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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

Predicting electron density and energy together with an orbital-free surrogate functional reduces charge-density error by one order of magnitude versus conventional OF-DFT

A machine-learning-based orbital-free surrogate functional was trained to predict electron density and energy simultaneously. The charge-density error was reduced by one order of magnitude compared with conventional OF-DFT, and energy prediction was competitive with state-of-the-art MLIP while showing better generalization to larger unseen systems. This may be useful for jointly handling electronic structure and potential prediction.

Published 2026.09.18 · 1 sources
Materials AIAI editor

Machine learning for inverse estimation of magnetic interactions from powder neutron diffraction data reports about 2% accuracy on high-symmetry lattices

Researchers estimated magnetic interaction parameters with machine learning from magnetic diffuse-scattering data in powder neutron diffraction. They reported about 2% accuracy across 8 high-symmetry lattices and said they avoided the false minima that often arise in nonlinear least-squares optimization. This may be useful as a reference for neutron-scattering-based magnetic modeling and inverse problems.

Published 2026.09.18 · 1 sources
AI trendsAI editor

Memristive SVD Applied to LLM Weight Decomposition and Fine-Tuning Initialization Improves Performance and Efficiency

A Nature Communications paper implemented memristive singular value decomposition(MSVD) and a correction method, SREA, in memristor-based compute-in-memory, and applied them to image enhancement, mechanics data reconstruction, user-extendable recognition, and LLM weight decomposition. In LLaMA 3.2-3B, performance improved on GSM8K and MATH, and energy efficiency and speed also improved.

Published 2026.09.20 · 1 sources
Materials AIAI editor

ML Prediction of Mobility and Polarity Type in Polymer Semiconductors

A paper published in JACS on 2026-09-18 reported that carrier mobility and polarity type in polymer semiconductors were predicted with a machine learning model trained on high-precision data, and that this was linked to experimental realization. The title and metadata confirm actual ML use.

Published 2026.09.18 · 2 sources
Materials AIAI editor

Unsupervised Deep Learning Applied to Limited-Angle STEM-EDX Tomography

npj Computational Materials published an unsupervised deep learning study for limited-angle STEM-EDX tomography on 2026-09-18. The study presented a reconstruction and interpretation framework applied to 3D chemical analysis of phase-change memory devices.

Published 2026.09.18 · 1 sources
Materials AIAI editor

A Quantum-Inspired Inverse-Design Framework for Searching HER Catalysts

npj Computational Materials published a paper on 2026-09-18 that addressed the inverse design of heterogeneous catalysts for the hydrogen evolution reaction (HER) using a quantum-inspired generative framework. The authors proposed an approach that preserves latent-space structure while improving exploration of unknown chemical space through a generative model including an orthogonal transformation inspired by VQC.

Published 2026.09.18 · 1 sources
Materials AIAI editor

NEBscape, a workflow that automates surface reaction barrier calculations

A workflow was proposed that automatically generates and combines the initial and final states of surface reactions to perform transition-state searches, and when MLIP was used as a calculator example, it showed lower barriers and higher consistency in OC20NEB.

Published 2026.09.04 · 1 sources
Materials AIAI editor

Large-scale expansion of the Cu catalyst CO2 hydrogenation pathway through ML reaction-network exploration

A reaction-network for CO2 hydrogenation on Cu catalysts was expanded using ML activation-energy prediction based on reaction/reagent fingerprints and an exploration algorithm, constructing a microkinetic model containing 9,389 elementary reaction. This approach predicted CO2 conversion up to 40 times higher than a manually screened 152 reaction model, and highlighted the importance of methanol and CO formation pathways and intermolecular hydrogen-hopping.

Published 2026.09.17 · 2 sources
Materials AIAI editor

Framework Proposed for Finding Nitrate→Ammonia Electrocatalyst Candidates through Multimodal Semantic Learning

Researchers at Peking University reported a data-driven search for nitrate-to-ammonia electrocatalysts using a multimodal semantic learning framework and MatSemNet that handles literature, experimental, and structural information together. As an example of applying AI to real catalyst discovery, the approach is notable for linking materials knowledge with experimental signals to find candidates.

Published 2026.09.15 · 1 sources
Materials AIAI editor

Predicting Vacancy Formation Energy in Ni-based Superalloys with Machine Learning

A study published in npj Computational Materials combined density functional theory calculations with machine learning to predict the vacancy formation energy of Ni-based superalloys. It reported that Cr, Nb, Ta, Al, and Ti substitution broadened the energy distribution and increased the standard deviation by up to 0.15 eV.

Published 2026.09.15 · 1 sources
Materials AIAI editor

Collaborative agent ‘MatBrain’ for autonomous crystal materials research unveiled, reports generation of 30,000 candidate structures within 48 hours

According to a report published in Nature Machine Intelligence on 2026-09-10, a multi-agent AI combining two lightweight cooperative models carried out idea generation, workflow design and execution, result evaluation and refinement, and candidate proposal in crystal materials discovery. In a catalyst design case, it reported generating 30,000 candidate structures and screening 38 promising materials within 48 hours.

Published 2026.09.10 · 1 sources
Materials AIAI editor

Deriving CO2 Methanation Catalyst Design Rules by Learning Activity Descriptors Directly from Measured Features

Using interpretable machine learning based on CatBoost, 4026 catalyst experimental data points were analyzed to learn activity descriptors directly from measured features and explain the relationship between composition, conditions, and activity. The study proposed data-driven design rules by presenting general feature interpretation and temperature response patterns for each active metal, which can be used for catalyst design.

Published 2026.09.15 · 1 sources
Materials AIAI editor

A machine learning approach for selecting substrate scope that balances performance and similarity

A preprint dated 2026-09-14 proposed ScopeBO, which considers performance together with substrate similarity and diversity when choosing a reaction substrate scope. Better performance than alternative methods was reported across several reaction datasets, but because it is still at the preprint stage, further validation and reproducibility in real synthetic contexts are needed.

Published 2026.09.14 · 2 sources