Search Results for author: Kedar Hippalgaonkar

Found 7 papers, 4 papers with code

Constructing Custom Thermodynamics Using Deep Learning

1 code implementation8 Aug 2023 Xiaoli Chen, Beatrice W. Soh, Zi-En Ooi, Eleonore Vissol-Gaudin, Haijun Yu, Kostya S. Novoselov, Kedar Hippalgaonkar, Qianxiao Li

Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate.

Physical Intuition

Explainable machine learning to enable high-throughput electrical conductivity optimization of doped conjugated polymers

no code implementations8 Aug 2023 Ji Wei Yoon, Adithya Kumar, Pawan Kumar, Kedar Hippalgaonkar, J Senthilnath, Vijila Chellappan

For the subset of highly conductive samples, we employed a second ML model (regression model), to predict their conductivities, yielding an impressive test R2 value of 0. 984.

Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

1 code implementation23 May 2021 Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III, Tonio Buonassisi

In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems.

Active Learning Benchmarking +2

Efficacious symmetry-adapted atomic displacement method for lattice dynamical studies

1 code implementation14 Jul 2020 Chee Kwan Gan, Yun Liu, Tze Chien Sum, Kedar Hippalgaonkar

Small displacement methods have been successfully used to calculate the lattice dynamical properties of crystals.

Materials Science

Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials

no code implementations15 Nov 2018 Leo Laugier, Daniil Bash, Jose Recatala, Hong Kuan Ng, Savitha Ramasamy, Chuan-Sheng Foo, Vijay R. Chandrasekhar, Kedar Hippalgaonkar

We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN), Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectric properties.


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