50 citations · 63 across the 4 of their papers we have counts for
4 papers
Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions
Yuji Ikeda, Axel Forslund, Pranav Kumar +4
Machine-learning interatomic potentials (MLIPs) enable large-scale atomistic simulations at moderate computational cost while retaining ab initio accuracy. MLIPs trained on coupled…
Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs
Axel Forslund, Jong Hyun Jung, Yuji Ikeda +1
We propose a free-energy-perturbation approach accelerated by machine-learning potentials to efficiently compute transition temperatures and entropies for all rungs of Jacob's ladd…
Temperature dependence of (111) and (110) ceria surface energy
A. S. Kholtobina, A. Forslund, A. V. Ruban +2
High temperature properties of ceria surfaces are important for many applications. Here we report the temperature dependences of surface energy for the (111) and (110) CeO2 obtaine…
High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials
Jong Hyun Jung, Prashanth Srinivasan, Axel Forslund +1
Accurate prediction of thermodynamic properties requires an extremely accurate representation of the free energy surface. Requirements are twofold -- first, the inclusion of the re…