2 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
The impact of spurious imaginary phonon modes on thermal properties of Metal-organic Frameworks
Prathami Divakar Kamath, Kristin A. Persson
Metal-organic Frameworks (MOFs) have emerged as potential candidates for direct air capture (DAC) of green house gases and water. Thermal properties of MOFs, such as their heat cap…
Machine Learned Potential for High-Throughput Phonon Calculations of Metal-Organic Frameworks
Alin Marin Elena, Prathami Divakar Kamath, Théo Jaffrelot Inizan +3
Metal-organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phono…