Enhancing Corrosion Resistance Predictions of Alloys Using Process-Aware Neural Networks


The advancement of machine learning techniques has ushered in a new era of accelerated materials design and discovery. The rapid progress witnessed in this domain can be attributed to the concerted efforts of the materials research community, which has systematically organized protocols for collecting and cataloging materials data, making it amenable to machine learning analysis. In  new study led by Professor Dierk Raabe   from the Max-Planck-Gesellschaft in Germany, which was published in Science Advances. The study focuses on employing a unique combination of automated natural language processing and deep neural networks to predict the pitting resistance of corrosion-resistant alloys. This groundbreaking research holds immense promise in revolutionizing our ability to design alloys with superior corrosion resistance, thus addressing a critical challenge faced by industries worldwide.

Corrosion of metallic materials results in staggering economic losses amounting to approximately 2.5 trillion USD annually. In response to this challenge, machine learning techniques have been harnessed to predict corrosion rates and identify corrosion-resistant alloys. Previous efforts have explored the prediction of various forms of corrosion, including high-temperature oxidation kinetics, atmospheric corrosion rates, and even image-based analysis for identifying corrosion patterns. However, the complexity of corrosion-resistant alloys presents unique challenges in terms of the vast array of factors influencing their behavior, such as alloy composition, processing history, and environmental conditions. This study aims to enhance the accuracy of corrosion resistance predictions by integrating textual information about alloy processing history and test conditions into machine learning models.

The heart of this research lies in the integration of textual information with traditional numerical data in the context of deep neural networks. Conventionally, deep learning models have been limited to numerical input data. However, corrosion resistance in alloys is profoundly influenced by alloying elements, processing history, and testing procedures—information typically conveyed through textual descriptions. The researchers employ a novel approach, combining natural language processing with deep neural networks, to transform textual data into a structured numerical format that the model can understand. This process extracts valuable information from the text, such as heat treatment details, test solution conditions, and more.

The study presents a comprehensive methodology that combines multiple layers of data processing to achieve its objectives. The process begins with tokenization, wherein words from textual data are transformed into unique integer tokens. Subsequently, word embedding is employed to convert these integer tokens into vectors with semantic meaning, capturing relationships between words. To process this data, recurrent neural network (RNN) layers, specifically long-short term memory (LSTM) layers, are employed. LSTM layers excel in handling sequential data, enabling the model to comprehend the contextual nuances of the text.

The process-aware neural network model demonstrates its prowess by achieving significantly improved accuracy in predicting pitting resistance compared to traditional models. The model showcases its ability to unravel the complexities of alloy behavior by discerning the influence of various alloying elements on pitting resistance. For instance, the model uncovers the contributions of elements like Mo, C, and N in stainless steels and Ni-based alloys. These findings align with existing literature, confirming the model’s efficacy in accurately identifying mechanisms that enhance corrosion resistance.

One of the standout features of this study is its capacity to provide mechanistic insights into corrosion behavior. By analyzing keyword trends within the textual data, the researchers uncover trends that correlate with improved corrosion resistance. Elements such as atomic packing efficiency, electronegativity differences, and configurational entropy are identified as crucial factors in determining pitting resistance. This discovery opens doors for further research, shedding light on the intricate relationships between alloy composition and corrosion performance.

The utility of this approach extends beyond the confines of training data. The study demonstrates the model’s ability to predict the behavior of alloys containing elements absent from the training dataset. This remarkable capability stems from the model’s feature-transformed architecture, which allows it to extrapolate insights from known elemental properties. This advancement holds immense promise in accelerating materials discovery, enabling predictions for unexplored alloy compositions.

In conclusion, the study conducted by Professor Dierk Raabe and colleagues showcases a paradigm shift in predicting corrosion resistance of alloys. By seamlessly integrating textual information with deep neural networks, the researchers have harnessed the power of explainable AI to unravel the intricate relationships between alloy composition, processing history, and corrosion behavior. The results highlight the potential of this methodology to revolutionize materials design, paving the way for the creation of corrosion-resistant alloys with unprecedented precision and efficiency. As industries strive to mitigate the economic burden of corrosion, this research offers a beacon of hope through the fusion of advanced data analytics and materials engineering.

Enhancing Corrosion Resistance Predictions of Alloys Using Process-Aware Neural Networks - Advances in Engineering

About the author

Prof. Dr.-Ing. habil. Dierk Raabe
Department Microstructure Physics and Alloy Design
Sustainable Synthesis of Materials

Dierk Raabe works in the fields of materials simulation, atom probe tomography, microstructures and the resulting mechanical properties of metallic alloys. He wrote and edited several books on this topic, e.g. ‘Computational Materials Science’ (1998), Continuum Scale Simulation of Engineering Materials’ (2005), and ‘Crystal Plasticity FEM in Materials Science and Engineering’ (2010) as well as more than 600 peer reviewed publications. Raabe conducts detailed comparisons between simulation and experiments. He is currently working on issues such as local thermodynamic phase states at confined spaces such as at lattice defects, alloy design, high-entropy alloys, atom probe tomography and the further development of the in-house simulation toolbox DAMASK on crystal mechanics, multiphysics and property prediction of complex materials. A special feature of the research approach is the combination of thermomechanical material processing, simulation and characterization at the atomic level. The common vision of these activities is the use of predictive simulations and their consequent engineering application for inventing advanced alloys. The aim is the physically-based design of materials with superior properties (strength, elongation, damage tolerance) for the fields of energy, mobility and health from the atomic to the macro-scale under consideration of synthesis and processing. The blend of theory, characterization, and processing is important in his approach. Raabe is a frequent plenary and keynote speaker. In 2004 he received the highest German research award (Leibniz-Award). 2008 he was awarded Lee Hsun Lecture Award of the Chinese Academy of Sciences and in 2011 the Weinberg Lecture Award of the University of British Columbia. In 2012 he received an ERC advanced grant, which is the largest single-researcher grant in Europe. Raabe mentored more than 55 Ph.D.s, many of whom now hold leading positions in companies and academia as faculty members in the US, UK, and Asia. Since 2010 he is a member of the German Council of Science and Humanities (Wissenschaftsrat). He is Professor at RWTH Aachen, member of the National Academy Leopoldina and Honorary Professor at the Katholieke Universiteit in Leuven in Belgien.


Kasturi Narasimha Sasidhar, Nima Hamidi Siboni, Jaber Rezaei Mianroodi, Michael Rohwerder, Jörg Neugebauer, Dierk Raabe. Enhancing corrosion-resistant alloy design through natural language processing and deep learning. Science Advances, 2023; 9 (32)

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