The Taheri-Mousavi Group develops novel multi-scale numerical and analytical frameworks in combination with machine learning techniques to discover next-generation structural alloys produced by various manufacturing techniques (particularly additive manufacturing) and under extreme environmental conditions. We validate our simulations in experiments. Our materials informatics frameworks also guide experiments to be performed efficiently and in a smart manner.
Assistant Professor
Additive Manufacturing (AM) is a rapidly emerging technology that provides new pathways for designing innovative alloys. Its high cooling rate, far beyond traditional casting techniques, creates new pathways for the microstructure design of alloys through solute supersaturation, microstructure refinements, and the formation of metastable phases. Our research exploits these exceptional opportunities to design next-generation Al alloys for AM with high-strength, high-temperature stability, high ductility, and creep resistance. We use advanced hybrid machine learning and integrated computational material engineering (ICME) techniques to explore a high-dimensional processing and composition space. Further research on our Al alloys will pave the way for transformative advances in lightweight high performance structural materials with potential applications in aerospace and automotive industries as vehicle chassis and turbine fan blades.
Hydrogen embrittlement (HE) is the most critical life limiting factor of high strength structural alloys. Some mechanisms associated with H leading to significant change in strain partitioning, and ultimate ductility or fracture toughness loss have been debated for over a century. As plasticity is governed by defects in metals, understanding the statistics of H-defect interactions provides critical insights into this phenomenon. However, experimental investigations of these interactions are highly challenging, time-intensive, and expensive. We utilize hybrid molecular dynamic and grand canonical Monte Carlo simulations to efficiently examine H-diffusion and deformation in unprecedented polycrystalline materials with atomic resolution. We analyze the statistics of H-segregation tendencies, dislocation activities, and strength of materials across various defect types interacting with H. Our new insights complement experimental efforts and provide guidelines to design alloys resistant to HE.
Additive manufacturing provides unique opportunities for material design and performance by enabling tailoring the composition and microstructure at voxel size resolution. However, the experimental exploration of such vast design space is extremely challenging and expensive. Here, we use hybrid CALPHAD-based Integrated Computational Materials Engineering (ICME) simulations and advanced machine learning techniques to provide guidelines on properties of gradient compositional alloys. Our multi-agent generative AI framework quantifies the uncertainties of properties with respect to design space and defines the required experimental validation smartly. Our design platform can be used to accelerate the design of thermo-mechanical properties of structural alloys with higher performance, cost, manufacturability, and sustainability metrics.
Designing additively manufacturable tungsten-based multi principle element alloys with high strength at temperatures beyond 2000°C is extremely challenging. We specifically aim to exploit rare earth and refractory elements in designing these alloys, therefore their processing constraints need to be considered in the design. These alloys will be used in our next-generation structural components in reactors or jet engines. Our research initially explores the high-dimensional compositional and processing space using hybrid generative AI (genAI) and CALPHAD-based ICME techniques. We are developing multi-agent genAI to combine different modality of simulation data and even with literature data. We will downselect on promising candidates and design next experiments smartly. Our validated data will be used to revise our next round of design. The multi-agent genAI design framework and concepts from our research will be used to discover various structural multi-component alloys at extreme temperatures.
Structural alloys play pivotal roles in various engineering applications. And the pursuit of ever stronger, tougher and better alloys often requires effective collaboration between different experts in performing processing, simulations, and experiments and also integration of their various information flows. To handle this fast-growing knowledge base, navigate through the broad design space, and achieve enhanced designs, we invite AI-enabled tools and players into the game and develop the human-AI collaborations for next-generation alloy design. We actively explore language-based models (e.g., AlloyGPT) for integrating knowledge of various formats (figure, text, video, audio, etc.), develop smart agents which can apply modeling/experimental tools (e.g., AlloyGen), and organize autopilot AI teams to discover novel alloy physics and design promising alloy candidates. We expect our efforts will deepen fundamental understanding of structural alloy systems, lead to novel workflows for material discovery and contribute to a sustainable future.
The properties of additively manufactured (AM) structural alloys are extremely sensitive to variations in process parameters, elemental concentrations in powder, heat treatment parameters, and environmental conditions, which can introduce uncertainties in adopting and certifying the AM parts for critical applications. To address these challenges, it is crucial to understand the sources of variabilities and study the sensitivity of properties on design parameters. Our goal is to build a robust framework to quantify the uncertainties in process-structure-property-performance of AM alloys, by leveraging CALPHAD-based ICME combined with machine learning and UQ techniques. We provide new guidelines and certificates (e.g., acceptable ranges of composition or heat treatment parameters) using robust inverse design techniques to achieve application-specific target properties with uncertainties below a pre-defined threshold. Integrating UQ into alloy design is paramount for qualifying AM processes and certifying parts for critical applications, ensuring precision, reliability, and optimal performance.
Video 1: Dr. Taheri-Mousavi discusses her research, which aims to design next generation structural alloys with higher performance and contribute to material sustainability.
Video 2: Group member Ben Glaser won the University's Three Minute Thesis (3MT) competition in 2024. His presentation is available below.
▲ Back to the topCMU Engineering
A recent DARPA award will bring together researchers from multiple universities to develop novel turbomachinery design tools to break the one-part-one-material paradigm.
CMU Engineering
Materials science and engineering and chemical engineering faculty will collaborate on projects supported by the Naval Nuclear Laboratory to create additively manufactured structural alloys that can sustain extreme environments.
Carnegie Mellon University
Seven faculty from the College of Engineering received project funding from this year’s Scott Institute Seed Grants.
Wilton E. Scott Institute for Energy Innovation
MSE’s Mohadeseh Taheri-Mousavi was awarded funding by the 2023 Scott Institute Seed Grants to study hydrogen embrittlement.
Wilton E. Scott Institute for Energy Innovation
Three MSE faculty—Paul Salvador, Mohammad Islam, and Mohadeseh Taheri-Mousavi—were awarded funding by the Scott Institute for Energy Innovation to support cutting-edge research in sustainable energy solutions.
Materials Science and Engineering
Doctoral and postdoctoral scholars from around the country convened at CMU for a program focused on developing their academic careers in MSE.
Scott Institute
Three CMU-led energy projects have been awarded seed grants from the Wilton E. Scott Institute for Energy Innovation.
CMU Engineering
A new NASA Space Technology Research Institute (STRI) led by Carnegie Mellon University seeks to shorten the cycle required to design, manufacture, and test parts that can withstand the conditions of space travel through constructing models for qualification and certification.
Lifewire
MSE’s Mohadeseh Taheri-Mousavi spoke with Lifewire about using AI to discover new materials.
Materials Science and Engineering
Assistant Professor Mohadeseh Taheri-Mousavi develops models to design additively manufactured next-generation structural alloys used in extreme environments.
