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.
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SMC-AI scales Monte Carlo simulations to 4 trillion atoms on AI hardware clusters, achieving 32 times larger systems and 1.3 times higher throughput than prior records while decoupling ML models from the simulation core.
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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.
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SMC-AI: Scaling Monte Carlo Simulation to Four Trillion Atoms with AI Accelerators
SMC-AI scales Monte Carlo simulations to 4 trillion atoms on AI hardware clusters, achieving 32 times larger systems and 1.3 times higher throughput than prior records while decoupling ML models from the simulation core.