Technical University of Darmstadt, Darmstadt, 64287, Germany
Date & Time: 10:30 - 11:30, Dec. 18th (Mon), 2023.
Place: 2nd Conference Room, 1F, Main Bldg., Sengen site.
Inverse Design of Functional Materials
Technical University of Darmstadt, Darmstadt, 64287, Germany
Abstract:
Machine learning has been widely applied to obtain statistical understanding and rational design of advanced materials by mapping out the processing - (micro)structure - property – performance relationships. In this work, I am going to demonstrate the concept of inverse design and to showcase how it can be carried out in three different flavours, i.e., high-throughput combinatorial computation, Bayesian optimization, and generative deep learning. Taking magnetic materials as an example, I have implemented an automatized high-throughput workflow, which has been applied to screen for promising candidate materials as permanent magnets and magnetocaloric, as well as spintronic materials [1]. After identifying the essential benchmarking properties, our workflow can be straightforwardly generalized to screening for other functional materials, as demonstrated for thermal management [2] and photovoltaic [3] materials. Furthermore, in order to explore the vast chemical space more efficiently, forward modelling of the Curie temperatures for ferromagnetic materials has been carried out [4]. This provides the basis for multi-objective optimization, which will be illustrated by figuring out the two-dimensional Pareto front of magnetization and critical temperature. Interestingly, such a generic approach based on Bayesian statistics can be directly integrated with experiments, leading to adaptive design of high-entropy alloys [5]. Last but not least, I am going to give an overview on how generative deep learning can be applied to predict novel crystal structures and microstructures based on our recent implementation using the generative adversarial network [6].
Keywords：inverse design; high-throughput; Bayesian optimization; generative deep learning
References:
[1] H. Zhang, Electronic structure, 3, (2021) 033001
[2] S. Lin, C. Shen, and H. Zhang, Materials Today Physics, 32, (2023) 100998
[3] C. Shen, et al., JACS, 145, (2023) 21925
[4] T. Long, et al., Mat. Res. Lett., 9, (2021) 169
[5] Z. Rao, et al., Science, 378, (2022) 78
[6] T. Long, et al., Acta Mat., 231, (2022) 117898
Keywords：inverse design; high-throughput; Bayesian optimization; generative deep learning
References:
[1] H. Zhang, Electronic structure, 3, (2021) 033001
[2] S. Lin, C. Shen, and H. Zhang, Materials Today Physics, 32, (2023) 100998
[3] C. Shen, et al., JACS, 145, (2023) 21925
[4] T. Long, et al., Mat. Res. Lett., 9, (2021) 169
[5] Z. Rao, et al., Science, 378, (2022) 78
[6] T. Long, et al., Acta Mat., 231, (2022) 117898
(Contact)
H. Sepehri-Amin, Green Magnetic Materials Gr..
E-mail: H.SEPEHRIAMIN[at]nims.go.jp
E-mail: H.SEPEHRIAMIN[at]nims.go.jp