601 citations · 1.8k across the 12 of their papers we have counts for
12 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…
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel +28
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include…
Trends in atomistic simulation software usage
Leopold Talirz, Luca M. Ghiringhelli, Berend Smit
Driven by the unprecedented computational power available to scientific research, the use of computers in solid-state physics, chemistry and materials science has been on a continu…
Common workflows for computing material properties using different quantum engines
Sebastiaan P. Huber, Emanuele Bosoni, Marnik Bercx +23
The prediction of material properties through electronic-structure simulations based on density-functional theory has become routinely common, thanks, in part, to the steady increa…
A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"
Kevin Maik Jablonka, Fergus Mcilwaine, Susana Garcia +2
Nigam et al. reported a genetic algorithm (GA) utilizing the SELFIES representation and also propose an adaptive, neural network-based penalty that is supposed to improve the diver…
AiiDAlab -- an ecosystem for developing, executing, and sharing scientific workflows
Aliaksandr V. Yakutovich, Kristjan Eimre, Ole Schütt +10
Cloud platforms allow users to execute tasks directly from their web browser and are a key enabling technology not only for commerce but also for computational science. Research so…