Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.
Machine Learning for Molecular Simulation
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Reformulation of many-body dispersion via a correlation matrix yields pairwise force decomposition and unified energy-force-Hessian expressions.
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.
citing papers explorer
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Generative Pseudo-Force Fields for Molecular Generation
Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.
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Structured force reformulation of many-body dispersion: towards effective atom--atom decomposition and surrogate modeling
Reformulation of many-body dispersion via a correlation matrix yields pairwise force decomposition and unified energy-force-Hessian expressions.
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Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.