Oak Ridge National Laboratory

Transition-state searches have long started from hand-chosen ground-state structures, which can miss metastable configurations that may govern rates at finite temperature. A workflow pairs global configurational search with a lightweight graph neural network: it samples many reactant, product and intermediate configurations on the potential energy surface, then predicts transition-state geometries with an edge-aware attention network of about 30,000 trainable parameters. On propane dehydrogenation over Pt4 supported on carbon nitride, the workflow recovers the known mechanism and yields competing pathways absent from earlier hand-built models; the authors also note that the metastable tetrahedral Pt4, though intrinsically more reactive, is too costly to form to dominate actual kinetics.

Transition-state searches have long started from hand-chosen ground-state structures, which can miss metastable configurations that may govern rates at finite temperature. A workflow pairs global configurational search with a lightweight graph neural network: it samples many reactant, product and intermediate configurations on the potential energy surface, then predicts transition-state geometries with an edge-aware attention network of about 30,000 trainable parameters. On propane dehydrogenation over Pt4 supported on carbon nitride, the workflow recovers the known mechanism and yields competing pathways absent from earlier hand-built models; the authors also note that the metastable tetrahedral Pt4, though intrinsically more reactive, is too costly to form to dominate actual kinetics.

Unlearning a large language model no longer requires diffuse updates across the whole model. FOM-UL scores each transformer layer by the ratio of its forget-set gradient norm to its retain-set gradient norm, updates only the few high-scoring layers, and adds layers one at a time when forgetting falls short. Across TOFU, KnowUnDo and MUSE-style evaluations it leaves less residual memorization than GA, NPO and KLD baselines while keeping retain-set utility close to the vanilla model, and it holds a favorable forgetting-utility trade-off under 8-bit and 4-bit post-training quantization, though residual memorization does rise under 4-bit relative to full precision.

Unlearning a large language model no longer requires diffuse updates across the whole model. FOM-UL scores each transformer layer by the ratio of its forget-set gradient norm to its retain-set gradient norm, updates only the few high-scoring layers, and adds layers one at a time when forgetting falls short. Across TOFU, KnowUnDo and MUSE-style evaluations it leaves less residual memorization than GA, NPO and KLD baselines while keeping retain-set utility close to the vanilla model, and it holds a favorable forgetting-utility trade-off under 8-bit and 4-bit post-training quantization, though residual memorization does rise under 4-bit relative to full precision.