Materials Informatics– category –
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arXiv Publishes Review on Generative Models, Multimodal Learning, and Closed-Loop Workflows in Inverse Materials Design
arXiv Unknown Overview A new review paper published on arXiv outlines advancements in generative models, multimodal learning, and closed-loop workflows for inverse materials design. The study highlights a shift in materials science from ... -
ACS Publications: Generative Multi-Property Optimization Accelerates Polymer Chemistry Design
ACS Publications USA Overview A study published in ACS Publications proposes a generative multi-property optimization method for polymer chemistry, offering a robust pathway to accelerate early-stage materials discovery. This technique l... -
TU Delft and ETH Zurich Develop ChatGPT-Like AI “DiffuMeta” for Inverse Design of Complex Metamaterials
ESEF Maakindustrie Netherlands Overview Researchers from TU Delft and ETH Zurich have developed "DiffuMeta," a ChatGPT-inspired AI model, successfully designing lightweight and strong metamaterials. DiffuMeta represents material shapes a... -
ASM International Highlights Accelerated Computational Materials Design Integrating CALPHAD, DFT, MLIPs, and AI Agents
ASM International USA Overview An ASM International webinar showcased recent advancements in accelerating computational materials design by integrating CALPHAD, Density Functional Theory (DFT), Machine Learning Interatomic Potentials (ML... -
Orbital Industries Secures $50M Series B Funding for AI Materials Platform
Fundraise Insider UK/USA Overview Orbital Industries, a startup designing advanced materials with AI, has raised $50 million in Series B funding. The company aims to accelerate physical technology development by integrating materials dis... -
arXiv: BiMat-ML Advances Stacked 2D Material Property Prediction via Multimodal Learning and GNNs
arXiv Unknown Overview A new research paper on arXiv proposes "BiMat-ML," a multimodal learning approach for property prediction in stacked two-dimensional (2D) materials. This method utilizes graph neural networks (GNNs) to process mole... -
AI and Graph Neural Networks Drive a Materials Revolution: Gulf University on Property Prediction
Gulf University バーレーン Overview Gulf University research highlights AI's profound impact on materials science, particularly through Graph Neural Networks (GNNs). These models predict material properties directly from atomic structure... -
Citrine Platform and AI Significantly Reduce Graphite Anode Failure Rate via Iterative Experimental Feedback
arXiv Unknown Overview This research demonstrates that the Citrine Platform, utilizing an AI-guided iterative closed-loop workflow, successfully achieved the design and optimization of graphite-based anode formulations, leading to a sign... -
AI and Simulation Transform Materials Design in Europe, Supercharging Green and Digital Transitions
MaX ヨーロッパ Overview Europe is fundamentally reshaping its approach to materials design by integrating simulation and artificial intelligence, significantly bolstering its competitiveness in green and digital transitions. Generative A... -
U.S. Department of Energy and Citrine Informatics Partner to Accelerate New Materials Discovery with AI
Department of Energy USA Overview The U.S. Department of Energy's (DOE) SLAC National Accelerator Laboratory has formed a new public-private partnership with AI-driven materials informatics company Citrine Informatics to explore the futu...
