249 citations · 270 across the 7 of their papers we have counts for
8 papers
AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces
Zongmin Zhang, Yuyang Lou, Bowen Zhang +6
Identifying the lowest-energy surface-adsorbate configuration is critical for modeling heterogeneous catalysis, yet exhaustive exploration with ab initio calculations is computatio…
On the Covalent Fields of Molecule-Surface Interactions
Edvin Fako, Philippe Schwaller
The ambiguity of the active site, the empirical status of Brønsted-Evans-Polanyi relations, and the unpredictability of linear scaling relation breakdown are three symptoms of a si…
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…
LeMat-Bulk: aggregating, and de-duplicating quantum chemistry materials databases
Martin Siron, Inel Djafar, Ali Ramlaoui +10
The rapid expansion of materials science databases has driven machine learning-based discovery while also posing challenges in data integration, duplication, and interoperability.…
Accelerating inverse materials design using generative diffusion models with reinforcement learning
Junwu Chen, Jeff Guo, Edvin Fako +1
Diffusion models promise to accelerate material design by directly generating novel structures with desired properties, but existing approaches typically require expensive and subs…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields
Lars Schaaf, Edvin Fako, Sandip De +2
In this study, we introduce a training protocol for developing machine learning force fields (MLFFs), capable of accurately determining energy barriers in catalytic reaction pathwa…
Lattice mismatch as the descriptor of segregation, stability and reactivity of supported thin catalyst films
Edvin Fako, Ana S. Dobrota, Igor A. Pašti +3
Increasing demand and high prices of advanced catalysts motivate a constant search for novel active materials with reduced content of noble metals. The development of thin films an…