Results 181 to 190 of about 12,284 (247)

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

The Modification of Nitrogen to Modulate Perovskite for the Application of p-Type Transparent Conductive Oxides. [PDF]

Molecules
Liang Y, Li K, Chen H, Wang Y, Zheng S, Bai L.   +5 more
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

Band Unfolding in Finite Nanostructures: Visualizing Dirac, Spin-Valley, and Rashba Features. [PDF]

Nano Lett
Yamaguchi N, Yunitasari S, Amalia W, Lee CC, Ozaki T, Ishii F.   +5 more
europepmc   +1 more source

Correction for Wurman et al., Supercell tornadoes are much stronger and wider than damage-based ratings indicate. [PDF]

Proc Natl Acad Sci U S A
europepmc   +1 more source

Reversible tunable topological phononic crystals based upon thermo-acoustic hydrogels. [PDF]

Microsyst Nanoeng
Lian M, Liu Z, Zhang J, Duan L, Su Y, Cao T.   +5 more
europepmc   +1 more source

Formation of Fluorine Vacancy (FV) Centers in Diamond. [PDF]

Materials (Basel)
Puthirath AB, Elkins J, Kannan H, Horne A, Chen JS, Zhang H, Khabashesku VN, Biswas A, Zhang X, Birdwell AG, Ivanov TG, Kentsch U, Akhmadaliev S, Vajtai R, Ma X, Mohite AD, Pati R, Ajayan PM.   +17 more
europepmc   +1 more source

Combined Gravimetric, Electrochemical, and Computational Research on Benzothiophene Derivatives as Effective Corrosion Inhibitors for Mild Steel

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT Four benzothiophenes, thianaphthene (BT‐1), 3‐bromothianaphthene (BT‐2), benzo[b]thiophene‐2‐carboxaldehyde (BT‐3), and benzo[b]thiophene‐2‐carbonitrile (BT‐4), were tested as mild steel corrosion inhibitors in 1‐M HCl. The compounds have the similar benzothiophene core but differ in the substituents bound to the ring.
Nhlanhla G. Chirwa, Motsie Elija Mashuga, Mohammad Salman, Indra Bahadur   +3 more
wiley   +1 more source

Revisiting the Configurations of Hydrogen Impurities in SrTiO₃: Insights from First-Principles Local Vibrational Mode Calculations. [PDF]

ACS Omega
Cai Z, Ma C.
europepmc   +1 more source