Materials Informatics– category –
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Bethe-Salpeter Equation: arXiv’s 2026 quantum simulation method
arXiv USA Overview A new arXiv paper details a quasiparticle quantum simulation method for multi-excitonic states within the Bethe-Salpeter equation, significantly streamlining material modeling on quantum computers. This approach reduce... -
Quantinuum: 2026 NMR simulation specs for battery materials
Quantinuum USA Overview Quantinuum has successfully performed the most accurate large-scale digital NMR simulation on quantum hardware to date, utilizing its System Model H2 quantum computer. This achievement accurately reproduces key sp... -
Nippon Steel & Fujitsu: How trace carbon strengthens iron bonds
npj Computational Materials / AZoM.com Japan Overview Joint research by Nippon Steel and Fujitsu Research has elucidated how trace carbon enhances crack-growth resistance in alpha-iron grain boundaries. Utilizing ML-driven atomistic simu... -
ML Interatomic Potentials: Chinese team’s 2026 accuracy roadmap
Zhongguancun Academy, Zhongguancun Institute of Artificial Intelligence China Overview A Chinese research team has successfully identified the origins of errors in universal machine learning force fields (uMLFFs) when applied to multicom... -
GNoME explained: Google DeepMind’s 2026 crystal discovery specs
Mewburn Ellis UK Overview Google DeepMind's GNoME project is spearheading a paradigm shift to inverse design in materials informatics, using machine learning to identify millions of novel candidate crystal structures. This represents a f... -
Materials Informatics Weekly Report October 4, 2026
▼ ▼ ▼ If the infographic makes you want to read the full report, please click the download button below. ▼ ▼ ▼ 📄 Weekly Report October 04, 2026 (PDF) — Download Weekly Report October 04, 2026 (PDF) — DownloadDownload 🎙 Podcast October 0... -
Genesis chip: UT San Antonio neuromorphic continuous learning
UT San Antonio Today USA Overview The MATRIX AI consortium at UT San Antonio has unveiled the "Genesis" spike neuromorphic accelerator chip, which solves the long-standing AI problem of catastrophic forgetting, enabling continuous on-dev... -
AI Scientist: Autonomous yeast growth compound design and specs
ZME Science Sweden Overview Researchers at Chalmers University of Technology have developed an AI system capable of autonomously generating scientific hypotheses, designing and executing experiments with automated lab equipment, and inte... -
Li–P–S crystals: AI strategy identifies 10,000 stable candidates
arXiv USA Overview Researchers have developed an iterative strategy combining crystal-structure generative models with machine-learned force fields (MLFFs), successfully producing approximately 70,000 candidate structures for the Li–P–S ... -
ProcGNN explained: PyTorch physics-informed GNN for simulation
ProcGNN South Korea Overview A new paper, "ProcGNN," introduces a PyTorch-based physics-informed graph neural network for stream-level chemical process surrogate modeling. ProcGNN uses shared unit and stream representations to predict te...
