arXiv subjects

Vahid Attari

Publications and source records attributed to Vahid Attari.

At least 19 recordsLinked to original sources

arXiv2026

From MLIPs to Microstructure: A High-Throughput Computational Framework to Design Spinodal Alloys in High-Dimensional Composition Spaces via Analytic Derivatives of CALPHAD Model Predictions

Identifying regions of design space subject to spinodal decomposition is a critical component of alloy design in high-dimensional composition spaces. In cases where designers are seeking to exploit spinodal microstructures to tailor alloy properties, prediction of microstructure evolution and morphology is also needed. In this work, we present a Machine Learning Interatomic Potential (MLIP)-trained, CALPHAD-based, open-source workflow for high-throughput microstructure stability analysis and visualization. In this workflow, coherent strain contributions are captured via high-throughput MLIP elastic constant calculations. To predict microstructure morphology for compositions of interest, MLIP-generated thermodynamic models are fed into an elasto-chemical phase field simulation. Both stability analyses and phase-field simulations utilize analytically-derived Gibbs energy Hessians to improve computational efficiency and accuracy over finite difference approximations. We demonstrate this workflow by investigating microstructure stability in the Hf-Nb-Ti-V quaternary system.

cond-mat.mtrl-sci↗

arXiv2026

A BRAVE Alloy Design Campaign (Bayesian Risk-aware Alloy discoVery and Exploration)

In constrained alloy optimization, the compositions with the highest performance potential often reside at the boundary of phase stability -- where the risk of experimental failure is also highest. This work demonstrates this principle through a risk-aware Bayesian optimization campaign on single-phase FCC high-entropy alloys in the Al-V-Cr-Mn-Fe-Co-Ni-Cu system. A learned feasibility classifier, integrated directly into the multi-objective acquisition function, probabilistically penalizes candidates likely to produce failed experiments while preserving access to high-performing boundary compositions. From approximately 27,000 CALPHAD-screened candidates, 48 alloys were synthesized over three closed-loop iterations targeting five objectives (yield strength, UTS/YS ratio, strain at UTS, dynamic-to-quasi-static hardness ratio, and simulated depth of penetration), exploring 0.12\% of the feasible space. Two compositional regimes emerged: a V-rich, Ni-rich high-strength regime (UTS up to ${\sim}1480$~MPa at 50% elongation) and a Mn-containing high-ductility regime (UTS/YS up to 4.20 at $>$50% elongation). Among feasible alloys, vanadium simultaneously drives yield strength ($r = 0.84$) and sigma-phase formation ($r = 0.54$ with infeasibility); at V = 24~at.%, the three strongest alloys and three sigma failures share the same compositional point. Additionally, the strongest performing alloys cluster in a narrow region of compositional space (V $\geq$ 20 at.%, Ni $\geq$ 36 at.%), representing ${\sim}100$ of $27,074$ feasible candidates -- a probability of $P \approx 6.5 \times 10^{-6}$ under random sampling. This dual role -- consistent with the KKT prediction that constrained optima lie on active constraint boundaries -- required feasibility-aware acquisition to access; hard filtering would have excluded this region entirely.

cond-mat.mtrl-sci↗

arXiv2026

Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry

Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. The Materials Acceleration Platform for Electrochemistry (MAP-E) is an autonomous, high-throughput system, capable of performing parallel electrochemical experiments. It integrates robotic liquid handling, sample transfer with a multi-channel potentiostatic control to extract corrosion metrics without human intervention. Validation against an ASTM G61-analog benchmark demonstrates good reproducibility, with a standard deviation of 75 mV in pitting potential across 32 automated measurements. The platform was then employed to autonomously construct pH-chloride stability diagrams for 304 stainless steel using an uncertainty-driven sampling strategy on a Gaussian process surrogate model. This approach reduces operator involvement and accelerates the exploration of environmental spaces. The MAP-E establishes a framework for autonomous electrochemical experimentation, enabling generation of corrosion datasets that inform materials discovery, alloy design, and durability assessment in service environments.

cond-mat.mtrl-sci↗

arXiv2026

DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery

The acceleration of materials discovery requires digital platforms that go beyond data repositories to embed learning, optimization, and decision-making directly into research workflows. We introduce DataScribe, an AI-native, cloud-based materials discovery platform that unifies heterogeneous experimental and computational data through ontology-backed ingestion and machine-actionable knowledge graphs. The platform integrates FAIR-compliant metadata capture, schema and unit harmonization, uncertainty-aware surrogate modeling, and native multi-objective multi-fidelity Bayesian optimization, enabling closed-loop propose-measure-learn workflows across experimental and computational pipelines. DataScribe functions as an application-layer intelligence stack, coupling data governance, optimization, and explainability rather than treating them as downstream add-ons. We validate the platform through case studies in electrochemical materials and high-entropy alloys, demonstrating end-to-end data fusion, real-time optimization, and reproducible exploration of multi-objective trade spaces. By embedding optimization engines, machine learning, and unified access to public and private scientific data directly within the data infrastructure, and by supporting open, free use for academic and non-profit researchers, DataScribe functions as a general-purpose application-layer backbone for laboratories of any scale, including self-driving laboratories and geographically distributed materials acceleration platforms, with built-in support for performance, sustainability, and supply-chain-aware objectives.

cs.LG↗

arXiv2025

Deep Gaussian Process-based Cost-Aware Batch Bayesian Optimization for Complex Materials Design Campaigns

The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast, nonlinear design spaces while judiciously allocating limited evaluation resources. We present a cost-aware, batch Bayesian optimization scheme powered by deep Gaussian process (DGP) surrogates and a heterotopic querying strategy. Our DGP surrogate, formed by stacking GP layers, models complex hierarchical relationships among high-dimensional compositional features and captures correlations across multiple target properties, propagating uncertainty through successive layers. We integrate evaluation cost into an upper-confidence-bound acquisition extension, which, together with heterotopic querying, proposes small batches of candidates in parallel, balancing exploration of under-characterized regions with exploitation of high-mean, low-variance predictions across correlated properties. Applied to refractory high-entropy alloys for high-temperature applications, our framework converges to optimal formulations in fewer iterations with cost-aware queries than conventional GP-based BO, highlighting the value of deep, uncertainty-aware, cost-sensitive strategies in materials campaigns.

cond-mat.mtrl-sci↗

arXiv2025

Data Driven Insights into Composition Property Relationships in FCC High Entropy Alloys

Structural High Entropy Alloys (HEAs) are crucial in advancing technology across various sectors, including aerospace, automotive, and defense industries. However, the scarcity of integrated chemistry, process, structure, and property data presents significant challenges for predictive property modeling. Given the vast design space of these alloys, uncovering the underlying patterns is essential yet difficult, requiring advanced methods capable of learning from limited and heterogeneous datasets. This work presents several sensitivity analyses, highlighting key elemental contributions to mechanical behavior, including insights into the compositional factors associated with brittle and fractured responses observed during nanoindentation testing in the BIRDSHOT center NiCoFeCrVMnCuAl system dataset. Several encoder decoder based chemistry property models, carefully tuned through Bayesian multi objective hyperparameter optimization, are evaluated for mapping alloy composition to six mechanical properties. The models achieve competitive or superior performance to conventional regressors across all properties, particularly for yield strength and the UTS/YS ratio, demonstrating their effectiveness in capturing complex composition property relationships.

cond-mat.mtrl-sci↗

arXiv2025

Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the fitting performance of four prominent surrogate models conventional Gaussian Processes(cGP), Deep Gaussian Processes(DGP), encoder-decoder neural networks for multi-output regression and XGBoost applied to a hybrid dataset of experimental and computational properties in the AlCoCrCuFeMnNiV HEA system. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic and quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical and deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials informatics. Our findings illustrate that DGP infused with machine learning-based prior outperform other surrogates by effectively capturing inter-property correlations and input-dependent uncertainty. This enhanced predictive accuracy positions advanced surrogate models as powerful tools for robust and data-efficient materials design.

cs.LG↗

arXiv2025

Accelerated Multi-Objective Alloy Discovery through Efficient Bayesian Methods: Application to the FCC Alloy Space

This study introduces BIRDSHOT, an integrated Bayesian materials discovery framework designed to efficiently explore complex compositional spaces while optimizing multiple material properties. We applied this framework to the CoCrFeNiVAl FCC high entropy alloy (HEA) system, targeting three key performance objectives: ultimate tensile strength/yield strength ratio, hardness, and strain rate sensitivity. The experimental campaign employed an integrated cyber-physical approach that combined vacuum arc melting (VAM) for alloy synthesis with advanced mechanical testing, including tensile and high-strain-rate nanoindentation testing. By incorporating batch Bayesian optimization schemes that allowed the parallel exploration of the alloy space, we completed five iterative design-make-test-learn loops, identifying a non-trivial three-objective Pareto set in a high-dimensional alloy space. Notably, this was achieved by exploring only 0.15% of the feasible design space, representing a significant acceleration in discovery rate relative to traditional methods. This work demonstrates the capability of BIRDSHOT to navigate complex, multi-objective optimization challenges and highlights its potential for broader application in accelerating materials discovery.

cond-mat.mtrl-sci↗

arXiv2025

Microstructure-Aware Bayesian Materials Design

In this study, we propose a novel microstructure-sensitive Bayesian optimization (BO) framework designed to enhance the efficiency of materials discovery by explicitly incorporating microstructural information. Traditional materials design approaches often focus exclusively on direct chemistry-process-property relationships, overlooking the critical role of microstructures. To address this limitation, our framework integrates microstructural descriptors as latent variables, enabling the construction of a comprehensive process-structure-property mapping that improves both predictive accuracy and optimization outcomes. By employing the active subspace method for dimensionality reduction, we identify the most influential microstructural features, thereby reducing computational complexity while maintaining high accuracy in the design process. This approach also enhances the probabilistic modeling capabilities of Gaussian processes, accelerating convergence to optimal material configurations with fewer iterations and experimental observations. We demonstrate the efficacy of our framework through synthetic and real-world case studies, including the design of Mg$_2$Sn$_x$Si$_{1-x}$ thermoelectric materials for energy conversion. Our results underscore the critical role of microstructures in linking processing conditions to material properties, highlighting the potential of a microstructure-aware design paradigm to revolutionize materials discovery. Furthermore, this work suggests that since incorporating microstructure awareness improves the efficiency of Bayesian materials discovery, microstructure characterization stages should be integral to automated -- and eventually autonomous -- platforms for materials development.

cond-mat.mtrl-sci↗

arXiv2024

Performance-driven Computational Design of Multi-terminal Compositionally Graded Alloy Structures using Graphs

The spatial control of material placement afforded by metal additive manufacturing (AM) has enabled significant progress in the development and implementation of compositionally graded alloys (CGAs) for spatial property variation in monolithic structures. However, cracking and brittle phase formation have hindered CGA development, with limited research extending beyond materials design to structural design. Notably, the high-dimensional alloy design space (systems with more than three active elements) remains poorly understood, specifically for CGAs. As a result, many prior efforts take a trial-and-error approach. Additionally, current structural design methods are inadequate for joining dissimilar alloys. In light of these challenges, recent work in graph information modeling and design automation has enabled topological partitioning and analysis of the alloy design space, automated design of multi-terminal CGAs, and automated conformal mapping of CGAs onto corresponding structural geometries. In comparison, prior gradient design approaches are limited to two-terminal CGAs. Here, we integrate these recent advancements, demonstrating a unified performance-driven CGA design approach on a gas turbine blade with broader application to other material systems and engineering structures.

cond-mat.mtrl-sci↗

arXiv2024

Decoding Non-Linearity and Complexity: Deep Tabular Learning Approaches for Materials Science

Materials data, especially those related to high-temperature properties, pose significant challenges for machine learning models due to extreme skewness, wide feature ranges, modality, and complex relationships. While traditional models like tree-based ensembles (e.g., XGBoost, LightGBM) are commonly used for tabular data, they often struggle to fully capture the subtle interactions inherent in materials science data. In this study, we leverage deep learning techniques based on encoder-decoder architectures and attention-based models to handle these complexities. Our results demonstrate that XGBoost achieves the best loss value and the fastest trial duration, but deep encoder-decoder learning like Disjunctive Normal Form architecture (DNF-nets) offer competitive performance in capturing non-linear relationships, especially for highly skewed data distributions. However, convergence rates and trial durations for deep model such as CNN is slower, indicating areas for further optimization. The models introduced in this study offer robust and hybrid solutions for enhancing predictive accuracy in complex materials datasets.

cond-mat.mtrl-sci↗

arXiv2024

Illustrating an Effective Workflow for Accelerated Materials Discovery

Algorithmic materials discovery is a multi-disciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, computational modeling, and optimization. Central to this effort is a robust data management system paired with an interactive work platform. This platform should empower users to not only access others data but also integrate their analyses, paving the way for sophisticated data pipelines. To realize this vision, there is a need for an integrative collaboration platform, streamlined data sharing and analysis tools, and efficient communication channels. Such a collaborative mechanism should transcend geographical barriers, facilitating remote interaction and fostering a challenge-response dynamic. In this paper, we present our ongoing efforts in addressing the critical challenges related to an accelerated Materials Discovery Framework as a part of the High-Throughput Materials Discovery for Extreme Conditions Initiative. Our BIRDSHOT Center has successfully harnessed various tools and strategies, including the utilization of cloud-based storage, a standardized sample naming convention, a structured file system, the implementation of sample travelers, a robust sample tracking method, and the incorporation of knowledge graphs for efficient data management. Additionally, we present the development of a data collection platform, reinforcing seamless collaboration among our team members. In summary, this paper provides an illustration and insight into the various elements of an efficient and effective workflow within an accelerated materials discovery framework while highlighting the dynamic and adaptable nature of the data management tools and sharing platforms.

cond-mat.mtrl-sci↗

arXiv2023

Phase-Field Model of Silicon Carbide Growth During Isothermal Condition

Silicon carbide (SiC) emerges as a promising ceramic material for high-temperature structural applications, especially within the aerospace sector. The utilization of SiC-based ceramic matrix composites (CMCs) instead of superalloys in components like engine shrouds, combustors, and nozzles offers notable advantages, including a 25% improvement in fuel efficiency, over 10% enhanced thrust, and the capability to withstand up to 500$^{\circ}$C higher operating temperatures. Employing a CALPHAD-reinforced multi-phase-field model, our study delves into the evolution of the SiC layer under isothermal solidification conditions. By modeling the growth of SiC between liquid Si and solid C at 1450$^{\circ}$C, we compared results with experimental microstructures and quantitatively examined the evolution of SiC thickness over time. Efficient sampling across the entire model space mitigated uncertainty in high-temperature kinetic parameters, allowing us to predict a range of growth rates and morphologies for the SiC layer. The model accounts for parameter uncertainty stemming from limited experimental knowledge and successfully predicts relevant morphologies for the system. Experimental results validated the kinetic parameters of the simulations, offering valuable insights and potential constraints on the reaction kinetics. We further explored the significance of multi-phase-field model parameters on two key outputs, and found that the diffusion coefficient of liquid Si emerges as the most crucial parameter significantly impacting the SiC average layer thickness and grain count over time. This study provides valuable insights into the microstructure evolution of the Si-C binary system, offering pertinent information for the engineering of CMCs in industrial applications.

physics.app-ph↗

arXiv2023

Efficient Propagation of Uncertainty via Reordering Monte Carlo Samples

Uncertainty analysis in the outcomes of model predictions is a key element in decision-based material design to establish confidence in the models and evaluate the fidelity of models. Uncertainty Propagation (UP) is a technique to determine model output uncertainties based on the uncertainty in its input variables. The most common and simplest approach to propagate the uncertainty from a model inputs to its outputs is by feeding a large number of samples to the model, known as Monte Carlo (MC) simulation which requires exhaustive sampling from the input variable distributions. However, MC simulations are impractical when models are computationally expensive. In this work, we investigate the hypothesis that while all samples are useful on average, some samples must be more useful than others. Thus, reordering MC samples and propagating more useful samples can lead to enhanced convergence in statistics of interest earlier and thus, reducing the computational burden of UP process. Here, we introduce a methodology to adaptively reorder MC samples and show how it results in reduction of computational expense of UP processes.

cs.LG↗

arXiv2022

On the Effect of Nucleation Undercooling on Phase Transformation Kinetics

We carry out an extensive comparison between Johnson-Mehl-Avrami-Kolmogorov (JMAK) theory of first-order phase transformation kinetics and phase-field (PF) results of a benchmark problem on nucleation. To address the stochasticity of the problem, several hundreds of simulations are performed to establish a comprehensive, statistically-significant analysis of the coincidences and discrepancies between PF and JMAK transformation kinetics. We find that PF predictions are in excellent agreement with both classical nucleation theory and JMAK theory, as long as the original assumptions of the latter are appropriately reproduced - in particular, the constant nucleation and growth rates in an infinite domain. When deviating from these assumptions, PF results are at odds with JMAK theory. In particular, we observe that the size of the initial particle radius $r_0$ relative to the critical nucleation radius $r^*$ has a significant effect on the rate of transformation. While PF and JMAK agree when $r_0$ is sufficiently higher than $r^*$, the duration of initial transient growth stage of a particle, before it reaches a steady growth velocity, increases as $r_0/r^*\to 1$. This incubation time has a significant effect on the overall kinetics, e.g. on the Avrami exponent of the multi-particle simulations. In contrast, for the considered conditions and parameters, the effect of interface curvature upon transformation kinetics - in particular negative curvature regions appearing during particle impingement, present in PF but absent in JMAK theory - appears to be minor compared to that of $r_0/r^*$. We argue that rigorous benchmarking of phase-field models of stochastic processes (e.g. nucleation) need sufficient statistical data in order to make rigorous comparisons against ground truth theories. In these problems, analysis of probability distributions is clearly preferable to a deterministic approach.

cond-mat.mtrl-sci↗

arXiv2020

Deep Multimodal Transfer-Learned Regression in Data-Poor Domains

In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc. This task can be further complicated by a lack of sufficient data. Here we propose a Deep Multimodal Transfer-Learned Regressor (DMTL-R) for multimodal learning of image and feature data in a deep regression architecture effective at predicting target parameters in data-poor domains. Our model is capable of fine-tuning a given set of pre-trained CNN weights on a small amount of training image data, while simultaneously conditioning on feature information from a complimentary data mode during network training, yielding more accurate single-target or multi-target regression than can be achieved using the images or the features alone. We present results using phase-field simulation microstructure images with an accompanying set of physical features, using pre-trained weights from various well-known CNN architectures, which demonstrate the efficacy of the proposed multimodal approach.

cs.CV↗

arXiv2020

Finite Interface Dissipation Phase Field Modeling of Ni-Nb under Additive Manufacturing Conditions

During the laser powder bed fusion (L-PBF) process, the built part undergoes multiple rapid heating-cooling cycles, leading to complex microstructures with nonuniform properties. In the present work, a computational framework, which weakly couples a finite element thermal model to a non-equilibrium PF model was developed to investigate the rapid solidification microstructure of a Ni-Nb alloy during L-PBF. The framework is utilized to predict the spatial variation of the morphology and size of cellular segregation structure as well as the microsegregation in single-track melt pool microstructures obtained under different process conditions. A solidification map demonstrating the variation of microstructural features as a function of the temperature gradient and growth rate is presented. A planar to cellular transition is predicted in the majority of keyhole mode melt pools, while a planar interface is predominant in conduction mode melt pools. The predicted morphology and size of the cellular segregation structure agrees well with experimental measurements.

physics.app-ph↗

arXiv2019

Uncertainty Propagation in a Multiscale CALPHAD-Reinforced Elastochemical Phase-field Model

ICME approaches provide decision support for materials design by establishing quantitative process-structure-property relations. Confidence in the decision support, however, must be achieved by establishing uncertainty bounds in ICME model chains. The quantification and propagation of uncertainty in computational materials science, however, remains a rather unexplored aspect of computational materials science approaches. Moreover, traditional uncertainty propagation frameworks tend to be limited in cases with computationally expensive simulations. A rather common and important model chain is that of CALPHAD-based thermodynamic models of phase stability coupled to phase field models for microstructure evolution. Propagation of uncertainty in these cases is challenging not only due to the sheer computational cost of the simulations but also because of the high dimensionality of the input space. In this work, we present a framework for the quantification and propagation of uncertainty in a CALPHAD-based elasto-chemical phase field model. We motivate our work by investigating the microstructure evolution in Mg$_2$(Si$_x$Sn$_{1-x}$) thermoelectric materials. We first carry out a Markov Chain Monte Carlo-based inference of the CALPHAD model parameters for this pseudobinary system and then use advanced sampling schemes to propagate uncertainties across a high-dimensional simulation input space. Through high-throughput phase field simulations we generate 200,000 time series of synthetic microstructures and use machine learning approaches to understand the effects of propagated uncertainties on the microstructure landscape of the system under study. The microstructure dataset has been curated in the Open Phase-field Microstructure Database (OPMD), available at \href{http://microstructures.net}{http://microstructures.net}.

cond-mat.mtrl-sci↗