In silico design of new functional materials
Materials design is a grand challenge of materials science. And the main approach for solving this problem is still intuition-based. Such a way requires a lot of time and financial resources and months to years of conducting the experiment and doing characterization. Therefore, any kind of model that can be used at the very first stage of materials design and can narrow the selection area is a helpful tool for synthetic chemist. Also, an automated search for materials with human-defined target properties in the entire chemical space, i.e. inverse materials design is a highly desired tool in the exploration of materials design space. Along with that, de novo design is not a kind of a completely new task in a field of development of new organic molecules with target properties. A lot of different generative approaches are being used along with screening the libraries of existing molecules, searching for drugs for a particular target, or generating new ones based on a very simple initial structure. Here we would like to present a new approach for generating new materials with desired properties. We used autoencoder neural network architecture to encode materials composition and crystal structure as a vector in a latent space. In such case, any Quantitative Structure-Property Relationship (QSPR) model based on the vector can be interpreted as function in the latent space and can be used to predict property of existing materials as well as for prophetic ones. Such an approach has comparable accuracy with such classic computational methods as DFT in the case of predicting values of energies or charges, but significantly transcends them in terms of computational time. The proposed method was tested for generating super-firm materials, but can easily be extended to any target properties, granted a database of materials properties can be provided for training.
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In silico design of new functional materials
- 1. In silico design of new functional materials Vadim Korolev, Artem Mitrofanov, Artem Eliseev, Boris Sattarov, Valery Tkachenko Science Data Software, LLC Lomonosov Moscow State University 1
- 2. 2
- 3. The aim • To expand ‘illuminated’ area: • Software able to propose new materials instead of calculating properties of the existing ones 3
- 4. “Encoder” Open materials databases: • Materials Project • AFLOWLIB • OQMD • COD • … “Decoder” “Encoder” “Generator” conjugated latent spaces (chemical composition + crystal structure) convolutional variational autoencoder “Estimator” • Formation energy • Target property • … Labeled materials datasets: • (?) superconductors • (?) magnetic materials • … “Filter” 4
- 6. “Encoder” Open materials databases: • Materials Project • AFLOWLIB • OQMD • COD • … “Decoder” “Encoder” “Generator” conjugated latent spaces (chemical composition + crystal structure) convolutional variational autoencoder “Estimator” • Formation energy • Target property • … Labeled materials datasets: • (?) superconductors • (?) magnetic materials • … “Filter” 6
- 7. Vectorization el. Ca H … O … Ca Sc Ti … Ti 0 … 3 … 1 0 0 … O3 7
- 9. “Encoder” Open materials databases: • Materials Project • AFLOWLIB • OQMD • COD • … “Decoder” “Encoder” “Generator” conjugated latent spaces (chemical composition + crystal structure) convolutional variational autoencoder “Estimator” • Formation energy • Target property • … Labeled materials datasets: • (?) superconductors • (?) magnetic materials • … “Filter” 9
- 10. Generator • Random • Grid search • Bayesian • … 10
- 11. Shahriari, Bobak, et al. "Taking the Human Out of the Loop: A Review of Bayesian Optimization.” 2015. Tree Parzen Estimator 11
- 12. “Encoder” Open materials databases: • Materials Project • AFLOWLIB • OQMD • COD • … “Decoder” “Encoder” “Generator” conjugated latent spaces (chemical composition + crystal structure) convolutional variational autoencoder “Estimator” • Formation energy • Target property • … Labeled materials datasets: • (?) superconductors • (?) magnetic materials • … “Filter” 12
- 13. Estimator or why do we need one more database? • Experimental check • Composition • Methods • DFT calculator • xyz • Method • Basis set • Machine learning model: • Vectorization • Method • Training dataset 13
- 14. “Encoder” Open materials databases: • Materials Project • AFLOWLIB • OQMD • COD • … “Decoder” “Encoder” “Generator” conjugated latent spaces (chemical composition + crystal structure) convolutional variational autoencoder “Estimator” • Formation energy • Target property • … Labeled materials datasets: • (?) superconductors • (?) magnetic materials • … “Filter” 14
- 17. “Encoder” Open materials databases: • Materials Project • AFLOWLIB • OQMD • COD • … “Decoder” “Encoder” “Generator” conjugated latent spaces (chemical composition + crystal structure) convolutional variational autoencoder “Estimator” • Formation energy • Target property • … Labeled materials datasets: • (?) superconductors • (?) magnetic materials • … “Filter” 17
- 18. Decoder • VAE decoder • XRD to xyz Raw spectra Decoded spectra hexagonal total 414 MAE, A a,b: 2.1; c: 8.0 a,b: 1.7 (22%) c: 9.7 (77%) Good 175 134 tetragonal Total 530 MAE, A a,b: 30; c: 30 a,b: 2.4 (30%) c: 7.6 (68%) Good 196 121 cubic Total 238 MAE, A a,b,c: 5 a,b,c: 1.5 (16%) Good 184 163 all Total 1233 MAE, A a,b: 2.1c: 31 2.4 (24%) Good 18
- 19. Testing 19
- 20. Testing • Models: XGBoost Regressors with tuned hyperparameters (using Tree-Parzen Estimators) • Input data: Bulk/shear moduli (Voigt-Reuss-Hill averages) for 2721 compounds from AFLOW repository RMSE = 36.3 GPa R2 = 0.781 20
- 21. Testing 21
- 22. Thank you! On Web: scidatasoft.com Slides: https://www.slideshare.net/valerytkachenko16 Contact us: info@scidatasoft.com 22