Generative ai and process systems engineering: The next frontier
This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …
such as large language models (LLMs), can enhance solution methodologies within process …
Generative deep learning for the inverse design of materials
In addition to the forward inference of materials properties using machine learning,
generative deep learning techniques applied on materials science allow the inverse design …
generative deep learning techniques applied on materials science allow the inverse design …
Transfer learning enables the rapid design of single crystal superalloys with superior creep resistances at ultrahigh temperature
F Yang, W Zhao, Y Ru, S Lin, J Huang, B Du… - npj Computational …, 2024 - nature.com
Accelerating the design of Ni-based single crystal (SX) superalloys with superior creep
resistance at ultrahigh temperatures is a desirable goal but extremely challenging task. In …
resistance at ultrahigh temperatures is a desirable goal but extremely challenging task. In …
Exploring chemistry and additive manufacturing design spaces: a perspective on computationally-guided design of printable alloys
Additive manufacturing (AM), especially Laser Powder-Bed Fusion (L-PBF), provides alloys
with unique properties, but faces printability challenges like porosity and cracks. To address …
with unique properties, but faces printability challenges like porosity and cracks. To address …
A deep learning-based crystal plasticity finite element model
This study presents an innovative deep learning-based surrogate model for the Crystal
Plasticity Finite Element (CPFE) method, fundamentally transforming the generation of …
Plasticity Finite Element (CPFE) method, fundamentally transforming the generation of …
[HTML][HTML] Inverse Design of Microstructures Using Conditional Continuous Normalizing Flows
Inverse design is a classical mathematical challenge found in various fields, including
materials science, where it is essential for property-driven microstructure design. This …
materials science, where it is essential for property-driven microstructure design. This …
A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial …
F Gomes Souza Jr, S Bhansali, K Pal… - Materials, 2024 - mdpi.com
From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment
analysis of nanocomposite literature, distinguishing itself from existing reviews through its …
analysis of nanocomposite literature, distinguishing itself from existing reviews through its …
[HTML][HTML] PSP-GEN: Stochastic inversion of the Process–Structure–Property chain in materials design through deep, generative probabilistic modeling
Y Zang, PS Koutsourelakis - Acta Materialia, 2025 - Elsevier
Inverse material design is a cornerstone challenge in materials science, with significant
applications across many industries. Traditional approaches that invert the structure …
applications across many industries. Traditional approaches that invert the structure …
Uncertainty quantification of microstructures: a perspective on forward and inverse problems for mechanical properties of aerospace materials
In this review, state‐of‐the‐art studies on the uncertainty quantification (UQ) of
microstructures in aerospace materials is examined, addressing both forward and inverse …
microstructures in aerospace materials is examined, addressing both forward and inverse …
Phase-field model of silicon carbide growth during isothermal condition
EJ Munoz, V Attari, MC Martinez, MB Dickerson… - Computational Materials …, 2024 - Elsevier
Silicon carbide (SiC) emerges as a promising ceramic material for high-temperature
structural applications, especially within the aerospace sector. The utilization of SiC-based …
structural applications, especially within the aerospace sector. The utilization of SiC-based …