82 research outputs found
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
A Peridynamics-Based Micromechanical Modeling Approach for Random Heterogeneous Structural Materials
This paper presents a peridynamics-based micromechanical analysis framework that can efficiently handle material failure for random heterogeneous structural materials. In contrast to conventional continuum-based approaches, this method can handle discontinuities such as fracture without requiring supplemental mathematical relations. The framework presented here generates representative unit cells based on microstructural information on the material and assigns distinct material behavior to the constituent phases in the random heterogenous microstructures. The framework incorporates spontaneous failure initiation/propagation based on the critical stretch criterion in peridynamics and predicts effective constitutive response of the material. The current framework is applied to a metallic particulate-reinforced cementitious composite. The simulated mechanical responses show excellent match with experimental observations signifying efficacy of the peridynamics-based micromechanical framework for heterogenous composites. Thus, the multiscale peridynamics-based framework can efficiently facilitate microstructure guided material design for a large class of inclusion-modified random heterogenous materials
Accelerated Design of Chalcogenide Glasses through Interpretable Machine Learning for Composition Property Relationships
Chalcogenide glasses possess several outstanding properties that enable
several ground breaking applications, such as optical discs, infrared cameras,
and thermal imaging systems. Despite the ubiquitous usage of these glasses, the
composition property relationships in these materials remain poorly understood.
Here, we use a large experimental dataset comprising approx 24000 glass
compositions made of 51 distinct elements from the periodic table to develop
machine learning models for predicting 12 properties, namely, annealing point,
bulk modulus, density, Vickers hardness, Littleton point, Youngs modulus, shear
modulus, softening point, thermal expansion coefficient, glass transition
temperature, liquidus temperature, and refractive index. These models, by far,
are the largest for chalcogenide glasses. Further, we use SHAP, a game theory
based algorithm, to interpret the output of machine learning algorithms by
analyzing the contributions of each element towards the models prediction of a
property. This provides a powerful tool for experimentalists to interpret the
models prediction and hence design new glass compositions with targeted
properties. Finally, using the models, we develop several glass selection
charts that can potentially aid in the rational design of novel chalcogenide
glasses for various applications.Comment: 17 pages, 8 figure
Dynamics of confined water and its interplay with alkali cations in sodium aluminosilicate hydrate gel: insights from reactive force field molecular dynamics
This paper presents the dynamics of confined water and its interplay with alkali cations in disordered sodium aluminosilicate hydrate (N-A-S-H) gel using reactive force field molecular dynamics. N-A-S-H gel is the primary binding phase in geopolymers formed via alkaline activation of fly ash. Despite attractive mechanical properties, geopolymers suffer from durability issues, particularly the alkali leaching problem which has motivated this study. Here, the dynamics of confined water and the mobility of alkali cations in N-A-S-H is evaluated by obtaining the evolution of mean squared displacements and Van Hove correlation function. To evaluate the influence of the composition of N-A-S-H on the water dynamics and diffusion of alkali cations, atomistic structures of N-A-S-H with Si/Al ratio ranging from 1 to 3 are constructed. It is observed that the diffusion of confined water and sodium is significantly influenced by the Si/Al ratio. The confined water molecules in N-A-S-H exhibit a multistage dynamic behavior where they can be classified as mobile and immobile water molecules. While the mobility of water molecules gets progressively restricted with an increase in Si/Al ratio, the diffusion coefficient of sodium also decreases as the Si/Al ratio increases. The diffusion coefficient of water molecules in the N-A-S-H structure exhibits a lower value than those of the calcium-silicate-hydrate (C-S-H) structure. This is mainly due to the random disordered structure of N-A-S-H as compared to the layered C-S-H structure. To further evaluate the influence of water content in N-A-S-H, atomistic structures of N-A-S-H with water contents ranging from 5–20% are constructed. Qn distribution of the structures indicates significant depolymerization of N-A-S-H structure with increasing water content. Increased conversion of Si–O–Na network to Si–O–H and Na–OH components with an increase in water content helps explain the alkali-leaching issue in fly ash-based geopolymers observed macroscopically. Overall, the results in this study can be used as a starting point towards multiscale simulation-based design and development of durable geopolymers
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
The time evolution of physical systems is described by differential
equations, which depend on abstract quantities like energy and force.
Traditionally, these quantities are derived as functionals based on observables
such as positions and velocities. Discovering these governing symbolic laws is
the key to comprehending the interactions in nature. Here, we present a
Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the
dynamics of systems directly from their trajectory. We demonstrate the
performance of HGNN on n-springs, n-pendulums, gravitational systems, and
binary Lennard Jones systems; HGNN learns the dynamics in excellent agreement
with the ground truth from small amounts of data. We also evaluate the ability
of HGNN to generalize to larger system sizes, and to hybrid spring-pendulum
system that is a combination of two original systems (spring and pendulum) on
which the models are trained independently. Finally, employing symbolic
regression on the learned HGNN, we infer the underlying equations relating the
energy functionals, even for complex systems such as the binary Lennard-Jones
liquid. Our framework facilitates the interpretable discovery of interaction
laws directly from physical system trajectories. Furthermore, this approach can
be extended to other systems with topology-dependent dynamics, such as cells,
polydisperse gels, or deformable bodies
The profiles of first and second SARS-CoV-2 waves in the top ten COVID-19 affected countries
In March 2020, the World Health Organization (WHO) acknowledged the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a "public health emergency of international concern." Within a month, coronavirus disease 2019 (COVID-19) was declared a pandemic. As of 21 July 2021, 192.8 million cases and 4.13 million deaths have been attributed to COVID-19 worldwide. Here we discuss the data from top ten COVID-19 affected countries, with an emphasis on the average strolling period of 6 to 8 months between first and second wave in these nations. Our study ascertains that analysis of the data from countries temporally ahead of others during the pandemic gives policymakers the chance to strategize and postpone or mitigate subsequent COVID-19 waves. With governments throughout the globe continuing their immunisation efforts, a study of the key indicators of COVID-19 waves from the top ten countries is critical to preparing the healthcare system to save millions of lives
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