Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
Transition1x - a dataset for building generalizable reactive machine learning potentials.Scientific Data, 9(1):779, December 2022
10 Pith papers cite this work, alongside 109 external citations. Polarity classification is still indexing.
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Skala is a neural XC functional trained on wavefunction data that beats state-of-the-art hybrids on main-group chemistry benchmarks at semi-local computational cost.
Drift-React produces full minimum energy pathways for reactions in a single step via SE(3) drifting fields, matching TS accuracy of iterative models with orders-of-magnitude speedup on Transition1x and Halo8 datasets.
TSAgent automates transition state searches at DFT accuracy via an agentic loop, reaching 83% success on 100 OC20NEB examples and 70% on 10 held-out cases versus 73% for human experts.
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
An open-source Snakemake workflow fully automates NEB reaction path calculations with ML potentials and recovers the known HCN-HNC energy profile without manual steps.
THEMol is a new large dataset of torsion, Hessian, energy, and multipole data from DFT for closed-shell organic molecules, organized into five subsets for use in molecular potential development.
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.
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.
Bayesian optimization with Gaussian processes unifies minimization, single-point saddle searches, and double-ended path searches on potential energy surfaces through a shared six-step surrogate loop using derivative observations and inverse-distance kernels.
citing papers explorer
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Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
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Accurate and scalable exchange-correlation with deep learning
Skala is a neural XC functional trained on wavefunction data that beats state-of-the-art hybrids on main-group chemistry benchmarks at semi-local computational cost.
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Drift-React: One-step Generation of Reaction Pathways via SE(3) Drifting Fields
Drift-React produces full minimum energy pathways for reactions in a single step via SE(3) drifting fields, matching TS accuracy of iterative models with orders-of-magnitude speedup on Transition1x and Halo8 datasets.
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TSAgent: An Agentic Workflow for Autonomous Transition State Search
TSAgent automates transition state searches at DFT accuracy via an agentic loop, reaching 83% success on 100 OC20NEB examples and 70% on 10 held-out cases versus 73% for human experts.
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Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
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Reproducible Orchestration of Best Practices for Reaction Path Optimization with the Nudged Elastic Band
An open-source Snakemake workflow fully automates NEB reaction path calculations with ML potentials and recovers the known HCN-HNC energy profile without manual steps.
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THEMol dataset: Torsion, Hessian, and Energy of Molecules
THEMol is a new large dataset of torsion, Hessian, energy, and multipole data from DFT for closed-shell organic molecules, organized into five subsets for use in molecular potential development.
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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.
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Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.
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A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches
Bayesian optimization with Gaussian processes unifies minimization, single-point saddle searches, and double-ended path searches on potential energy surfaces through a shared six-step surrogate loop using derivative observations and inverse-distance kernels.