activity
20202026
most citedBig-Data Science in Porous Materials: Materials Genomics and Machine Learning

601 citations · 1.2k across the 19 of their papers we have counts for

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8 papers · 1 filter

cs.LG2026

MatBind: A Shared Embedding Space for Multimodal Materials Characterization

Le Yang, Anoop K. Chandran, Jona Östreicher +8

Fully characterizing a crystalline material requires integrating heterogeneous data sources -- atomic structures, diffraction patterns, electronic density of states, and natural la…

cs.LG2026

Reducing cross-sample prediction churn in scientific machine learning

Gordan Prastalo, Kevin Maik Jablonka

Scientific machine learning reports predictive performance. It does not report whether the same prediction would survive a different draw of training data. Across chemistry ben…

cs.LG2025★ 1 cited

General-Purpose Models for the Chemical Sciences: LLMs and Beyond

Nawaf Alampara, Anagha Aneesh, Martiño Ríos-García +6

Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, f…

cs.LG2025

ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models

Adrian Mirza, Nawaf Alampara, Martiño Ríos-García +12

Foundation models have shown remarkable success across scientific domains, yet their impact in chemistry remains limited due to the absence of diverse, large-scale, high-quality da…

cs.LG2024★ 6 cited

Probing the limitations of multimodal language models for chemistry and materials research

Nawaf Alampara, Mara Schilling-Wilhelmi, Martiño Ríos-García +5

Recent advancements in artificial intelligence have sparked interest in scientific assistants that could support researchers across the full spectrum of scientific workflows, from…

cs.LG2024★ 5 cited

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…

cs.LG2024★ 19 cited

Are large language models superhuman chemists?

Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu +32

Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. Howeve…

cs.LG2021

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…