6 citations · 10 across the 13 of their papers we have counts for
4 papers · 2 filters
uMOF: A Universal Database, Benchmark, and Machine Learning Interatomic Potentials for Metal-Organic Frameworks
Théo Jaffrelot Inizan, Prathami Divakar Kamath, Alin Marin Elena +1
Foundation machine learning interatomic potentials (MLIPs) deliver near-ab-initio accuracy at a fraction of the computational cost, yet their promise for Metal-organic Frameworks (…
Data-driven Design of Metal-Organic Frameworks with Tunable Negative Thermal Expansion
Prathami Divakar Kamath, Francesco Tavani, Alin Marin Elena +6
Materials with negative thermal expansion (NTE) are essential for applications requiring precise control of thermal expansion. Owing to their exceptional chemical tunability, flexi…
Bond, orbital and spin order in d4/d6/d7 perovskite oxides: successes and limitations of foundation interatomic potentials
Swagata Acharya, Dimitar Pashov, Mark van Schilfgaarde +1
Foundation machine-learning interatomic potentials (MLIPs) are rapidly replacing density-functional theory (DFT) for modeling structure and nuclear dynamics, making their fidelity…
High-Pressure Inelastic Neutron Spectroscopy: A true test of Machine-Learned Interatomic Potential energy landscapes
Jeff Armstrong, Adam Jackson, Alin Elena
Machine-learned interatomic potentials (MLIPs) promise to provide near density-functional theory accuracy at a fraction of the computational cost, offering a transformative route t…