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
4 Pith papers cite this work, alongside 888 external citations. Polarity classification is still indexing.
4
Pith papers citing it
888
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
Reformulation of many-body dispersion via a correlation matrix yields pairwise force decomposition and unified energy-force-Hessian expressions.
DeltaDiff is a physics-guided inference method that predicts mutant protein structures from a baseline diffusion model without retraining, tested on three systems with nonlocal changes.
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
-
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.
-
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.
-
DeltaDiff: Training-Free, Physics-Guided Machine Learning for Predicting Mutant Protein Structures
DeltaDiff is a physics-guided inference method that predicts mutant protein structures from a baseline diffusion model without retraining, tested on three systems with nonlocal changes.
-
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.