An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

Mao, Yuwei; Hasan, Masud; Paul, Arindam; Gupta, Vishu; Choudhary, Kamal; Tavazza, Francesca; Liao, Wei-keng; Choudhary, Alok; Acar, Pınar; Agrawal, Ankit

doi:10.1038/s41524-023-01067-8

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…[33,189,190] It can be seen that with powerful data analysis capabilities and low research costs, AI has been widely used in property prediction, material structure search, and new material design. At the application level, AI not only has great advantages over traditional calculation methods in different fields, but also has more and more achievements in different material modeling tasks, such as electronic structure, [51,[191][192][193] ionic conductivity, [83,94,194] stability, [195][196][197][198] mechanical property, [199][200][201] optical property, [202][203][204] magnetism, [205,206] [53] Copyright 2021, The Authors, published by Springer Nature.…”

Section: Other Explorationsmentioning

confidence: 99%

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Wang,

Chen,

Tao

et al. 2023

Advanced Materials

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Combining materials science, artificial intelligence (AI), physical chemistry and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of “GPT AI” shows that the scientific research field has entered the era of intelligent civilization with “data” as the basic factor and “algorithm + computing power” as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads us to think more about materials informatics, both in terms of opportunities and challenges. In this review, we provide a comprehensive discussion of AI models, materials infrastructures, and review the current advances in the discovery and design of new materials. With the rise of new research paradigms triggered by “AI for Science”, we propose the vane of materials informatics: “MatGPT”, and carry out the technical path planning from the aspects of data, descriptors, generative models, pre‐training models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics. Finally, we discuss the challenges and constraints faced by materials informatics, in order to achieve a more digital, intelligent and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists. This article is protected by copyright. All rights reserved

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Section: Other Explorationsmentioning

confidence: 99%

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Wang,

Chen,

Tao

et al. 2023

Advanced Materials

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“…Recently, the influence of AI has also spread to the natural sciences, especially engineering, physics, materials science, or even chemistry 11 . In particular, large amount of data makes it possible to pursue new approaches, e.g., the prediction of protein folding 12 , the description of material behaviour 13 – 15 , microstructure optimisation 16 , 17 , or even the discovery of materials with novel properties 18 – 20 . Moreover, in the field of porous materials, data-driven approaches have been utilised also for rapid material characterisation, to enable accelerated materials synthesis 21 , 22 .…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for microstructural optimisation of silica aerogels

Pandit,

Abdusalamov,

Itskov

et al. 2024

Sci Rep

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Silica aerogels are being extensively studied for aerospace and transportation applications due to their diverse multifunctional properties. While their microstructural features dictate their thermal, mechanical, and acoustic properties, their accurate characterisation remains challenging due to their nanoporous morphology and the stochastic nature of gelation. In this work, a deep reinforcement learning (DRL) framework is presented to optimise silica aerogel microstructures modelled with the diffusion-limited cluster–cluster aggregation (DLCA) algorithm. For faster computations, two environments consisting of DLCA surrogate models are tested with the DRL framework for inverse microstructure design. The DRL framework is shown to effectively optimise the microstructure morphology, wherein the error of the material properties achieved is dependent upon the complexity of the environment. However, in all cases, with adequate training of the DRL agent, material microstructures with desired properties can be achieved by the framework. Thus, the methodology provides a resource-efficient means to design aerogels, offering computational advantages over experimental iterations or direct numerical solutions.

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“…Owing to significant advances in materials theory [1][2][3] and computational power, it has become possible to compute several materials properties of a compound using DFT. This has led to the creation of large DFT databases 4,5 , which when combined with various advanced data mining techniques have extensively contributed to enhanced property prediction models [6][7][8][9][10][11][12][13] and catalyzed the development of the field of materials informatics [14][15][16][17][18][19][20] .…”

Section: Introductionmentioning

confidence: 99%

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Gupta,

Choudhary,

DeCost

et al. 2024

npj Comput Mater

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Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

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An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

Cited by 6 publications

References 41 publications

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Deep reinforcement learning for microstructural optimisation of silica aerogels

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

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