ReactionAtlas is an iterative ML framework that proposes candidate reactions from seed molecules, filters them with an ML force field for valid transition states, and grows a network of ~47,000 reactions among ~12,000 compounds up to C4 in pre-biotic chemistry.
T.et al.Biomolecular dynamics with machine-learned quantum- mechanical force fields trained on diverse chemical fragments.Sci
4 Pith papers cite this work, alongside 112 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
LADeQ is an LLM-driven workflow that autonomously discovers and implements approximation algorithms for CCSD and CISD calculations, delivering speedups while respecting user-specified error tolerances.
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.
ORION is a universal ML force field for organic molecules that reaches near-DFT accuracy on forces while running 215 times faster than ReaxFF by using a unified top-down and bottom-up training dataset strategy.
citing papers explorer
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ReactionAtlas: Ab origine exploration of chemical reaction networks with machine learning
ReactionAtlas is an iterative ML framework that proposes candidate reactions from seed molecules, filters them with an ML force field for valid transition states, and grows a network of ~47,000 reactions among ~12,000 compounds up to C4 in pre-biotic chemistry.
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LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms
LADeQ is an LLM-driven workflow that autonomously discovers and implements approximation algorithms for CCSD and CISD calculations, delivering speedups while respecting user-specified error tolerances.
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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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ORION: Unifying Top-Down and Bottom-Up Chemical Space Sampling for a Universal Organic Force Field
ORION is a universal ML force field for organic molecules that reaches near-DFT accuracy on forces while running 215 times faster than ReaxFF by using a unified top-down and bottom-up training dataset strategy.