Results 201 to 210 of about 7,011 (294)

Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models

Angewandte Chemie, EarlyView.
AI‐empowered catalysis research via integrated database platform, universal machine learning interatomic potentials (MLIPs), and large language models (LLMs). ABSTRACT The integration of artificial intelligence (AI) into catalysis is fundamentally reshaping the research paradigm of catalyst discovery.
Di Zhang, Yuanzheng Chen, Chuanyu Liu, Yan Liu, Hongliang Xin, Jiayu Peng, Pengfei Ou, Hao Li   +7 more
wiley   +2 more sources

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Unveiling GaN Prismatic Edge Dislocations at the Atomic Scale via P-N Theory Combined with DFT. [PDF]

Materials (Basel)
Peng L, Huang L, Chen S, Huang C, Wang R, Li M.   +5 more
europepmc   +1 more source

Pseudo-Global Warming Simulations Reveal Enhanced Supercell Intensity and Hail Growth in a Future Central European Climate [PDF]

Lisanne E. Lucas, Christian Barthlott, Corinna Hoose, Peter Knippertz   +3 more
openalex   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Supercell-enhanced multimodal plasmonic sensor for high-fidelity antigen detection via refractive index modulation. [PDF]

Sci Rep
Moghtader MS, Heidarzadeh H, Khodaie A.
europepmc   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Disorder-Mediated Ionic Conductivity in Irreducible Solid Electrolytes. [PDF]

J Am Chem Soc
Landgraf V, Tu M, Zhao W, Lavrinenko AK, Cheng Z, Canals J, de Leeuw J, Ganapathy S, Vasileiadis A, Wagemaker M, Famprikis T.   +10 more
europepmc   +1 more source

A Case of Supercell Intensification along a Preexisting Boundary – Clay County Nebraska Tornado of 22 September 2001

, 2002
Jared Guyer
openalex   +1 more source

Stability and Induced Magnetism by Edge Modification of HfS₂ Nanoribbons. [PDF]

Langmuir
Pimenta BGA, da Conceição Vasconcelos R, de Oliveira Neto PH, F de Menezes R, Sprenger KG, Gargano R.   +5 more
europepmc   +1 more source