From the 1 of 9 linked papers with an AI index.
9 papers
Fast and Accurate Foundation Models for Equivariant Machine-Learned Interatomic Potentials
Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang +12
The paper presents fast and accurate equivariant machine‑learned interatomic potentials (NequIP and Allegro) as foundation models that scale to ultra‑large datasets while maintaini…
Strain-Dependent Wetting of Graphene
Darren Wayne Lim, Xavier R. Advincula, William C. Witt +3
Understanding how water wets graphene is critical for predicting and controlling its behaviour in nanofluidic, sensing, and energy applications. A key measure of wetting is the con…
Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi +9
First-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational cost. While Machine Learning…
From Accurate Quantum Chemistry to Converged Thermodynamics for Ion Pairing in Solution
Niamh O'Neill, Benjamin X. Shi, William C. Witt +6
Quantitative prediction of thermodynamic properties in solution is essential for translating atomistic simulations into reliable chemical insight. As an exemplar system, the behavi…
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…
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
Dávid Péter Kovács, J. Harry Moore, Nicholas J. Browning +8
Classical empirical force fields have dominated biomolecular simulation for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dy…