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CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment

npj Computational Materials 2023年第1期9卷 2106-2117页

作者： suvo banik Debdas Dhabal Henry Chan Sukriti Manna Mathew Cherukara Valeria Molinero Subramanian K.R.S.Sankaranarayanan Center for Nanoscale Materials Argonne National LaboratoryLemontIL 60439USA Department of Mechanical and Industrial Engineering University of IllinoisChicagoIL 60607USA Department of Chemistry The University of UtahSalt Lake CityUT 84112USA Advanced Photon Source Argonne National LaboratoryLemontIL 60439USA

We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture tolearn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) anddiverse classes ranging from metals, oxides, non-metals to hierarchical materials such as zeolites and semi-ordered *** can classify based on a global, structure-level representation such as space group and dimensionality (e.g., bulk, 2D,clusters, etc.). Using representative materials such as polycrystals and zeolites, we demonstrate its transferability in performing localatom-level classification tasks, such as grain boundary identification and other heterointerfaces. CEGANN classifies in (thermal)noisy dynamical environments as demonstrated for representative zeolite nucleation and growth from an amorphous ***, we use CEGANN to classify multicomponent systems with thermal noise and compositional diversity. Overall, our approachis material agnostic and allows for multiscale feature classification ranging from atomic-scale crystals to heterointerfaces tomicroscale grain boundaries.

关键词： Crystal classify zeolite

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A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery

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npj Computational Materials 2023年第1期9卷 500-515页

作者： suvo banik Troy Loefller Sukriti Manna Henry Chan Srilok Srinivasan Pierre Darancet Alexander Hexemer Subramanian K.R.S.Sankaranarayanan Center for Nanoscale Materials Argonne National LaboratoryLemontIL 60439USA Department of Mechanical and Industrial Engineering University of IllinoisChicagoIL 60607USA Advanced Light Source(ALS)Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA

Material properties share an intrinsic relationship with their structural attributes,making inverse design approaches crucial for discovering new materials with desired *** Learning(RL)approaches are emerging as powerful inverse design tools,often functioning in discrete action *** constrains their application in materials design problems,which involve continuous search ***,we introduce an RL-based framework CASTING(Continuous Action Space Tree Search for inverse design),that employs a decision tree-based Monte Carlo Tree Search(MCTS)algorithm with continuous space adaptation through modified policies and *** representative examples like Silver(Ag)for metals,Carbon(C)for covalent systems,and multicomponent systems such as graphane,boron nitride,and complex correlated oxides,we showcase its accuracy,convergence speed,and scalability in materials discovery and ***,with the inverse design of super-hard Carbon phases,we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences.

关键词： Action framework Tree

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