aceData-efficient exploration of atomic clusters via uncertainty quantification on complex potential energy surfaces
Machine learning interatomic potentials have become an effective method for exploring complex potential energy surfaces; however, their application to atomic clusters is frequently hindered by the high cost of sampling diverse isomer spaces and the difficulty in ensuring model generalizability across complex energy landscapes. While uncertainty quantification (UQ) offers a pathway to mitigate data scarcity, its efficacy in capturing continuous potential energy surface features and guiding active learning within…
Data-efficient exploration of atomic clusters via uncertainty quantification on complex potential energy surfaces: Furthermore, active learning driven by MCD significantly reduces the computational overhead of first-principles calculations while maintaining high predictive accuracy.
Evidence
- Peer-reviewedThe Journal of Chemical Physics2026-09-01
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Truvace Impact Record TRV-2026-0959, v1: “Data-efficient exploration of atomic clusters via uncertainty quantification on complex potential energy surfaces.” Truvace, 2026-09-02. /record/TRV-2026-0959 (accessed at citation time). sha256 c51c0663b52f505a…
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