arXiv subjects

Taichi Abe

Publications and source records attributed to Taichi Abe.

6 recordsLinked to original sources

arXiv2026

Machine Learning Compatible CALPHAD-type Optimization from Phase Equilibria by Auto-differentiation

To accurately determine phase boundaries and phase transitions, thermodynamic models that describe free energies of phases often have to be optimized based on experimentally observed phase equilibria. While different approaches exist for thermodynamic optimizations, these approaches are often implemented in ways that are not compatible with machine learning workflows that requires differentiable calculation of loss function. In this work, we derive a phase equilibrium loss function based on thermodynamic potentials that can be efficiently evaluated and enable gradient based optimization by auto-differentiation in the PyTorch package. By minimizing this loss function, general thermodynamic model parameters can be optimized with respect to experimental phase equilibria data. Using thermodynamic models in the CALculation of PHAse Diagram (CALPHAD) framework, We illustrate successful and efficient optimization in different systems including ternary ones with more than 100 parameters. As the loss function is defined independently of the details of the thermodynamic models, it can be used to optimize machine learning thermodynamic models in general. In particular, we demonstrate a top-down optimization of atomistic potential from target phase equilibria.

cond-mat.mtrl-sci↗

arXiv2026

LLM-guided phase diagram construction through high-throughput experimentation

Constructing phase diagrams for multicomponent alloys requires extensive experimental measurements and is a time-consuming task. Here we investigate whether large language models (LLMs) can guide experimental planning for phase diagram construction. In our framework, a general-purpose LLM serves as the experimental planner, suggesting compositions for measurement at each cycle in a closed loop with high-throughput synthesis and X-ray diffraction phase identification. Using this framework, we experimentally constructed the ternary phase diagram of the Co-Al-Ge system at 900 degree C through iterative synthesis and characterization. We compared two strategies that differ in how the initial compositions are selected: one uses predictions from a domain-specific LLM trained on phase diagram data (aLLoyM), while the other relies solely on the general-purpose LLM. The two strategies exhibited complementary strengths. aLLoyM directed the initial measurements toward compositionally complex regions in the interior of the ternary diagram, enabling the earliest discovery of all three novel phases that form only in the ternary system. In contrast, the general-purpose LLM adopted a textbook-like approach which efficiently identified a larger number of phases in fewer cycles. In addition, a simulated benchmark comparing the LLM against conventional machine learning confirmed that the LLM achieves more efficient exploration. The results demonstrate that LLMs have high potential as experimental planners for phase diagram construction.

cond-mat.mtrl-sci↗

arXiv2025

aLLoyM: A large language model for alloy phase diagram prediction

Large Language Models (LLMs) are general-purpose tools with wide-ranging applications, including in materials science. In this work, we introduce aLLoyM, a fine-tuned LLM specifically trained on alloy compositions, temperatures, and their corresponding phase information. To develop aLLoyM, we curated question-and-answer (Q&A) pairs for binary and ternary phase diagrams using the open-source Computational Phase Diagram Database (CPDDB) and assessments based on CALPHAD (CALculation of PHAse Diagrams). We fine-tuned Mistral, an open-source pre-trained LLM, for two distinct Q&A formats: multiple-choice and short-answer. Benchmark evaluations demonstrate that fine-tuning substantially enhances performance on multiple-choice phase diagram questions. Moreover, the short-answer model of aLLoyM exhibits the ability to generate novel phase diagrams from its components alone, underscoring its potential to accelerate the discovery of previously unexplored materials systems. To promote further research and adoption, we have publicly released the short-answer fine-tuned version of aLLoyM, along with the complete benchmarking Q&A dataset, on Hugging Face.

cond-mat.mtrl-sci↗

arXiv2024

An Analysis of the Relationship Between the Characteristics of Innovative Consumers and the Degree of Serious Leisure in User Innovation

This study examines the relationship between the concept of serious leisure and user innovation. We adopted the characteristics of innovative consumers identified by Luthje (2004)-product use experience, information exchange, and new product adoption speed-to analyze their correlation with serious leisure engagement. The analysis utilized consumer behavior survey data from the "Marketing Analysis Contest 2023" sponsored by Nomura Research Institute, examining the relationship between innovative consumer characteristics and the degree of serious leisure (Serious Leisure Inventory and Measure: SLIM). Since the contest data did not directly measure innovative consumer characteristics or serious leisure engagement, we established alternative variables for quantitative analysis. The results showed that the SLIM alternative variable had positive correlations with diverse product experiences and early adoption of new products. However, no clear relationship was found with information exchange among consumers. These findings suggest that serious leisure practice may serve as a potential antecedent to user innovation. The leisure career perspective of the serious leisure concept may capture the motivations of user innovators that Okada and Nishikawa (2019) identified.

econ.EM↗

arXiv2024

Data-driven study of the enthalpy of mixing in the liquid phase

The enthalpy of mixing in the liquid phase is a thermodynamic property reflecting interactions between elements that is key to predict phase transformations. Widely used models exist to predict it, but they have never been systematically evaluated. To address this, we collect a large amount of enthalpy of mixing data in binary liquids from a review of about 1000 thermodynamic evaluations. This allows us to clarify the prediction accuracy of Miedema's model which is state-of-the-art. We show that more accurate predictions can be obtained from a machine learning model based on LightGBM, and we provide them in 2415 binary systems. The data we collect also allows us to evaluate another empirical model to predict the excess heat capacity that we apply to 2211 binary liquids. We then extend the data collection to ternary metallic liquids and find that, when mixing is exothermic, extrapolations from the binary systems by Muggianu's model systematically lead to slight overestimations of roughly 10% close to the equimolar composition. Therefore, our LightGBM model can provide reasonable estimates for ternary alloys and, by extension, for multicomponent alloys. Our findings extracted from rich datasets can be used to feed thermodynamic, empirical and machine learning models for material development.

cond-mat.mtrl-sci↗

arXiv2022

A machine learning-based classification approach for phase diagram prediction

Knowledge of phase diagrams is essential for material design as it helps in understanding microstructure evolution during processing. The determination of phase diagrams is thus one of the central tasks in materials science. When exploring new materials for which the phase diagram is unknown, experimentalists often try to determine the key experiments that should be performed by referencing known phase diagrams of similar systems. To enhance this practical strategy, we attempted to estimate unknown phase diagrams based on known phase diagrams using a machine learning-based classification approach. As a proof of concept, we focused on predicting the number of coexisting phases across the 800 K isothermal section of each of the 10 ternaries of the Al-Cu-Mg-Si-Zn system from the other 9 sections. To increase the prediction accuracy, we introduced new descriptors generated from the thermodynamic properties of the elements and CALPHAD extrapolations from lower-order systems. Using the random forest method, the presence of single-, two-, and three-phase domains was predicted with an average accuracy of 84% across all 10 considered sections with a standard deviation of 11%. The proposed approach represents a promising tool for assisting the investigator in developing new materials and determining phase equilibria efficiently.

cond-mat.mtrl-sci↗