Raymundo Arróyave
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View article: Data-driven framework for printability and geometric quality prediction in 3D concrete printing
Data-driven framework for printability and geometric quality prediction in 3D concrete printing Open
View article: Leveraging domain knowledge for optimal initialization in Bayesian materials optimization
Leveraging domain knowledge for optimal initialization in Bayesian materials optimization Open
Bayesian optimization (BO) has emerged as an effective strategy to accelerate the discovery of new materials by efficiently exploring complex and high-dimensional design spaces.
View article: Bringing ML to the real world: rewards are all we need
Bringing ML to the real world: rewards are all we need Open
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “S…
View article: Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes
Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes Open
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 s…
View article: Deep Gaussian Process-based Cost-Aware Batch Bayesian Optimization forComplex Materials Design Campaigns
Deep Gaussian Process-based Cost-Aware Batch Bayesian Optimization forComplex Materials Design Campaigns Open
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, bat…
View article: Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy
Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy Open
High-entropy alloys (HEAs) have attracted increasing attention due to their unique structural and functional properties. In the study of HEAs, thermodynamic properties and phase stability play a crucial role, making phase diagram calculati…
View article: Mapping of microstructure transitions during rapid alloy solidification using Bayesian-guided phase-field simulations
Mapping of microstructure transitions during rapid alloy solidification using Bayesian-guided phase-field simulations Open
View article: Accelerated multi-objective alloy discovery through efficient bayesian methods: Application to the FCC high entropy alloy space
Accelerated multi-objective alloy discovery through efficient bayesian methods: Application to the FCC high entropy alloy space Open
View article: A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials.
A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials. Open
Computational quantum chemistry plays a critical role in drug discovery, chemical synthesis, and materials science. While first-principles methods, such as density functional theory (DFT), provide high accuracy in modeling electronic struc…
View article: Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes Open
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 sy…
View article: High-throughput alloy and process design for metal additive manufacturing
High-throughput alloy and process design for metal additive manufacturing Open
Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of …
View article: Analytical Gradient-Based Optimization of CALPHAD Model Parameters
Analytical Gradient-Based Optimization of CALPHAD Model Parameters Open
The calibration of CALPHAD (CALculation of PHAse Diagrams) models involves the solution of a very challenging high-dimensional multiobjective optimization problem. Traditional approaches to parameter fitting predominantly rely on gradient-…
View article: Directed energy deposition of functionally graded V-4Cr-4Ti to Fe-9Cr transition for fusion power systems
Directed energy deposition of functionally graded V-4Cr-4Ti to Fe-9Cr transition for fusion power systems Open
View article: Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces
Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces Open
View article: Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation Open
We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior studies used the crystallographic information framework (CI…
View article: Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design Open
Alloy design can be framed as a constraint-satisfaction problem. Building on previous methodologies, we propose equipping Gaussian Process Classifiers (GPCs) with physics-informed prior mean functions to model the boundaries of feasible de…
View article: Compositionally Grading Alloy Stacking Fault Energy using Autonomous Path Planning and Additive Manufacturing with Elemental Powders
Compositionally Grading Alloy Stacking Fault Energy using Autonomous Path Planning and Additive Manufacturing with Elemental Powders Open
Compositionally graded alloys (CGAs) are often proposed for use in structural components where the combination of two or more alloys within a single part can yield substantial enhancement in performance and functionality. For these applica…
View article: Towards Autonomous Experimentation: Bayesian Optimization over Problem Formulation Space for Accelerated Alloy Development
Towards Autonomous Experimentation: Bayesian Optimization over Problem Formulation Space for Accelerated Alloy Development Open
Accelerated discovery in materials science demands autonomous systems capable of dynamically formulating and solving design problems. In this work, we introduce a novel framework that leverages Bayesian optimization over a problem formulat…
View article: Microstructure-Aware Bayesian Materials Design
Microstructure-Aware Bayesian Materials Design Open
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…
View article: Compositionally Grading Alloy Stacking Fault Energy Using Autonomous Path Planning and Additive Manufacturing with Elemental Powders
Compositionally Grading Alloy Stacking Fault Energy Using Autonomous Path Planning and Additive Manufacturing with Elemental Powders Open
View article: Decoding non-linearity and complexity: deep tabular learning approaches for materials science
Decoding non-linearity and complexity: deep tabular learning approaches for materials science Open
Materials datasets often contain skewed distributions, broad feature ranges, and multimodal behavior, posing challenges for machine learning. These complexities call for models that capture non-linearity and structure in tabular data.
View article: Machine Learning-Based Prediction and Geometric Quality Assessment in 3dcp
Machine Learning-Based Prediction and Geometric Quality Assessment in 3dcp Open
View article: Microstructure-Aware Bayesian Materials Design
Microstructure-Aware Bayesian Materials Design Open
View article: Analytical Gradient-Based Optimization of CALPHAD Model Parameters
Analytical Gradient-Based Optimization of CALPHAD Model Parameters Open
View article: Physics-informed Gaussian process classification for constraint-aware alloy design
Physics-informed Gaussian process classification for constraint-aware alloy design Open
Alloy design can be framed as a constraint-satisfaction problem. Building on previous methodologies, we propose equipping Gaussian Process Classifiers (GPCs) with physics-informed prior mean functions to model the centers of feasible desig…
View article: Visualizing high entropy alloy spaces: methods and best practices
Visualizing high entropy alloy spaces: methods and best practices Open
Multi-Principal Element Alloys (MPEAs) have emerged as an exciting area of research in materials science in the 2020s, owing to the vast potential for discovering alloys with unique and tailored properties enabled by the combinations of el…
View article: Performance-driven Computational Design of Multi-terminal Compositionally Graded Alloy Structures using Graphs
Performance-driven Computational Design of Multi-terminal Compositionally Graded Alloy Structures using Graphs Open
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 monoli…
View article: Decoding Non-Linearity and Complexity: Deep Tabular Learning Approaches for Materials Science
Decoding Non-Linearity and Complexity: Deep Tabular Learning Approaches for Materials Science Open
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 li…
View article: Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges Open
Accurate phase diagram prediction is crucial for understanding alloy thermodynamics and advancing materials design. While traditional CALPHAD methods are robust, they are resource-intensive and limited by experimentally assessed data. This…
View article: An automated computational framework to construct printability maps for additively manufactured metal alloys
An automated computational framework to construct printability maps for additively manufactured metal alloys Open
In metal additive manufacturing (AM), processing parameters can affect the probability of macroscopic defect formation (lack-of-fusion, keyholing, balling), which can, in turn, jeopardize the final product’s integrity. A printability map c…