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AI in materials

Cases where AI was actually used in materials research and scientific experiments.Cases where AI was actually used in materials research and scientific experiments.Cases where AI was actually used in materials research and scientific experiments.Cases where AI was actually used in materials research and scientific experiments.Cases where AI was actually used in materials research and scientific experiments.Cases where AI was actually used in materials research and scientific experiments.Cases where AI was actually used in materials research and scientific experiments.Cases where AI was actually used in materials research and scientific experiments.

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Materials AIAI editor

Case of predicting inorganic synthesis conditions from small data: applying machine learning to control the diameter of copper nanoparticles

A case study published in Chemistry of Materials showed that inorganic synthesis prediction is possible even with a small dataset. An approach was presented for predicting the synthesis conditions and results of copper nanoparticles that control the hydrodynamic diameter using machine learning, suggesting that ML can be effective for synthesis design even with limited experimental data. However, caution is needed when extrapolating from small data.

Published 2026.10.26 · 1 sources
Materials AIAI editor

Optical edge-enhanced DNN on a spin-multiplexed metasurface improves single-chip classification accuracy from 64.2% to 80.7%

A diffractive neural network combined with a spin-multiplexed nonlocal metasurface carried out optical edge detection and single-chip image classification in the field of physics applications. MNIST classification accuracy increased from 64.2% to 80.7%, and the polarization conversion efficiency was reported at about 55%. However, because these results are from a specific optical setup and dataset, generalization performance requires separate verification.

Published 2026.11.06 · 2 sources
Materials AIAI editor

Interpreting glass-forming rules across more than 78,000 compositions by common and system-specific analysis with SHAP-integrated XGBoost

A study published in npj Computational Materials analyzed glass-forming behavior by combining XGBoost classification with SHAP interpretation across 78,889 composition data points spanning conjugated, ionic, and metallic networks. The overall classification accuracy was 89.6%, and SHAP suggested a common frustration mechanism across different series and nonlinear critical behavior of compositional heterogeneity.

Published 2026.11.06 · 1 sources
Materials AIAI editor

Design Rules for Electrolyte Additives in Sodium Metal Batteries Derived by Interpretable Machine Learning

A JACS paper reported deriving design rules for electrolyte compositions and additives for sodium metal batteries using an interpretable machine-learning knowledge-discovery framework. The combination of lower oxygen content and larger nonpolar surface area favored anion-dominated solvation and NaF-rich SEI formation, and on this basis the study reported 800 cycles with an average CE of 99.9% and energy density exceeding 230 Wh kg⁻¹ in Ah-scale pouch cells.

Published 2026.11.06 · 2 sources
AI industryAI editor

Google DeepMind releases Genie 4, a world simulation model

Google DeepMind released Genie 4 on October 27. The company described it as a new generation of world simulation and stated that it is currently being distributed as a research preview to trusted testers.

Published 2026.10.27 · 1 sources
AI industryAI editor

OpenAI releases GPT-6 Cyber for vulnerability detection and patch suggestions

OpenAI unveiled GPT-6 Cyber on November 5. The model was introduced for cybersecurity use and focuses on helping detect vulnerabilities and suggest fixes. It shows a trend toward deeper AI use in security evaluation and response automation.

Published 2026.11.05 · 1 sources
Materials AIAI editor

Machine learning potential accelerates global structure search for planar boron clusters, proposing stable candidates B49, B70, and B84

A machine learning force field was used to accelerate global structure search for planar boron clusters, and particularly stable candidates were predicted according to stability criteria: B49, B70, and B84. It also reportedly reproduced DFT formation energy trends, suggesting potential to reduce the computational burden of atomic-scale exploration.

Published 2026.11.05 · 1 sources
Materials AIAI editor

Polymer property prediction combining geometry-based splitting and 3D structure shows better performance than p-SMILES alone

Multimodal learning that divides polymer structures into multiscale fragments based on geometry and then uses p-SMILES together with 3D structure showed improvements over p-SMILES alone in several property prediction tasks. The abstract states that the paper is scheduled for publication in ACS Omega, but it is currently at the arXiv submission stage.

Published 2026.11.05 · 1 sources
Materials AIAI editor

SteelAgent, which predicted steel properties, reported uncertainty, and performed inverse design with an LLM, also confirmed the limits of holdout generalization

SteelAgent is a physics-informed model with LLM orchestration attached that performed steel property prediction, uncertainty reporting, and inverse design. It showed strong performance under random splits, but performance and 90% confidence interval coverage dropped sharply under series and group holdout, revealing vulnerability to distribution shift in materials data.

Published 2026.11.03 · 1 sources
AI industryAI editor

OpenAI Announces New Policies and Community Commitments for Building Responsible AI Infrastructure

OpenAI released standards for responsible AI infrastructure around data center and compute campus development, and announced community support commitments alongside them. It shows that AI infrastructure expansion is linking not only to capacity growth, but also to policy, local acceptance, and the distribution of social benefits.

Published 2026.11.05 · 1 sources
Materials AIAI editor

Adaptive Machine Learning Discovers 9 Unknown Zeolite-Type Frameworks

A JACS paper reported that a high-throughput search based on adaptive machine learning examined mixed-ligand ZIFs and found 9 new structures. Six of them showed a bcv topology that had not previously been observed. The authors proposed ligand width and extension ratio as steric descriptors for topology selection.

Published 2026.11.04 · 2 sources
AI industryAI editor

Google DeepMind releases Genie 4, a world model for real-time interactive simulation

Google DeepMind released Genie 4 on 2026-10-27. The model can perform real-time manipulation by natural-language instructions, such as moving the camera, changing the weather, and introducing new characters, while also generating native audio. It has begun rollout first to limited testers and some partners.

Published 2026.10.27 · 1 sources
Materials AIAI editor

ML nonadiabatic molecular dynamics identifies the effect of carrier trapping and recombination of oxygen vacancies in SrTiO3

A study published in JACS on 2026-11-04 is confirmed to have clarified the key effect of the topology of oxygen vacancies in SrTiO3 on carrier trapping and recombination using machine-learning nonadiabatic molecular dynamics. It is an example useful for materials design that treats defect structure and charge dynamics together.

Published 2026.11.04 · 1 sources
AI industryAI editor

Google DeepMind unveils agentic cheminformatics with Google Antigravity

Google DeepMind announced ‘agentic cheminformatics’ on 2026-11-03 using Google Antigravity and new Cheminformatics Skills. It said it carried out tasks such as searching thousands of compounds, matched molecular pair analysis, and proposing new macrocycle within one hour in a drug-discovery workflow.

Published 2026.11.03 · 1 sources
Materials AIAI editor

Tracking temperature-dependent behavior of semiconductor defects with machine learning

Researchers at Chalmers University evaluated charge levels and stability of semiconductor defects over a temperature range close to room temperature with a machine-learning-based computational framework. By capturing defect-level shifts that are easy to miss under a 0 K assumption, they provided predictions closer to actual operating conditions.

Published 2026.10.29 · 1 sources