Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboar

Improving deep learning model performance under parametric constraints for materials informatics applications

Abstract Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties

An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

Abstract Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework for elastic properties of materials. In this work, the microstructure is represented by the Orientation Distribution Function (ODF) that determines the volume densities of crystallographic orientations. The framework was evaluated on two crystal systems, cubic and hexagonal, for Titanium (Ti) in Joint Automated Repository for Various Integrated Simulations (JARVIS) database and is expected to be widely applicable for materials with multiple crystal systems. The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time

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Not AvailableIntensive tillage based management practices are threatening soil quality and systems sustainability in the rice wheat belt of Northwest India. Furthermore, it is accentuated with puddling of soil, which disrupts soil aggregates. Conservation agriculture (CA) practices involving zero tillage, crop residue management and suitable crop rotation can serve as better alternative to conventional agriculture for maintaining soil quality. Soil organic carbon is an important determinant of soil quality, playing critical role in food production, mitigation and adaptation to climate change as well as performs many ecosystem functions. To understand the turnover of soil carbon in different forms (Total organic carbon-TOC; aggregate associated carbon-AAC; particulate organic carbon- POC), soil aggregation and crop productivity with different management practices, one conventional agriculture based scenario and three CA based crop management scenarios namely conventional rice-wheat system (Sc1), partial CA based rice-wheat-mungbean system (Sc2), full CA-based rice-wheat-mungbean system (Sc3) and maize-wheat-mungbean system (Sc4) were evaluated. TOC was increased by 71%, 68% and 25% after 4 years of the experiment and 75%, 80% and 38% after 6 years of the experiment in Sc4, Sc3 and Sc2, respectively, over Sc1 at 0–15 cm soil depth. After 4 years of the experiment, 38.5% and 5.0% and after 6 years 50.8% and 24.4% improvement in total water stable aggregates at 0–15 and 15–30 cm soil depth, respectively was observed in CA-based scenarios over Sc1. Higher aggregate indices were associated with Sc3 at 0–15 cm soil depth than others. Among the size classes of aggregates, highest aggregate associated C (8.94 g kg−1) was retained in the 1-0.5mm size class under CA-based scenarios. After 6 years, higher POC was associated with Sc4 (116%). CA-based rice/maize system (Sc3 and Sc4) showed higher productivity than Sc1. Therefore, CA could be a potential management practice in rice-wheat cropping system of Northwest India to improve the soil carbon pools through maintaining soil aggregation and productivity.Not Availabl

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Not AvailableIntensive tillage coupled with crop residue burning in rice-wheat (RW) system is a serious issue that causes soil degradation and environmental pollution. Soil organic carbon (SOC) is one of the main indicators of soil health and system's sustainability. Zero-tillage has been widely recommended as an alternative for improving carbon sequestration in soil under different ecologies. But the SOC sequestration is very inconsistent and varied as it depends on the crop management practices. This study was performed in the western Indo-Gangetic plains (IGP) of India where RW system contributes 40% to the total country's food grain basket; however there exists issue of its sustainability because of declining SOC coupled with open field crop residue burning. Therefore, we evaluated the effects of different management scenarios (Sc) namely Sc1 (conventional till rice-wheat cropping system; business as usual), Sc2 (partial climate smart agriculture (CSA)-based rice-wheat-mungbean system), Sc3 (CSA-based rice-wheat-mungbean system), and Sc4 (CSA-based maize-wheat-mungbean system) on SOC pools and biological properties after 4 crop cycles (year 2009–2013). Soil samples were collected from surface and sub surface layers (0–15 and 15–30 cm soil depth) after rice harvesting in 2013. Results showed that the SOC stock at surface layer was higher by 70% with Sc4 than Sc1 (16.2 Mg C ha−1) (P Sc3 > Sc2 > Sc1 (P < 0.05). Higher lability index (LI) (2.1) and stratification ratio (SR) (2.5) of organic carbon were observed in CSA-based systems (Sc2 and Sc4). At surface layer (0–15 cm) the CSA- based scenarios (mean of Sc2, Sc3 and Sc4) showed higher (P < 0.05) enzyme activities viz. dehydrogenase (641 μgTPF g−1 24 h−1) and alkaline phosphatase (158 μg pnitrophenol g−1), and microbial biomass carbon (MBC) (787 μg g−1) and microbial biomass nitrogen (MBN) (98 μg g−1) compared with Sc1. Higher value of the basal soil respiration (34%) was also observed with CSA based scenarios (Sc2, Sc3, Sc4). Surface soil layer showed maximum counts of fungi, bacteria and actinomycetes in Sc4. MBC, fungal population and SOC were the most sensitive biological soil parameters identified through principal component analysis (PCA) which can be used for soil quality assessment. Therefore, medium term adoption of climate smart agricultural practices involving zero-tillage, crop establishment, residue management and crop diversification in rice-wheat system can significantly improve the systems productivity by improving SOC and soil biological quality.Not Availabl

Effects of tillage, crop establishment and diversification on soil organic carbon, aggregation, aggregate associated carbon and productivity in cereal systems of semi-arid Northwest India

Intensive tillage based management practices are threatening soil quality and systems sustainability in the ricewheat belt of Northwest India. Furthermore, it is accentuated with puddling of soil, which disrupts soil aggregates. Conservation agriculture (CA) practices involving zero tillage, crop residue management and suitable crop rotation can serve as better alternative to conventional agriculture for maintaining soil quality. Soil organic carbon is an important determinant of soil quality, playing critical role in food production, mitigation and adaptation to climate change as well as performs many ecosystem functions. To understand the turnover of soil carbon in different forms (Total organic carbon-TOC; aggregate associated carbon-AAC; articulate organic carbon- POC), soil aggregation and crop productivity with different management practices, one conventional agriculture based scenario and three CA based crop management scenarios namely conventional rice-wheat system (Sc1), partial CA based rice-wheat-mungbean system (Sc2), full CA-based rice-wheat-mungbean system (Sc3) and maize-wheat-mungbean system (Sc4) were evaluated. TOC was increased by 71%, 68% and 25% after 4 years of the experiment and 75%, 80% and 38% after 6 years of the experiment in Sc4, Sc3 and Sc2, respectively, over Sc1 at 0–15 cm soil depth. After 4 years of the experiment, 38.5% and 5.0% and after 6 years 50.8% and 24.4% improvement in total water stable aggregates at 0–15 and 15–30 cm soil depth, respectively was observed in CA-based scenarios over Sc1. Higher aggregate indices were associated with Sc3 at 0–15 cm soil depth than others. Among the size classes of aggregates, highest aggregate associated C (8.94 g kg−1) was retained in the 1-0.5mm size class under CA-based scenarios. After 6 years, higher POC was associated with Sc4 (116%). CA-based rice/maize system (Sc3 and Sc4) showed higher productivity than Sc1. Therefore, CA could be a potential management practice in rice-wheat cropping system of Northwest India to improve the soil carbon pools through maintaining soil aggregation and productivity

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction

Carbon mineralization in soil as influenced by crop residue type and placement in an Alfisols of Northwest India

Carbon (C) mineralization of crop residues is an important process occurring in soil which is helpful in predicting CO2 emission to the atmosphere and nutrient availability to plants. A laboratory experiment was conducted in which C mineralization of residues of rice (Oryza sativa), wheat (Triticum aestivum), maize (Zea mays), mungbean (Vigna radiata) and their mixtures was applied to the soil surface or incorporated into an Alfisols from Northwest India. C mineralization was significantly affected by residue placement and type and their interactions. Rice residue had a higher decomposition rate (k = 0.121 and 0.076 day−1) than wheat (0.073 and 0.042 day−1) and maize residues (0.041 day−1) irrespective of placements. Higher decomposition rates of rice and wheat were observed when placed on soil surface than incorporated in the soils. Additive effects of the contribution of each residue type to C mineralization of the residue mixture were observed. When mungbean residue was added to the rice/wheat or maize/wheat mixture, decomposition of the residue mixture was enhanced. Crop residues with low N and high C/N ratio such as maize, wheat, rice and their mixtures can be applied on the soil surface for faster C and N mineralization, thereby helping to manage high volumes of residues under conservation agriculture-based practices in northwest India