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  • Springer Science and Business Media LLC  (4)
  • Manna, Sukriti  (4)
  • Sankaranarayanan, Subramanian K. R. S.  (4)
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  • Springer Science and Business Media LLC  (4)
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  • 1
    Online Resource
    Online Resource
    Learning in continuous action space for developing high dimensional potential energy models
    Springer Science and Business Media LLC ; 2022
    In:  Nature Communications Vol. 13, No. 1 ( 2022-01-18)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2022-01-18)
    Abstract: Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a “window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-021-27849-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2553671-0
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  • 2
    Online Resource
    Online Resource
    A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery
    Springer Science and Business Media LLC ; 2023
    In:  npj Computational Materials Vol. 9, No. 1 ( 2023-09-30)
    Details
    In: npj Computational Materials, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2023-09-30)
    Abstract: Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as powerful inverse design tools, often functioning in discrete action spaces. This constrains their application in materials design problems, which involve continuous search spaces. Here, 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 sampling. Using 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 design. Furthermore, 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.
    Type of Medium: Online Resource
    ISSN: 2057-3960
    URL: Article
    DOI: 10.1038/s41524-023-01128-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2843287-3
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  • 3
    Online Resource
    Online Resource
    CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment
    Springer Science and Business Media LLC ; 2023
    In:  npj Computational Materials Vol. 9, No. 1 ( 2023-02-16)
    Details
    In: npj Computational Materials, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2023-02-16)
    Abstract: We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) and diverse classes ranging from metals, oxides, non-metals to hierarchical materials such as zeolites and semi-ordered mesophases. CEGANN 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 local atom-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 mixture. Finally, we use CEGANN to classify multicomponent systems with thermal noise and compositional diversity. Overall, our approach is material agnostic and allows for multiscale feature classification ranging from atomic-scale crystals to heterointerfaces to microscale grain boundaries.
    Type of Medium: Online Resource
    ISSN: 2057-3960
    URL: Article
    DOI: 10.1038/s41524-023-00975-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2843287-3
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  • 4
    Online Resource
    Online Resource
    Multi-reward reinforcement learning based development of inter-atomic potential models for silica
    Springer Science and Business Media LLC ; 2023
    In:  npj Computational Materials Vol. 9, No. 1 ( 2023-07-18)
    Details
    In: npj Computational Materials, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2023-07-18)
    Abstract: Silica is an abundant and technologically attractive material. Due to the structural complexities of silica polymorphs coupled with subtle differences in Si–O bonding characteristics, the development of accurate models to predict the structure, energetics and properties of silica polymorphs remain challenging. Current models for silica range from computationally efficient Buckingham formalisms (BKS, CHIK, Soules) to reactive (ReaxFF) and more recent machine-learned potentials that are flexible but computationally costly. Here, we introduce an improved formalism and parameterization of BKS model via a multireward reinforcement learning (RL) using an experimental training dataset. Our model concurrently captures the structure, energetics, density, equation of state, and elastic constants of quartz (equilibrium) as well as 20 other metastable silica polymorphs. We also assess its ability in capturing amorphous properties and highlight the limitations of the BKS-type functional forms in simultaneously capturing crystal and amorphous properties. We demonstrate ways to improve model flexibility and introduce a flexible formalism, machine-learned ML-BKS, that outperforms existing empirical models and is on-par with the recently developed 50 to 100 times more expensive Gaussian approximation potential (GAP) in capturing the experimental structure and properties of silica polymorphs and amorphous silica.
    Type of Medium: Online Resource
    ISSN: 2057-3960
    URL: Article
    DOI: 10.1038/s41524-023-01074-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2843287-3
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