1 code implementation • 20 Feb 2024 • Arpan Biswas, Sai Mani Prudhvi Valleti, Rama Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often non-differentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces.
no code implementations • 8 Oct 2023 • Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim Ziatdinov, Rama Vasudevan
Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition.
1 code implementation • 5 Apr 2023 • Arpan Biswas, Yongtao Liu, Nicole Creange, Yu-Chen Liu, Stephen Jesse, Jan-Chi Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Rama K. Vasudevan
Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites.
1 code implementation • 8 Feb 2023 • Arpan Biswas, Maxim Ziatdinov, Sergei V. Kalinin
Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as EELS or 4D STEM, that contain information on a wide range of structural, physical, and chemical properties of materials.
2 code implementations • 30 Jun 2022 • Arpan Biswas, Rama Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin
Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences due to their capability in disentangling representations and ability to find latent manifolds for classification and regression of complex experimental data.
no code implementations • 16 Oct 2021 • Arpan Biswas, Claudio Fuentes, Christopher Hoyle
We propose a nested weighted Tchebycheff Multi-objective Bayesian optimization framework where we build a regression model selection procedure from an ensemble of models, towards better estimation of the uncertain parameters of the weighted-Tchebycheff expensive black-box multi-objective function.