ClimateNLP: Analyzing Public Sentiment Towards Climate Change Using Natural Language Processing

Climate change's impact on human health poses unprecedented and diverse challenges. Unless proactive measures based on solid evidence are implemented, these threats will likely escalate and continue to endanger human well-being. The escalating advancements in information and communication technologies have facilitated the widespread availability and utilization of social media platforms. Individuals utilize platforms such as Twitter and Facebook to express their opinions, thoughts, and critiques on diverse subjects, encompassing the pressing issue of climate change. The proliferation of climate change-related content on social media necessitates comprehensive analysis to glean meaningful insights. This paper employs natural language processing (NLP) techniques to analyze climate change discourse and quantify the sentiment of climate change-related tweets. We use ClimateBERT, a pretrained model fine-tuned specifically for the climate change domain. The objective is to discern the sentiment individuals express and uncover patterns in public opinion concerning climate change. Analyzing tweet sentiments allows a deeper comprehension of public perceptions, concerns, and emotions about this critical global challenge. The findings from this experiment unearth valuable insights into public sentiment and the entities associated with climate change discourse. Policymakers, researchers, and organizations can leverage such analyses to understand public perceptions, identify influential actors, and devise informed strategies to address climate change challenges

Fracture Response of Metallic Particulate-reinforced Cementitious Composites: Insights from Experiments and Multiscale Numerical Simulations

This paper presents an experimental and numerical investigation into the fracture response of mortars containing up to 30% waste iron powder by volume as OPC-replacement. The iron powder-modified mortars demonstrate significantly improved strength and fracture properties as compared to the control mortars due to presence of elongated iron particulates in the powder. With a view to develop a predictive tool towards materials design of such particulate-reinforced systems, fracture responses of iron powder-modified mortars are simulated using a multiscale numerical approach. The approach implements multi-scale numerical homogenization involving cohesive zone-based damage at the matrix-inclusion interface and isotropic damage in the matrix to obtain composite constitutive response and fracture energy. Consequently, these results serve as input to macro-scale XFEM-based three-point-bend simulations of notched mortar beams. The simulated macroscopic fracture behavior exhibit excellent match with the experimental results. Thus, the numerical approach links the material microstructure to macroscopic fracture parameters facilitating microstructure-guided material design

Microstructure-guided numerical simulation to evaluate the influence of phase change materials (PCMs) on the freeze-thaw response of concrete pavements

The use of phase change materials in infrastructure has gained significant attention in the recent years owing to their robust thermal performance. This study implements a numerical simulation framework using finite element analysis to evaluate the influence of phase change materials (PCMs) on the thermal response of concrete pavements in geographical regions with significant winter weather conditions. The analysis is carried out at different length scales. The latent-heat associated with different PCMs is efficiently incorporated into the simulation framework. Besides, the numerical simulation framework employs continuum damage mechanics to evaluate the influence of PCMs on the freeze-thaw induced damage in concretes. The simulations show significant reductions in the freeze-thaw induced damage when PCMs are incorporated in concrete. The numerical simulation framework, developed here, provides efficient means of optimizing the material design of such durable PCM-incorporated concretes for pavements by tailoring the composition and material microstructure to maximize performance

MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models

Information extraction and textual comprehension from materials literature are vital for developing an exhaustive knowledge base that enables accelerated materials discovery. Language models have demonstrated their capability to answer domain-specific questions and retrieve information from knowledge bases. However, there are no benchmark datasets in the materials domain that can evaluate the understanding of the key concepts by these language models. In this work, we curate a dataset of 650 challenging questions from the materials domain that require the knowledge and skills of a materials student who has cleared their undergraduate degree. We classify these questions based on their structure and the materials science domain-based subcategories. Further, we evaluate the performance of GPT-3.5 and GPT-4 models on solving these questions via zero-shot and chain of thought prompting. It is observed that GPT-4 gives the best performance (~62% accuracy) as compared to GPT-3.5. Interestingly, in contrast to the general observation, no significant improvement in accuracy is observed with the chain of thought prompting. To evaluate the limitations, we performed an error analysis, which revealed conceptual errors (~64%) as the major contributor compared to computational errors (~36%) towards the reduced performance of LLMs. We hope that the dataset and analysis performed in this work will promote further research in developing better materials science domain-specific LLMs and strategies for information extraction

Predicting the dissolution kinetics of silicate glasses using machine learning

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties

Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors

Due to their disordered structure, glasses present a unique challenge in predicting the composition-property relationships. Recently, several attempts have been made to predict the glass properties using machine learning techniques. However, these techniques have the limitations, namely, (i) predictions are limited to the components that are present in the original dataset, and (ii) predictions towards the extreme values of the properties, important regions for new materials discovery, are not very reliable due to the sparse datapoints in this region. To address these challenges, here we present a low complexity neural network (LCNN) that provides improved performance in predicting the properties of oxide glasses. In addition, we combine the LCNN with physical and chemical descriptors that allow the development of universal models that can provide predictions for components beyond the training set. By training on a large dataset (~50000) of glass components, we show the LCNN outperforms state-of-the-art algorithms such as XGBoost. In addition, we interpret the LCNN models using Shapely additive explanations to gain insights into the role played by the descriptors in governing the property. Finally, we demonstrate the universality of the LCNN models by predicting the properties for glasses with new components that were not present in the original training set. Altogether, the present approach provides a promising direction towards accelerated discovery of novel glass compositions.Comment: 15 pages, 3 figure