4 papers
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…
An accurate and efficient framework for modelling the surface chemistry of ionic materials
Benjamin X. Shi, Andrew S. Rosen, Tobias Schäfer +4
Quantum-mechanical simulations can offer atomic-level insights into chemical processes on surfaces. This understanding is crucial for the rational design of new solid catalysts as…
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…
Deep Learning of ab initio Hessians for Transition State Optimization
Eric C. -Y. Yuan, Anup Kumar, Xingyi Guan +5
Identifying transition states -- saddle points on the potential energy surface connecting reactant and product minima -- is central to predicting kinetic barriers and understanding…