Tootfinder

Andrea Velez, a US Citizen & CalPoly Pomona grad, was in Los Angeles when she was forced to the ground, handcuffed & 'disappeared' by ICE.
ICE is utterly lawless.
▶️ U.S. citizen detained by ICE agents in downtown Los Angeles speaks out - KTLA
https://youtube.com/watch?v=IfVq6qfE…

I did a tiny thing: http://webcam.calpenedes.com/
#webcam #montserrat

Gifford Fire - CALPF
Size: 96106 Acres
Contained: 15%
https://share.watchduty.org/i/58187?ts=1754581059000

Watch Duty - Wildfire Maps & Alerts
Real-time information about wildfire and firefighting efforts nearby

Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy
Siya Zhu, Doguhan Sariturk, Raymundo Arroyave
https://arxiv.org/abs/2506.16771

Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy
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 calculations significantly important. However, phase diagram calculations with conventional CALPHAD assessments based on experimental or ab-initio data can be expensive. With the emergence of machine-learning interatomic potentials (MLIPs), we have developed a program …

AutoMAT: A Hierarchical Framework for Autonomous Alloy Discovery
Penghui Yang, Chendong Zhao, Bijun Tang, Zhonghan Zhang, Xinrun Wang, Yanchen Deng, Yuhao Lu, Cuntai Guan, Zheng Liu, Bo An
https://arxiv.org/abs/2507.16005

AutoMAT: A Hierarchical Framework for Autonomous Alloy Discovery
Alloy discovery is central to advancing modern industry but remains hindered by the vastness of compositional design space and the costly validation. Here, we present AutoMAT, a hierarchical and autonomous framework grounded in and validated by experiments, which integrates large language models, automated CALPHAD-based simulations, and AI-driven search to accelerate alloy design. Spanning the entire pipeline from ideation to validation, AutoMAT achieves high efficiency, accuracy, and interpret…

Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt-W System
Courtney Kunselman, Siya Zhu, Doguhan Sariturk, Raymundo Arroyave
https://arxiv.org/abs/2508.01028

Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt-W System
This work introduces PhaseForgePlus -- a computationally efficient, fully open-source workflow for physically-informed CALPHAD model generation and parameter fitting. Using the Pt-W system as an example, we show that the integration of Machine Learning Potentials into the Alloy Theoretic Automated Toolkit can produce physically grounded Gibbs energy descriptions requiring only slight adjustments to produce accurate phase diagrams. Employing the Jansson derivative method in the context of experi…

aLLoyM: A large language model for alloy phase diagram prediction
Yuna Oikawa, Guillaume Deffrennes, Taichi Abe, Ryo Tamura, Koji Tsuda
https://arxiv.org/abs/2507.22558 https://…

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 Diagr…