Machine Learning(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 o...Machine Learning(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”(OTF),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 largeMLmodel 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 largeMLmodel,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,cathode materials 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.展开更多
In moirésystems,the impact of lattice relaxation on electronic band structures is significant,yet the computational demands of first-principles relaxation are prohibitively high due to the large number of atoms i...In moirésystems,the impact of lattice relaxation on electronic band structures is significant,yet the computational demands of first-principles relaxation are prohibitively high due to the large number of atoms involved.To address this challenge,Weintroduce a robust methodology for the construction of machine learning potentials specifically tailored for moiréstructures and present an open-source software package DPmoire designed to facilitate this process.Utilizing this package,we have developed machine learning force fields(MLFFs)for MX_(2)(M=Mo,W;X=S,Se,Te)materials.Our approach not only streamlines the computational process but also ensures accurate replication of the detailed electronic and structural properties typically observed in density functional theory(DFT)relaxations.The MLFFs were rigorously validated against standard DFT results,confirming their efficacy in capturing the complex interplay of atomic interactions within these layered materials.展开更多
基金funding by the Central Research Development Fund of the University of Bremen.
文摘Machine Learning(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”(OTF),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 largeMLmodel 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 largeMLmodel,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,cathode materials 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.
基金supported by the Science Center of the National Natural Science Foundation of China(Grant no.12188101)the National Key R&D Program of China(Grant no.2023YFA1607400,2024YFA1408400,2022YFA1403800)the National Natural Science Foundation of China(Grant nos.12274436,11925408,11921004).H.W.acknowledges support from the New Cornerstone Science Foundation through the XPLORER PRIZE.The AI-driven experiments,simulations and model training were performed on the robotic AI-Scientist platform of the Chinese Academy of Science.
文摘In moirésystems,the impact of lattice relaxation on electronic band structures is significant,yet the computational demands of first-principles relaxation are prohibitively high due to the large number of atoms involved.To address this challenge,Weintroduce a robust methodology for the construction of machine learning potentials specifically tailored for moiréstructures and present an open-source software package DPmoire designed to facilitate this process.Utilizing this package,we have developed machine learning force fields(MLFFs)for MX_(2)(M=Mo,W;X=S,Se,Te)materials.Our approach not only streamlines the computational process but also ensures accurate replication of the detailed electronic and structural properties typically observed in density functional theory(DFT)relaxations.The MLFFs were rigorously validated against standard DFT results,confirming their efficacy in capturing the complex interplay of atomic interactions within these layered materials.
