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 Young's Modulus of Glasses with Sparse Datasets using Machine Learning

Machine learning (ML) methods are becoming popular tools for the prediction and design of novel materials. In particular, neural network (NN) is a promising ML method, which can be used to identify hidden trends in the data. However, these methods rely on large datasets and often exhibit overfitting when used with sparse dataset. Further, assessing the uncertainty in predictions for a new dataset or an extrapolation of the present dataset is challenging. Herein, using Gaussian process regression (GPR), we predict Young's modulus for silicate glasses having sparse dataset. We show that GPR significantly outperforms NN for sparse dataset, while ensuring no overfitting. Further, thanks to the nonparametric nature, GPR provides quantitative bounds for the reliability of predictions while extrapolating. Overall, GPR presents an advanced ML methodology for accelerating the development of novel functional materials such as glasses.Comment: 17 pages, 5 figure

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

Topological Control on the Structural Relaxation of Atomic Networks under Stress

Upon loading, atomic networks can feature delayed irreversible relaxation. However, the effect of composition and structure on relaxation remains poorly understood. Herein, relying on accelerated molecular dynamics simulations and topological constraint theory, we investigate the relationship between atomic topology and stress-induced structural relaxation, by taking the example of creep deformations in calcium silicate hydrates (C─S─H), the binding phase of concrete. Under constant shear stress, C─S─H is found to feature delayed logarithmic shear deformations. We demonstrate that the propensity for relaxation is minimum for isostatic atomic networks, which are characterized by the simultaneous absence of floppy internal modes of relaxation and eigenstress. This suggests that topological nanoengineering could lead to the discovery of nonaging materials.National Science Foundation (U.S.) (Grant 1562066)Schlumberger-Doll Research CenterMassachusetts Institute of Technology. Concrete Sustainability HubMassachusetts Institute of Technology. Interdisciplinary Center on MultiScale Material Science for Energy and Environment (Grant ANR-11-LABX-0053)Massachusetts Institute of Technology. Interdisciplinary Center on MultiScale Material Science for Energy and Environment (Grant ANR-11-IDEX-0001- 02

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