramlaoui, Daniel T. Speckhard, Sagar Pal +3
Atomistic machine learning datasets are increasingly used for training: large immutable snapshots are read repeatedly, shuffled across epochs, staged across clusters' storage syste…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
An exciting approach to theoretical spectroscopy
Martí Raya-Moreno, Alexander Buccheri, Noah Alexy Dasch +27
Theoretical spectroscopy, and more generally, electronic-structure theory, are powerful concepts for describing the complex many-body interactions in materials. They comprise a var…
Information-Theoretic Grid Topology Reconstruction using Low-Precision Smart Meter Data
Daniel T. Speckhard
Accurate knowledge of power grid topology is a prerequisite for effective state estimation and grid stability. While data-driven methods for topology reconstruction exist, the mini…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms
Daniel T. Speckhard, Tim Bechtel, Sebastian Kehl +2
Graph neural networks (GNN) have shown promising results for several domains such as materials science, chemistry, and the social sciences. GNN models often contain millions of par…
How big is Big Data?
Daniel T. Speckhard, Tim Bechtel, Luca M. Ghiringhelli +3
Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {\it big} means in the context of typical materials-science machi…
Band-gap regression with architecture-optimized message-passing neural networks
Tim Bechtel, Daniel T. Speckhard, Jonathan Godwin +1
Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) have shown great potential in predicting physical properties of solids. In this work, we trai…
Validity Constraints for Data Analysis Workflows
Florian Schintke, Ninon De Mecquenem, David Frantz +9
Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often chal…
Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models
Daniel T. Speckhard, Christian Carbogno, Luca Ghiringhelli +3
The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ult…
Neural Architecture Search for Energy Efficient Always-on Audio Models
Daniel T. Speckhard, Karolis Misiunas, Sagi Perel +3
Mobile and edge computing devices for always-on classification tasks require energy-efficient neural network architectures. In this paper we present several changes to neural archi…
OPTIMADE, an API for exchanging materials data
Casper W. Andersen, Rickard Armiento, Evgeny Blokhin +53
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible an…