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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Accelerating Materials Discovery with MaterialsFramework and PhaseForge: Universal Machine Learning Potentials and Automated Phase Diagram Generation

Author(s)

Doguhan Sariturk, Siya Zhu, Raymundo Arróyave

On-Site Speaker (Planned)

Doguhan Sariturk

Abstract Scope

MaterialsFramework and PhaseForge are open-source platforms that streamline the deployment, benchmarking, and integration of advanced machine learning potentials in materials science. MaterialsFramework enables real-time, on-demand digital twin capabilities for DFT-accurate property prediction, supporting a wide range of ML models and modular analysis tools. PhaseForge complements this by providing robust, automated phase diagram generation and visualization, facilitating the exploration of phase stability across composition and temperature. Together, these frameworks offer computational efficiency and extensibility, making them ideal for iterative experimental workflows. This talk will demonstrate how MaterialsFramework and PhaseForge empower researchers to rapidly develop, validate, and apply universal ML potentials and phase diagram analysis, accelerating the discovery and optimization of advanced materials.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Modeling and Simulation, Machine Learning

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Computational Framework for the Intelligent Discovery of Selective Mineral Processing Reagents: Case Study of Phosphate Flotation

A design-focused machine learning framework for creep behavior in structural alloys

A Multi-objective Bayesian Optimization Framework for Predicting Grain-Boundary Segregation in High-Entropy Alloys

A PSO-ELM Based Prediction Model for Sulfur Content at the Endpoint of Hot Metal Pretreatment

A transfer learning approach for predicting fatigue behavior in additively manufactured metals

A “DFT-ML” Framework for Rational Design of Next-Generation Semiconductors

Accelerated Prediction of Metallic deformation by Crystal Plasticity informed Machine Learning Models: Application to Dual Phase Steels

Accelerating Materials Discovery with AI

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Accelerating Microstructural Solidification Simulations with Deep Neural Surrogates in Additive Manufacturing Conditions

AI-Driven Temperature Inference in XRD Patterns with Synthetic Pipelines and Molecular Dynamics Validation

An Agentic Framework for Extracting Structured Knowledge from Materials Science Literature

An explainable artificial intelligence strategy for materials design

Artificial Intelligence in Water and Wastewater Treatment: A Meta-analysis of Emerging Contaminants, Matrix Interference, and Real-time Quality Monitoring

Assessing Synthetic Accessibility of Inorganic Materials via Reaction Pathway Analysis

Autonomous Control of an X-ray Microscope: A Multi-Agent Framework for Closed-Loop Materials Characterization

Autonomous Exploration of Nanostructure Evolution in Solid-State Metal Dealloying via Laser-Induced Thermal Gradients and Multimodal Synchrotron X-rays

Bayesian Inference of Local SiC/SiC Thermal Conductivity with Curved Geometry

Bayesian Inference of Local, Temperature-Dependent, Anisotropic Thermal Conductivity from Modulated Photothermal Phase Data

Bayesian Optimization for Inverse Design of Pt-Au Films with Custom Friction Properties

Beyond Atom-Centric: A Transformer-Based Approach for Crystal Structure Prediction

Beyond Gaussian Noise: Symmetric and Asymmetric Likelihood Models for Robust Materials Optimization

Can Machine Learning Predict Complex Materials and Materials Science Problems?

ChatGPT Material Explorer: Design and Implementation of a Custom GPT Assistant for Materials Science Applications

Comparing universal machine learning interatomic potentials (uMLIP) in calculating selected properties in Co–Ni–Ru family alloys

Creating Benchmark Datasets for Electron-Microscopy Based Machine Learning Models

Data-driven and LLM-based design of hydrogen solubilities in metallic alloys

Data-Driven Prediction of High-Strain-Rate Behavior in Polyurea from Molecular Dynamics Simulations

Data-driven selection of lightweight manganite-based magnetocaloric materials for ambient temperature magnetic refrigeration

Data Informatics for Featurization and Discovery of Metal to Insulator Transition Materials

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

Deep Gaussian Process-based Cost-Aware Batch Bayesian optimization for Complex Materials Discovery Campaigns

Designing Novel High Entropy Alloys with High Strength and Ductility Using Machine Learning and Multi-Objective Optimization

Effects of void size and material properties on hydrostatic compression of a single crystal: A combined atomistic simulation and deep learning study

Efficient Exploration of Shape Memory Alloy Process-Composition Space Using Multi-Objective Bayesian Optimization

Elucidation of the Effects of Doping on the Bandgap of Bilayer Graphene using Interpretable Machine Learning

Enhancing Crystal Structure Prediction via Generative Initialization in Genetic Algorithms

Enhancing the Hybrid Learning Experience: Navigating Lecture Videos with AI

Evaluation of Out-of-Distribution Errors in Equivariant Graph Neural Networks for Materials Discovery

Extending first-principles simulations of electronic stopping across time and length scales via machine learning

Extracting Equations for Thermal Conductivity of Fuels Using Symbolic Regression

Foundations of Autonomy: AI-Ready Data Infrastructure for Labs and Digital Twins

From Atoms to Application: AI-Accelerated Materials Discovery in Constrained Design and Manufacturing Spaces

Harmonized Data Schema for AI Ready Materials R&D Data

High-Throughput Study of Amorphous Dielectric Materials: Integrating Foundation Potentials with Density Functional Theory

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Influence of Microstructure on Mechanical Properties of High Entropy Alloys: A Physics-Informed Machine learning approach

Interpretable and generative deep learning framework for predicting flow behavior and microstructure evolution in thermo-mechanical processing

Introducing XRDReader for Automated Extraction and Library Generation of X‑Ray Diffraction Data from Scientific Literature

KnowMat: Transforming Unstructured Material Science Literature into Structured Knowledge

Large Language Model-Driven FAIR Materials Database for AI-Augmented Scientific Inquiry

Large Language Model assisted Design of Superalloys via Olson-Flow Block Diagrams

Large Language Models for Materials Science: From Sparse Data Classification to Tensorial Property Prediction and Inverse Design

Leveraging Active Learning and LLMs for Efficient Cold Spray Meta-Analysis

Leveraging Large Language Models to Optimize Materials Synthesis and Design

Machine-Learned Spin-Lattice Dynamic Interatomic Potential for Iron-Manganese Alloys

Machine learning-driven exploration of temperature and pressure dependent phase transitions in ferroelectric Hafnium dioxide

Machine learning-improved X-CT detection of fatigue critical defects in AM Al alloys

Machine Learning Approach to Prediction of Mechanical Properties Considering Phase and Microstructure Morphology

Machine Learning for Automated Decoding of Crystals from XRD Patterns

Machine Learning Informed Hot Isostatic Pressing Parameters for beta Grain Refinement and Porosity Control in Laser Powder Bed Fusion of Ti-6Al-4V.

Machine Learning Model to Predict Mass Loss during the Catalytic Pyrolysis of Polyetherimide/Graphite Nanocomposites

Metallurgy-guided graph convolutional network for tensile property prediction of stainless steel with reduced data dependency

Micromechanical surrogate development with Crystal Plasticity Data

Modeling History Effects in Materials via a Recursive Neural Network

Modeling Stochastic Dynamics by Transforming Conditional Densities with Amortized Conditional Optimal Transport

Molecular Dynamics Study of Temperature-Dependent Creep and Stress Relaxation in DGEBA-AFD Vitrimers

Multimodal Spatial Encoding of Metal Microstructure and Plasticity for Mechanical Properties Prediction

Next-Generation Medical Oxygen Generator for Remote and Austere Environments

NFDI MatWerk Ontology: A Framework for FAIR Data Management in the Materials Science and Engineering

Physics-Constrained Convolutional Neural Networks with Gradient-Aware Optimization for Real-Time Thermal Prediction in PBF Metal AM

Physics-Informed Neural Networks for Parameter Quantification in Materials Science

Posing Inverse Design of Mg-Alloy Microstructure and Texture as a Physics Guided Latent Diffusion-Optimization Problem

Predicting Dislocation Density Evolution via Machine Learned Symbolic Regression

Predicting Microstructural Outcomes in Heat-Treated Steels Using Machine Learning

Predicting the High-Temperature Oxidation Response of Nickel Superalloys Using CALPHAD-Enhanced Machine Learning

Prediction of Formation Energies of XYB₁₄ Boron-Rich Borides By Machine Learning

Prediction of Metal-Insulator-Transition (MIT) Compounds from Density of States (DOS) Data Using Image-Based Deep Learning

Quantum-Enhanced Machine Learning for HEA Discovery

Quantum Computers and Hybrid Machine Learning Models for the Discovery of Lightweight Structural Alloys

Reconstructing Modal Shapes in Resonant Ultrasound Spectroscopy from Sparse Spatial Data via Attention-Based Machine Learning

Reconstruction of Polycrystalline Atomic Structure using Diffusion Model

Reinforcement Learning Recommendations for Crystallization-Resistant Nuclear Waste Glass Formulation

Simultaneous Microstructure and Composition-based Machine Learning for Hardness Distribution Prediction & Verification in an Aluminum Alloy

Spin-lattice Dynamics Study of Grain Boundary and Phase Interface Migration in Pure Iron

Streamlining Bayesian Optimization in Materials Science via a Retrieval-Augmented LLM Assistant Integrated with Honegumi

Text-to-Crystal Structure Generation Enabled by Fine-Tuned Large Language Model

Towards FAIR-er crystallographic texture data to enable machine learning approaches

Transformer-Based Prediction of Mechanical Properties in CoCrCuFeNi High-Entropy Alloys

Universal machine learning interatomic potentials for large-scale molecular dynamics: Phase transitions in Barium Titanate and melting behavior in High-Entropy Ceramics

Using 2D Data and Diffusion Model Principles to Generate 3D Microstructures of TRISO Fuel Compacts

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