Edvin Fako

BASF (United States) — United States

Materials Science · Physical Sciences

19h-index2.5kcitations64works4.92yr avg
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Research Topics

Catalytic Processes in Materials Science(19), Machine Learning in Materials Science(17), Nanomaterials for catalytic reactions(17), Catalysis and Oxidation Reactions(8), Electrocatalysts for Energy Conversion(7)

Publications62 total

Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence
Journal of the American Chemical Society·2026
Simple Heuristics for Advanced Sampling of Reactive Species on Surfaces
ACS Catalysis·2026
A foundation model for atomistic materials chemistry
The Journal of Chemical Physics·2025· 65 citedOpen Access
Accelerating inverse materials design using generative diffusion models with reinforcement learning
arXiv (Cornell University)·2025Open Access
Copper Gallium Aluminum Mixed Metal Oxides as Alternative Catalyst Candidates for Efficient Conversion of Carbon Dioxide to Methanol and Dimethyl Ether
ACS Catalysis·2025
MLIPX: machine-learned interatomic potential eXploration
Journal of Physics Condensed Matter·2025· 3 citedOpen Access
MLIPX: Machine Learned Interatomic Potential eXploration
2025Open Access
Copper Gallium Aluminum Mixed Metal Oxides as new Alternative Catalyst Candidates for Efficient Conversion of Carbon Dioxide to Methanol and Dimethyl Ether
2025Open Access
Simple Heuristics for Advanced Sampling of Reactive Species on Surfaces
2025· 2 citedOpen Access
Exploring the Intricacies of Glycerol Hydrodeoxygenation on Copper Surface: A Comprehensive Investigation with the Aid of Machine Learning Force Field
The Journal of Physical Chemistry C·2025· 5 cited
From Poison to Promotor: Spatially Isolated Metal Sites in Supported Rhodium Sulfides as Hydroformylation Catalysts
Small Structures·2025· 1 citedOpen Access
Exploring the Intricacies of Glycerol Hydrodeoxygenation to Propanediol on Cu surface: A Comprehensive Investigation with the Aid of Machine Learning Forcefield
2024Open Access
From Poison to Promotor: Spatially Isolated Metal Sites in Supported Rhodium Sulfides as Hydroformylation Catalysts
Small Structures·2024· 1 citedOpen Access
From poison to promotor –unique Rh4 structural motifs in supported rhodium sulphides as hydroformylation catalysts
2024· 1 citedOpen Access
Exploring the Intricacies of Glycerol Hydrodeoxygenation to Propanediol on Cu Catalysts: A Comprehensive Investigation with the Aid of Machine Learning Forcefield
2024
From poison to promoter: investigation of supported rhodium sulphides as heterogeneous hydroformylation catalysts
2024· 1 citedOpen Access
A foundation model for atomistic materials chemistry
arXiv (Cornell University)·2023· 229 citedOpen Access
Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields
npj Computational Materials·2023· 67 citedOpen Access
Data‐driven Design of Enhanced In‐based Catalyst for CO<sub>2</sub> to Methanol Reaction
ChemCatChem·2023· 5 citedOpen Access
Data deposit accompanying Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
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Frequent Co-authors

Núria López(25), Javier Pérez‐Ramírez(16), Sandip De(16), Sharon Mitchell(10), Roland Hauert(10), Оlga V. Safonova(9), Zupeng Chen(7), Paul A. Midgley(7), Selina K. Kaiser(7), Stephan A. Schunk(7), Evgeniya Vorobyeva(6), Sean M. Collins(6), Lars L. Schaaf(6), Frank Krumeich(5), Ansgar Schäfer(5), Ronghe Lin(4), Ana S. Dobrota(4), Natalia V. Skorodumova(4), Igor A. Pašti(4), Gábor Cśanyi(4)