The Experts below are selected from a list of 195 Experts worldwide ranked by ideXlab platform

Rampi Ramprasad - One of the best experts on this subject based on the ideXlab platform.

  • accelerated Materials Property predictions and design using motif based fingerprints
    Physical Review B, 2015
    Co-Authors: Tran Doan Huan, Arun Mannodikanakkithodi, Rampi Ramprasad
    Abstract:

    Data-driven approaches are particularly useful for computational Materials discovery and design as they can be used for rapidly screening over a very large number of Materials, thus suggesting lead candidates for further in-depth investigations. A central challenge of such approaches is to develop a numerical representation, often referred to as a fingerprint, of the Materials. Inspired by recent developments in cheminformatics, we propose a class of hierarchical motif-based topological fingerprints for Materials composed of elements such as C, O, H, N, F, etc., whose coordination preferences are well understood. We show that these fingerprints, when representing either molecules or crystals, may be effectively mapped onto a variety of properties using a similarity-based learning model and hence can be used to predict the relevant properties of a material, given that its fingerprint can be defined. Two simple machine-learning-based procedures are introduced to demonstrate that the learning model can be inverted to identify the desired fingerprints and then to reconstruct molecules which possess a set of targeted properties.

  • Accelerating Materials Property predictions using machine learning
    Scientific Reports, 2013
    Co-Authors: Ghanshyam Pilania, Sanguthevar Rajasekaran, Xun Jiang, Chenchen Wang, Rampi Ramprasad
    Abstract:

    The Materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, Property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific Materials.

Zhigang Shuai - One of the best experts on this subject based on the ideXlab platform.

Zheng Xiong - One of the best experts on this subject based on the ideXlab platform.

  • evaluating explorative prediction power of machine learning algorithms for Materials discovery using k fold forward cross validation
    Computational Materials Science, 2020
    Co-Authors: Zheng Xiong, Yuxin Cui, Zhonghao Liu, Yong Zhao
    Abstract:

    Abstract The Materials discovery problem usually aims to identify novel “outlier” Materials with extremely low or high Property values outside of the scope of all known Materials. It can be mapped as an explorative prediction problem. However, currently the performance of machine learning algorithms for Materials Property prediction is usually evaluated via k-fold cross-validation (CV) or holdout-test, which tend to over-estimate their explorative prediction performance in discovering novel Materials. We propose k -fold- m -step forward cross-validation ( km FCV) as a new way for evaluating exploration performance in Materials Property prediction and conducted a comprehensive benchmark evaluation on the exploration performance of a variety of prediction models on Materials Property (including formation energy, band gap, and superconducting critical temperature) prediction with different Materials representation and machine learning algorithms. Our results show that even though current machine learning models can achieve good results when evaluated with traditional CV, their explorative power is actually very low as shown by our proposed km FCV evaluation method and the proposed exploration accuracy. More advanced explorative machine learning algorithms are strongly needed for new Materials discovery.

  • convolutional neural networks for crystal material Property prediction using hybrid orbital field matrix and magpie descriptors
    Crystals, 2019
    Co-Authors: Zhuo Cao, Yabo Dan, Zheng Xiong, Chengcheng Niu, Songrong Qian
    Abstract:

    Computational prediction of crystal Materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments showed that our method achieves better performance than conventional regression algorithms such as support vector machines and Random Forest. It is also better than CNN models using only the OFM features, the Magpie features, or the basic one-hot encodings. This demonstrates the advantages of CNN and feature fusion for Materials Property prediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the features extracted by the CNN to obtain greater understanding of the CNN-OFM model.

Ghanshyam Pilania - One of the best experts on this subject based on the ideXlab platform.

  • Accelerating Materials Property predictions using machine learning
    Scientific Reports, 2013
    Co-Authors: Ghanshyam Pilania, Sanguthevar Rajasekaran, Xun Jiang, Chenchen Wang, Rampi Ramprasad
    Abstract:

    The Materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, Property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific Materials.

Yingli Niu - One of the best experts on this subject based on the ideXlab platform.