GFFMERGE formulates GNN force field merging as a convex embedding-alignment problem with an analytical solution, recovering near joint-training performance on MD17, MD22, LiPS20 and other benchmarks while delivering 5-27x speedups.
Title resolution pending
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 7verdicts
UNVERDICTED 7roles
background 2polarities
background 2representative citing papers
A protocol is introduced to derive effective inertial and viscous-damping constants for nonequilibrium polarization dynamics in soft-mode ferroelectric PbTiO3.
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
Monte-Carlo simulations with an ML potential demonstrate that coherency strain removes the Ag-Cu miscibility gap in Ag_xCu_{1-x}GaSe2, producing complete mixing.
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
A multi-fidelity screening of 56 quinary HE-MBenes identifies 45 thermodynamically stable candidates and evaluates their CO2-to-CO pathway using DFT, MLIP, and AIMD.
A perspective advocating an integrated foundation AI model for inorganic materials that connects generative design, multi-modal databases, and experimental validation to address data-driven inverse design challenges.
citing papers explorer
-
GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
GFFMERGE formulates GNN force field merging as a convex embedding-alignment problem with an analytical solution, recovering near joint-training performance on MD17, MD22, LiPS20 and other benchmarks while delivering 5-27x speedups.
-
Effective dynamic constants for nonequilibrium third-principles simulations
A protocol is introduced to derive effective inertial and viscous-damping constants for nonequilibrium polarization dynamics in soft-mode ferroelectric PbTiO3.
-
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
-
Chemo-mechanical coupling stabilizes mixed $\mathrm{Ag}_{x}\mathrm{Cu}_{1-x}\mathrm{GaSe}_{2}$ solar-cell absorbers: Insights from Monte-Carlo simulations assisted by ab initio informed machine-learning potentials
Monte-Carlo simulations with an ML potential demonstrate that coherency strain removes the Ag-Cu miscibility gap in Ag_xCu_{1-x}GaSe2, producing complete mixing.
-
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
-
Multi-Fidelity Computational Screening of High-Entropy MBenes for CO$_2$ Electroreduction
A multi-fidelity screening of 56 quinary HE-MBenes identifies 45 thermodynamically stable candidates and evaluates their CO2-to-CO pathway using DFT, MLIP, and AIMD.
-
Generative design of inorganic materials
A perspective advocating an integrated foundation AI model for inorganic materials that connects generative design, multi-modal databases, and experimental validation to address data-driven inverse design challenges.