Engineering topics
Publications and source records attributed to Pei, Zongrui.
Large language models are very effective at solving general tasks, but can also be useful in materials design and extracting and using information from the scientific literature and unstructured corpora. For instance, in the domain of alloy design and manufacturing, they can expedite the materials design process and enable the inclusion of holistic criteria.
Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.
Designing novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated high-dimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4%. The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. Finally, this study provides a practical and general approach to designing CCAs based on quantum technologies.
Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. In this study, the stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young's, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior in fcc MPEAs.
The interest in high entropy ceramics (HECs) has increased steadily due to their superior properties. However, the prediction of their formation still poses challenges for the discovery of new systems. Here, we discover a rational rule for designing single-phase high entropy metal diborides (HEBs) using data-driven approach. The machine learning (ML) model is trained on data collected via high-throughput experiments (HTEs). K nearest neighbor (KNN) model shows an experimental validation accuracy of 93.75%. By implementing interpretable ML method, we demonstrate that a mismatch of the bonds between boron and transition metals (δ B-TM ) dominates the formation of HEBs. We propose an empirical rule that HEBs favor forming a single phase when δ B-TM < 3.66; otherwise, multiphase. The rule has a high accuracy of 93.33% for new HEBs predictions. In addition, we contribute 165 high quality HEBs data in total, which can promote the development of materials informatics in HEBs. Furthermore, this data-driven strategy can be expanded to accelerate the search for new HECs, paving a pathway to design novel HECs with superior properties rapidly.
Chemical randomness and the associated energy fluctuation are essential features of multi-principal element alloys (MPEAs). Due to these features, nanoscale stacking fault energy (SFE) fluctuation is a natural and independent contribution to strengthening MPEAs. However, existing models for conventional alloys (i.e., alloys with one principal element) cannot be applied to MPEAs. The extreme values of SFEs required by such models are unknown for MPEAs, which need to calculate the nanoscale volume relevant to the SFE fluctuation. In the present work, we developed an analytic model to evaluate the strengthening effect through the SFE fluctuation, profuse in MPEAs. The model has no adjustable parameters, and all parameters can be determined from experiments and ab initio calculations. Further, this model explains available experimental observations and provides insightful guidance for designing new MPEAs based on the SFE fluctuation. It generally applies to MPEAs in random states and with chemical short-range order.
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This virtual presentation was given at HEA 2021 Congress.
We report that mechanical properties are fundamental to structural materials, where dislocations play a decisive role in describing their mechanical behavior. Although the high-yield stresses of multiprincipal element alloys (MPEAs) have received extensive attention in the last decade, the relation between their mechanistic origins remains elusive. Our multiscale study of density functional theory, atomistic simulations, and high-resolution microscopy shows that the excellent mechanical properties of MPEAs have diverse origins. The strengthening effects through Shockley partials and stacking faults can be decoupled in MPEAs, breaking the conventional wisdom that low stacking fault energies are coupled with wide partial dislocations. This study clarifies the mechanistic origins for the strengthening effects, laying the foundation for physics-informed predictive models for materials design.
Metallurgy and material design have thousands of years’ history and have played a critical role in the civilization process of humankind. The traditional trial-and-error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increasing, with high-entropy alloys as the representative. New opportunities emerge for alloy design in the artificial intelligence era. Here a successful machine-learning (ML) method is developed to identify the microstructure images with eye-challenging morphology for a number of martensitic and ferritic steels. Assisted by it, a new neural-network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure. The method is also readily applied to other material systems given sufficient microstructure images. This work lays the foundation for inverse alloy design based on microstructure images with extremely similar features.
Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters, including the order–disorder phase transition. However, finding a representative order parameter for complex systems is non-trivial, such as for high-entropy alloys. Given the strength of dimensionality reduction of a variational autoencoder (VAE), we introduce a VAE-based order parameter. Here, we propose that the Manhattan distance in the VAE latent space can serve as a generic order parameter for order–disorder phase transitions. The physical properties of our order parameter are quantitatively interpreted and demonstrated by multiple refractory high-entropy alloys. Using this order parameter, a generally applicable alloy design concept is proposed by mimicking the natural mixing process of elements. Our physically interpretable VAE-based order parameter provides a computational technique for understanding chemical ordering in alloys, which can facilitate the development of rational alloy design strategies.
The effect of Si contamination when using B to improve the creep properties of a Ni-based superalloy was investigated using advanced characterization techniques and first-principles simulations on alloys with high and low B levels with varying Si contents. The positive effect of B segregation along grain boundaries on the creep properties was mitigated by the presence of Si which showed a similar segregation preference. Density functional theory calculations were used for validation by calculating grain boundary cleavage energies. Silicon was shown to decrease grain boundary cohesion which offsets the positive effect of B.
Developing affordable and light high-temperature materials alternative to Ni-base superalloys has significantly increased the efforts in designing advanced ferritic superalloys. However, currently developed ferritic superalloys still exhibit low high-temperature strengths, which limits their usage. Here we use a CALPHAD-based high-throughput computational method to design light, strong, and low-cost high-entropy alloys for elevated-temperature applications. Through the high-throughput screening, precipitation-strengthened lightweight high-entropy alloys are discovered from thousands of initial compositions, which exhibit enhanced strengths compared to other counterparts at room and elevated temperatures. The experimental and theoretical understanding of both successful and failed cases in their strengthening mechanisms and order-disorder transitions further improves the accuracy of the thermodynamic database of the discovered alloy system. This study shows that integrating high-throughput screening, multiscale modeling, and experimental validation proves to be efficient and useful in accelerating the discovery of advanced precipitation-strengthened structural materials tuned by the high-entropy alloy concept.
TMS 2021 Annual Meeting & Exhibition, Virtual, March 15-18, 2021
An alloy's processing history, including melting, remelting or the choice of stock material, affects its purity and eventually involves tramp element pickup or retention. Herein, variants of a novel Ni-based superalloy were manufactured with different levels of purity. The so-called low purity alloys contained 0.138 wt. % Cu and 0.019 wt. % Si while the Cu and Si levels were below x-ray fluorescence (XRF) detection limits of 0.003 and 0.010 wt. %, respectively, in the high purity ingots. Atom-probe tomography (APT) was carried out and revealed Si partitioning at the following interfaces: grain boundaries, MC carbide/γ, M 3 B 2 boride/$γ$ and M 3 B 2 boride/$γ$'. Copper was found to primarily segregate to the $γ$' precipitates. An average of 2.4 × decrease in creep life and 4.3 × decrease in creep ductility was measured in the low purity alloys, which was attributed to the embrittlement caused by Si segregation to grain boundaries. Furthermore, the positive effect of B on the creep properties was mitigated by the presence of Si. Thermodynamic predictions for the matrix and γ' precipitate compositions represented the trends observed experimentally although the extent of preferential partitioning lacks accuracy. Monte Carlo simulations were performed to describe the partitioning of Cu and Si atoms to either $γ$ or $γ$' phases.
The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability. Some rules with seemingly good predictability were, however, tested using small data sets. Based on an unprecedented large dataset containing 1252 multicomponent alloys, machine-learning methods showed that the formation of solid solutions can be very accurately predicted (93%). The machine-learning results help identify the most important features, such as molar volume, bulk modulus, and melting temperature. As such a new thermodynamics-based rule was developed to predict solid–solution alloys. The new rule is nonetheless slightly less accurate (73%) but has roots in the physical nature of the problem. The new rule is employed to predict solid solutions existing in the three blocks, each of which consists of 9 elements. The predictions encompass face-centered cubic (FCC), body-centered cubic (BCC), and hexagonal closest packed (HCP) structures in a high throughput manner. The validity of the prediction is further confirmed by CALculations of PHAse Diagram (CALPHAD) calculations with high consistency (94%). Since the new thermodynamics-based rule employs only elemental properties, applicability in screening for solid solution high-entropy alloys is straightforward and efficient.
Presented by NETL at TMS 2020, 02/20
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