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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Presentation Title Metadynamics-Accelerated Active Learning of Interatomic Potentials for InP Magic-Sized Cluster Nucleation
Author(s) Myeongjin Chung, Gyu Weon Hwang, Sangtae Kim
On-Site Speaker (Planned) Myeongjin Chung
Abstract Scope Indium phosphide(InP) quantum dots are excellent Cd-free emitters, yet their synthesis proceeds through non-classical nucleation paths forming persistent magic-sized clusters(MSCs), complicating size control. Resolving these pathways requires prolonged atomistic simulations, yet ab initio molecular dynamics(AIMD) cannot access experimentally relevant precursor-ligand-solvent lengthscales. Here, we introduce an active-learning workflow that trains a moment tensor potential(MTP) from pretrained universal neural-network interatomic potentials(PFP) data. Two-dimensional metadynamics are employed with total In-P coordination and largest In-P connected-cluster size as collective variables to accelerate the cluster-forming events. Benchmarks show MTP reproduces PFP/DFT energies and forces on identical configurations, while MTP-MD agrees with PFP-MD/AIMD in radial distribution functions. The results from 2D metadynamics based on these trained MTP will be discussed, emphasizing on the free-energy landscapes versus temperature, In/P precursor and trioctylphosphine concentrations in >10,000-atom systems, identifying metastable-cluster with stability, composition, and coordination motifs. These landscapes guide potential predictive optimization of synthesis conditions for monodispersed InP QDs.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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