arXiv subjects

Doğuhan Sarıtürk

Publications and source records attributed to Doğuhan Sarıtürk.

5 recordsLinked to original sources

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↗

arXiv2025

A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures

Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLIPs involves the trade-off between invariant and equivariant architectures. Invariant models offer computational efficiency but may not perform as well, especially when predicting high-order outputs. In contrast, equivariant models can capture high-order symmetries, but are computationally expensive. In this work, we propose HIENet, a hybrid invariant-equivariant materials interatomic potential model that integrates both invariant and equivariant message passing layers, while provably satisfying key physical constraints. HIENet achieves state-of-the-art performance with considerable computational speedups over prior models. Experimental results on both common benchmarks and downstream materials discovery tasks demonstrate the efficiency and effectiveness of HIENet.

cs.LG↗

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↗