The chemical space for designing materials is practically infinite. This makes disruptive
progress by traditional physics-based modeling alone challenging. Yet, training data for …

Abstract Design and optimization of CO 2 capture processes have become a tremendously
active area of research particularly in the past decade. In this context, development of …

Physics‐informed neural networks (PINNs) are a new tool for solving boundary value
problems by defining loss functions of neural networks based on governing equations …

This paper proposes a novel algorithm named surrogate ensemble assisted differential
evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale …

Biological systems generate a wealth of materials, and their design principles inspire and
inform scientists from a broad range of fields. Nature often adapts hierarchical multilevel …

This study presents a data-driven finite element-machine learning surrogate model for
predicting the end-to-end full-field stress distribution and stress concentration around an …

Silica aerogels are being extensively studied for aerospace and transportation applications
due to their diverse multifunctional properties. While their microstructural features dictate …

Determining, understanding, and predicting the so-called structure–property relation is an
important task in many scientific disciplines, such as chemistry, biology, meteorology …

This study investigates the application of machine learning models to predict time-evolving
stress fields in complex three-dimensional structures trained with full-scale finite element …

This article introduces an innovative approach that utilizes machine learning (ML) to address
the computational challenges of accurate atomistic simulations in materials science …