FALCON: Fast Active Learning for Machine Learning Potentials in Atomistic and ab initio Molecular Dynamics Simulations

FALCON: Fast Active Learning for Machine Learning Potentials in Atomistic and ab initio Molecular Dynamics Simulations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article FALCON: Fast Active Learning for Machine Learning Potentials in Atomistic and ab initio Molecular Dynamics Simulations Wilke Dononelli, Noah Felis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7096218/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The use of machine learning (ML) techniques has become increasingly important in computational chemistry and materials science, in recent years. ML potentials can be used for the construction of potential energy surfaces (PES) to avoid computationally expensive ab initio methods. However, many such applications still require a significant number of first-principles calculations to train the ML model, prior to use. Active learning methods can address this issue by performing these calculations and trainings "on-the-fly", based on the ML model’s uncertainty estimation. Nevertheless, current active learning approaches suffer from problems in complex simulations were frequent retraining is required, since repeated training of a large ML model increases training times substantially. This work presents a solution to this limitation by introducing the FALCON (Fast Active Learning for Computational ab initio mOlecular dyNamics) calculator. Instead of relying on a single large ML model, FALCON clusters the training data into subsets of similar structures and distributes them across multiple smaller ML models. This approach significantly increases the efficiency of the OTF training, drastically reducing the computational cost of training-intensive simulations. The use of FALCON is demonstrated on various molecular dynamics (MD) simulations of bulk metals, metal clusters and water diffusion in a carbon nanotube. However, the FALCON calculator is highly flexible and could be easily adapted for various applications and different ML models. Physical sciences/Materials science/Theory and computation/Atomistic models Physical sciences/Chemistry/Theoretical chemistry/Molecular dynamics Physical sciences/Chemistry/Theoretical chemistry/Method development Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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This approach significantly increases the efficiency of the OTF training, drastically reducing the computational cost of training-intensive simulations. The use of FALCON is demonstrated on various molecular dynamics (MD) simulations of bulk metals, metal clusters and water diffusion in a carbon nanotube. 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