arXiv subjects

Raymundo Arróyave

Publications and source records attributed to Raymundo Arróyave.

At least 19 recordsLinked to original sources

arXiv2026

Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery

The discovery and development of advanced materials is a challenging process constrained by the high time and monetary costs of synthesis, processing, and characterization. The underlying design spaces can be enormous, often with multiple competing objectives. Bayesian optimization (BO) provides a principled approach for efficiently navigating such spaces, but most workflows rely on fixed exploration-exploitation policies that lack the capacity to adapt to shifting constraints in dynamic campaigns typical of self-driving laboratories. In this work, we develop a multi-objective BO framework for alloy design under resource constraints, benchmarking strategies for adaptive policy tuning at each iteration. Our evaluation covers a septenary refractory high-entropy alloy (RHEA) system focused on maximizing melting temperature and minimizing density, and an Fe-Co-Ni-based soft magnetic alloy system targeting saturation magnetization, coercivity, and hardness. We compare an exploitation-focused strategy, a fixed mixed exploratory/exploitative policy, and two distinct LLM-based adaptive strategies with different approaches to batch allocation and campaign signal interpretation, evaluated across baseline and mid-campaign resource event conditions including budget reductions, timeline cuts, and combined disruptions. Our results show that mixed allocation strategies accumulate substantially more mutual information than the exploitation-focused baseline at a proportionally smaller cost to hypervolume and optimization speed, with adaptive strategies outperforming a fixed-mixed allocation policy by adjusting their allocation in response to both evolving campaign statistics and resource constraints. These findings suggest that adaptive resource allocation offers a favorable tradeoff for materials discovery campaigns in reducing predictive uncertainty on Pareto-optimal compositions.

cond-mat.mtrl-sci↗

arXiv2026

Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists

We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agents to perform specific tasks within predefined workflows, we aim to automate workflow planning given high-level goals and scientist intuition. To this end, we propose Materials Agent unifying Planning, Physics, and Scientists, known as MAPPS. MAPPS consists of a Workflow Planner, a Tool Code Generator, and a Scientific Mediator. The Workflow Planner uses large language models (LLMs) to generate structured and multi-step workflows. The Tool Code Generator synthesizes executable Python code for various tasks, including invoking a force field foundation model that encodes physics. The Scientific Mediator coordinates communications, facilitates scientist feedback, and ensures robustness through error reflection and recovery. By unifying planning, physics, and scientists, MAPPS enables flexible and reliable materials discovery with greater autonomy, achieving a five-fold improvement in stability, uniqueness, and novelty rates compared with prior generative models when evaluated on the MP-20 data. We provide extensive experiments across diverse tasks to show that MAPPS is a promising framework for autonomous materials discovery.

cs.AI↗

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

HEACalculator: An Open-Source Python Tool for Thermodynamic Property Calculation and Solid Solution Prediction in High-Entropy Alloys

High-entropy alloys (HEAs) have attracted sustained interest since their introduction by Cantor et al. and Yeh et al. because multi-principal-element compositions can exhibit unusual combinations of strength, thermal stability, and functional performance. A recurring problem in HEA design is determining whether a candidate composition is likely to form a single-phase solid solution or instead separate into multiple phases or intermetallic compounds. That question sits early in the alloy-design workflow because it shapes which compositions require further thermodynamic analysis, synthesis, and experimental validation. HEACalculator is an open-source Python package for calculating thermodynamic and structural descriptors used in HEA research and for evaluating published solid-solution formation rules in a single place. It computes sixteen commonly used quantities, including mixing enthalpy, configurational entropy, valence electron concentration, Hume-Rothery electron-to-atom ratio, atomic size mismatch, electronegativity mismatch, and derived stability parameters such as Omega, Lambda, and Phi, and it evaluates eight published prediction criteria. The package combines a curated elemental and binary-interaction dataset with three access modes: a command-line interface (CLI), a desktop graphical user interface (GUI), and a Python application programming interface (API) for programmatic use in notebooks and screening workflows.

cond-mat.mtrl-sci↗

arXiv2026

akaitools: A Python package for parsing and analyzing AkaiKKR electronic structure calculations

The Korringa-Kohn-Rostoker (KKR) Green's function method is a first-principles electronic structure approach well suited to substitutionally disordered alloys through the Coherent Potential Approximation (CPA). AkaiKKR is a widely used implementation, known for efficient treatment of metallic systems and their magnetic properties. Its output, however, is unstructured plain text with no programmatic interface, leaving data extraction entirely to the user and making systematic or high-throughput studies impractical. akaitools is a Python package that parses AkaiKKR output files into structured, type-annotated Python objects. The package covers three output types: self-consistent field (SCF) results, which capture convergence history and per-atom electronic and magnetic properties; spin-resolved, orbital projected density of states for each CPA component; and Bloch spectral functions on a user-defined k-point path. Results come back as immutable dataclasses backed by NumPy arrays. Energy quantities are available in both Rydbergs and electronvolts, and results can be exported to Pandas DataFrames. A built-in plotting module produces Matplotlib figures for DOS curves and SCF convergence. A command-line interface provides file summaries and JSON export without any Python scripting. The package also includes a programmatic input file generator, so full calculation pipelines from input preparation to output analysis can be run in Python.

cond-mat.mtrl-sci↗

arXiv2026

datascribe_api: Enabling Data-Driven Materials Discovery with DataScribe

datascribe_api is a Python library and command-line interface (CLI) that gives researchers programmatic access to DataScribe Cloud, an AI-native platform for materials design and discovery. DataScribe integrates two data services: user-managed scientific data tables, where research groups store experimental measurements, simulation outputs, and derived features; and curated materials science databases from the Materials Project, AFLOW, and OQMD. datascribe_api exposes both through a single, typed Python interface, so researchers can retrieve, filter, and analyze data without writing custom HTTP code. The library handles authentication, connection management, and transient network errors automatically. It exposes nine query endpoints as ordinary Python method calls. Filter conditions use standard Python comparison syntax and are passed as a list, keeping query logic readable without format-specific boilerplate. Pydantic models validate all API responses at parse time, so schema mismatches surface as exceptions rather than silent wrong values. Each result collection converts to a pandas DataFrame in a single call, so retrieved data is directly available for machine learning and statistical analysis. Every materials search result carries a provenance field identifying its source database, satisfying FAIR traceability requirements. The library includes a CLI that exposes all nine endpoints as subcommands, using Typer for argument parsing and Rich for terminal output. Every subcommand supports machine-readable JSON output for shell pipelines and LLM agents. Pre-built wheels for Python 3.11 and later are published on PyPI under the GPLv3 license for Linux, macOS, and Windows.

cs.CE↗

arXiv2026

Element-Specific Solute Trapping and Grain Structure Evolution during Laser Powder Bed Fusion of Multicomponent Alloys

Under the rapid solidification conditions of laser powder bed fusion (LPBF), solute trapping manifests in an element-specific manner, altering nonequilibrium partitioning, constitutional undercooling, and grain selection behavior in multicomponent alloys. Here, we elucidate the mechanisms by which element-specific solute trapping governs nucleation behavior and grain structure evolution during LPBF demonstrated on a SS316L. This requires quantitative description of nonequilibrium multicomponent thermodynamics and grain evolution across broad LPBF solidification conditions, which is achieved through a CALPHAD-informed Gaussian Process Regression (GPR)-assisted Phase-Field (PF) approach. The predicted transitions in grain morphology and grain size are validated against EBSD measurements under multiple LPBF processing conditions. Results demonstrate that increasing solidification rate drives a composition-dependent transition from solute diffusion-controlled nucleation to solute trapping-controlled grain growth, where nonequilibrium solute redistribution intensified by solute trapping suppresses equiaxed grain formation despite high cooling rates. Quantitative decomposition of multicomponent undercooling further reveals distinct element-specific sensitivities to solute trapping, where C, Cr, and Mo remain dominant contributors to the overall undercooling, while the undercooling contribution of low-partitioning elements such as S and P are strongly suppressed relative to their equilibrium values under rapid solidification conditions. These results reveal how element-specific solute trapping governs grain selection in multicomponent alloys, providing a mechanistic basis for alloy design under nonequilibrium solidification conditions.

cond-mat.mtrl-sci↗

arXiv2026

Influence of Coherent Elastic Strain on Phase Separation in BCC Nb-V Alloys

Coherent elastic strain is an important but often neglected contribution to phase-separation thermodynamics in alloy systems where decomposed phases have appreciable lattice mismatch. We develop a thermodynamic framework that incorporates coherent elastic compatibility directly into phase-diagram calculations alongside conventional CALPHAD chemical free energies. Applied to the BCC Nb-V system, the framework shows that coherent elasticity substantially suppresses phase separation, narrows the miscibility gap, and lowers the critical temperature toward experimentally observed values. Beyond these quantitative effects, the coherent constraint qualitatively alters the interpretation of phase equilibria: the equilibrium decomposition compositions become functions of both temperature and overall alloy composition, so the two-phase boundary no longer represents unique coexistence compositions. These results establish coherent elasticity as a key thermodynamic factor in lattice-mismatched systems and provide a general framework for coherent phase-diagram modeling.

cond-mat.mtrl-sci↗

arXiv2026

The Kernel Manifold: A Geometric Approach to Gaussian Process Model Selection

Gaussian Process (GP) regression is a powerful nonparametric Bayesian framework, but its performance depends critically on the choice of covariance kernel. Selecting an appropriate kernel is therefore central to model quality, yet remains one of the most challenging and computationally expensive steps in probabilistic modeling. We present a Bayesian optimization framework built on kernel-of-kernels geometry, using expected divergence-based distances between GP priors to explore kernel space efficiently. A multidimensional scaling (MDS) embedding of this distance matrix maps a discrete kernel library into a continuous Euclidean manifold, enabling smooth BO. In this formulation, the input space comprises kernel compositions, the objective is the log marginal likelihood, and featurization is given by the MDS coordinates. When the divergence yields a valid metric, the embedding preserves geometry and produces a stable BO landscape. We demonstrate the approach on synthetic benchmarks, real-world time-series datasets, and an additive manufacturing case study predicting melt-pool geometry, achieving superior predictive accuracy and uncertainty calibration relative to baselines including Large Language Model (LLM)-guided search. This framework establishes a reusable probabilistic geometry for kernel search, with direct relevance to GP modeling and deep kernel learning.

cs.LG↗

arXiv2026

Simulated Annealing-based Candidate Optimization for Batch Acquisition Functions

Bayesian Optimization with multi-objective acquisition functions such as q-Expected Hypervolume Improvement (qEHVI) requires efficient candidate optimization to maximize acquisition function values. Traditional approaches rely on continuous optimization methods like Sequential Least Squares Programming (SLSQP) for candidate selection. However, these gradient-based methods can become trapped in local optima, particularly in complex or high-dimensional objective landscapes. This paper presents a simulated annealing-based approach for candidate optimization in batch acquisition functions as an alternative to conventional continuous optimization methods. We evaluate our simulated annealing approach against SLSQP across four benchmark multi-objective optimization problems: ZDT1 (30D, 2 objectives), DTLZ2 (7D, 3 objectives), Kursawe (3D, 2 objectives), and Latent-Aware (4D, 2 objectives). Our results demonstrate that simulated annealing consistently achieves superior hypervolume performance compared to SLSQP in most test functions. The improvement is particularly pronounced for DTLZ2 and Latent-Aware problems, where simulated annealing reaches significantly higher hypervolume values and maintains better convergence characteristics. The histogram analysis of objective space coverage further reveals that simulated annealing explores more diverse and optimal regions of the Pareto front. These findings suggest that metaheuristic optimization approaches like simulated annealing can provide more robust and effective candidate optimization for multi-objective Bayesian optimization, offering a promising alternative to traditional gradient-based methods for batch acquisition function optimization.

cs.LG↗

arXiv2025

Predicting interstitial elements in Refractory Complex Concentrated Alloys

Refractory complex concentrated alloys, composed of multiple principal refractory elements, are promising candidates for high-temperature structural applications due to their exceptional thermal stability and high melting points. However, their mechanical performance is often compromised by interstitial impurities, particularly oxygen, nitrogen, and carbon, which segregate to grain boundaries and promote embrittlement. In this study, we investigate the solubility and thermodynamic behavior of oxygen interstitials in a model NbTiHfTa RCCA system. We synthesized NbTiHfTa alloys with varying oxygen contents via plasma arc melting and characterized their phase evolution and microstructure using XRD, SEM, and TEM. Complementary computational modeling was performed using machine-learning interatomic potentials integrated with Monte Carlo simulations to probe oxygen interactions at the atomic scale. Our results reveal a solubility limit for oxygen between 0.8 and 1.0 atomic percentage, beyond which HfO2 formation is energetically favorable. This combined experimental-computational framework provides a predictive approach for managing interstitial behavior in RCCAs, enabling improved alloy design strategies for enhanced mechanical performance.

cond-mat.mtrl-sci↗

arXiv2025

Good Enough is Better: Feasibility vs. Pareto-Optimality in Alloy Design

In alloy design, the search for candidate materials is often framed as an optimization problem, with the goal of identifying Pareto-optimal solutions across multiple objectives. However, Pareto-optimal solutions do not necessarily satisfy all minimum performance thresholds required for practical deployment. An alternative approach is to treat alloy design as a constraint satisfaction problem, in which the goal is to identify any solution that meets all bare minimum requirements across multiple quantities of interest. These approaches have yet to be benchmarked against each other in the context of realistic alloy design problems. In this work, we demonstrate that, in realistic alloy design campaigns involving multiple objectives and constraints, the constraint satisfaction framework yields a higher likelihood of finding viable alloys than optimization-based approaches. Furthermore, constraint-satisfaction approaches find the first viable alloy solutions earlier than optimization. Our results suggest that focusing on feasibility rather than optimality can lead to more actionable outcomes in materials discovery, particularly in highly constrained applications.

cond-mat.mtrl-sci↗

arXiv2025

Mapping of Microstructure Transitions during Rapid Alloy Solidification Using Bayesian-Guided Phase-Field Simulations

This study addresses microstructure selection mechanisms in rapid solidification, specifically targeting the transition from cellular/dendritic to planar interface morphologies under conditions relevant to additive manufacturing. We use a phase-field model that quantitatively captures solute trapping, kinetic undercooling, and morphological instabilities across a broad range of growth velocities ($V$) and thermal gradients ($G$), and apply it to a binary Fe-Cr alloy, as a surrogate for 316L stainless steel. By combining high-fidelity phase-field simulations with a Gaussian Process-based Bayesian active learning approach, we efficiently map the microstructure transitions in the multi-dimensional space of composition, growth velocity, and temperature gradient. We compare our PF results to classical theories for rapid solidification. The classical KGT model yields an accurate prediction of the value of $G$ above which the interface is planar for any growth velocity. Microstructures transition from dendrites to cells as the temperature gradient increases close to this value of $G$. We also identify the occurrence of unstable "intermediate" microstructures at the border between dendritic and planar at low $G$, in the absence of banding instability in this Fe-Cr alloy. Our results highlight the capabilities of Bayesian-guided PF approaches in exploring complex microstructural transitions in multidimensional parameters spaces, thereby providing a robust computational tool for designing process parameters to achieve targeted microstructures and properties in rapidly solidified metallic alloys.

cond-mat.mtrl-sci↗

arXiv2025

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials

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 structures and predicting molecular properties, they are computationally expensive. Machine learning interatomic potentials (MLIPs) have emerged as promising surrogate models that aim to achieve DFT-level accuracy while enabling efficient large-scale atomistic simulations. The development of accurate and transferable MLIPs requires large-scale, high-quality datasets with both energy and force labels. Critically, MLIPs must generalize not only to stable geometries but also to intermediate, non-equilibrium conformations encountered during atomistic simulations. In this work, we introduce PubChemQCR, a large-scale dataset of molecular relaxation trajectories curated from the raw geometry optimization outputs of the PubChemQC project. PubChemQCR is the largest publicly available dataset of DFT-based relaxation trajectories for small organic molecules, comprising approximately 3.5 million trajectories and over 300 million molecular conformations computed at various levels of theory. Each conformation is labeled with both total energy and atomic forces, making the dataset suitable for training and evaluating MLIPs. To provide baselines for future developments, we benchmark nine representative MLIP models on the dataset. Our resources are publicly available at https://huggingface.co/divelab

q-bio.QM↗

arXiv2025

Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation

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 (CIF) file stream, which fails to ensure SE(3) and periodic invariance and may not lead to unique sequence representations for a given crystal structure. Here, we propose a novel method, known as Mat2Seq, to tackle this challenge. Mat2Seq converts 3D crystal structures into 1D sequences and ensures that different mathematical descriptions of the same crystal are represented in a single unique sequence, thereby provably achieving SE(3) and periodic invariance. Experimental results show that, with language models, Mat2Seq achieves promising performance in crystal structure generation as compared with prior methods.

cs.LG↗

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

Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges

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 work explores the use of machine learning interatomic potentials (MLIPs) such as M3GNet, CHGNet, MACE, SevenNet, and ORB to significantly accelerate phase diagram calculations by using the Alloy Theoretic Automated Toolkit (ATAT) to map calculations of the energies and free energies of atomistic systems to CALPHAD-compatible thermodynamic descriptions. Using case studies including Cr-Mo, Cu-Au, and Pt-W, we demonstrate that MLIPs, particularly ORB, achieve computational speedups exceeding three orders of magnitude compared to DFT while maintaining phase stability predictions within acceptable accuracy. Extending this approach to liquid phases and ternary systems like Cr-Mo-V highlights its versatility for high-entropy alloys and complex chemical spaces. This work demonstrates that MLIPs, integrated with tools like ATAT within a CALPHAD framework, provide an efficient and accurate framework for high-throughput thermodynamic modeling, enabling rapid exploration of novel alloy systems. While many challenges remain to be addressed, the accuracy of some of these MLIPs (ORB in particular) are on the verge of paving the way toward high-throughput generation of CALPHAD thermodynamic descriptions of multi-component, multi-phase alloy systems.

cond-mat.mtrl-sci↗

arXiv2024

Supply Risk-Aware Alloy Discovery and Design

Materials design is a critical driver of innovation, yet overlooking the technological, economic, and environmental risks inherent in materials and their supply chains can lead to unsustainable and risk-prone solutions. To address this, we present a novel risk-aware design approach that integrates Supply-Chain Aware Design Strategies into the materials development process. This approach leverages existing language models and text analysis to develop a specialized model for predicting materials feedstock supply risk indices. To efficiently navigate the multi-objective, multi-constraint design space, we employ Batch Bayesian Optimization (BBO), enabling the identification of Pareto-optimal high entropy alloys (HEAs) that balance performance objectives with minimized supply risk. A case study using the MoNbTiVW system demonstrates the efficacy of our approach in four scenarios, highlighting the significant impact of incorporating supply risk into the design process. By optimizing for both performance and supply risk, we ensure that the developed alloys are not only high-performing but also sustainable and economically viable. This integrated approach represents a critical step towards a future where materials discovery and design seamlessly consider sustainability, supply chain dynamics, and comprehensive life cycle analysis.

cs.LG↗