MP2SS reduces finite-size errors in periodic MP2 to millihartree accuracy at coarser k-point meshes for gapped systems via auxiliary function subtraction.
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Transition path sampling serves as an active learning engine to build machine-learned potentials accurate in barrier regions, enabling discovery of multiple protonation mechanisms in CO2 reduction on copper.
SA-ADAPT reaches near-CASSCF accuracy for a multiconfigurational surface chemistry benchmark using far fewer operators than SA-fUCCSD, with a modified selection scheme speeding convergence.
DFT calculations show guest atom ionization potential controls stability and rattler motion in A8T27Pn19 clathrates, spin-orbit coupling matters for heavy elements, and synthesis yields new compounds but misses the target phases.
A finite-displacement workflow is established to isolate numerical artifacts causing imaginary phonon modes in MOF-5 and to analyze remaining modes via mapping and Monte Carlo distortions for dynamical stability assessment.
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Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials
Transition path sampling serves as an active learning engine to build machine-learned potentials accurate in barrier regions, enabling discovery of multiple protonation mechanisms in CO2 reduction on copper.