Materials Informatics– category –
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Materials Informatics Weekly Report October 31, 2026
▼ ▼ ▼ If the infographic makes you want to read the full report, please click the download button below. ▼ ▼ ▼ 📄 Weekly Report October 31, 2026 (PDF) — Download Weekly Report October 31, 2026 (PDF) — DownloadDownload 🎙 Podcast October 3... -
Lithium Metal Batteries: AI-Driven Discovery of 368 Non-Fluorinated Diluents
AI타임스 South Korea Overview Researchers at Seoul National University developed an AI algorithm that screened 1.32 million molecules to identify 368 promising non-fluorinated electrolyte diluents for lithium metal batteries. Three of th... -
qNEP Potential: Sodium Battery SEI Formation and Simulation Specs
BIOENGINEER.ORG Overview Researchers have developed a charge-aware neuroevolution potential (qNEP) that extends simulation timescales for sodium battery interfaces to 100 nanoseconds. This machine-learning approach overcomes previous bot... -
OrgAIcat: ML model for asymmetric organocatalysis selectivity
BIOENGINEER.ORG China Overview Researchers have developed OrgAIcat, a machine learning framework that predicts enantioselectivity in asymmetric organocatalysis using a manually compiled dataset of 22,554 data points. The model achieves R... -
Open Polymer Datasets: Reuse Quality Audit across 8 Repositories (2020–2025)
BIOENGINEER.ORG Overview A cross-repository audit of 53,038 open polymer datasets reveals that the choice of repository is the primary determinant of data reusability, outweighing science or publication year. While deposit volumes grew 1... -
Graph Neural Networks: Accelerating Electrocatalyst Design for 2026
BIOENGINEER.ORG Overview Graph neural networks (GNNs) are replacing traditional trial-and-error and DFT screening to accelerate the discovery of high-performance electrocatalysts for green hydrogen production. By learning material proper... -
CLaC Model: MIT’s AI for Crystal Structure and Text Retrieval
Intelligent Living USA Overview MIT researchers developed CLaC, a vision-language model that aligns 3D crystal structures with natural language descriptions using contrastive learning. By utilizing synthetic narratives to overcome data b... -
MARVEL-AI: A*STAR’s AI-Driven Materials Discovery Framework
A*STAR Singapore Overview A*STAR has launched MARVEL-AI to accelerate the transition of advanced materials from discovery to industrial deployment using an AI-enabled closed-loop system. The initiative demonstrates significant throughput... -
Epoxy Degradation Analysis: PCA-Based Spectral Fingerprinting
BIOENGINEER Overview Researchers used principal component analysis (PCA) to link the spectral fingerprints of degrading amine-cured epoxy adhesives to the loss of mechanical strength. The study reveals that degradation behavior varies si... -
NVIDIA Tensor Cores: Speedups for ML Interatomic Potentials
Blockchain News USA Overview NVIDIA's Tensor Core FP32 methods, specifically BF16x9 for Blackwell and TF32x3 for Ampere, accelerate machine learning interatomic potentials without requiring model retraining. In tests using the Point Edge...
