Results 131 to 140 of about 71,384 (267)

Fine-Tuning Unifies Foundational Machine-Learned Interatomic Potential Architectures at <i>ab initio</i> Accuracy. [PDF]

J Phys Chem Lett
Hänseroth J, Flötotto A, Qaisrani MN, Dreßler C.   +3 more
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Extended Fused Carbazole‐BODIPY, High Brightness NIR Organic Dyes

Angewandte Chemie, EarlyView.
Highly fused boron‐dipyrromethene (BODIPY) with a strong donor group has been synthesized, and their photophysical behaviors fully characterized. They exhibit outstanding brightness for fluorophores emitting in the NIR region. Water‐soluble nanoparticles filled with one of these dyes demonstrate high efficiency as the NIR II visualization agent ...
Fabien Ceugniet, Valentin Ott, Pascal Retailleau, Yann Bretonniere, Olivier Maury, Manon H. E. Bousquet, Lucie Sancey, Nicolas Leclerc, Denis Jacquemin, Gilles Ulrich   +9 more
wiley   +2 more sources

Automated Discovery of Algorithms for Molecular Electronic Structure Calculations Using Physics-Informed Program Synthesis. [PDF]

J Am Chem Soc
Acheson K, Turanyi R, Habershon S.
europepmc   +1 more source

Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning

Advanced Intelligent Systems, EarlyView.
Illustration of text data mining of rare earth mineral thermodynamic parameters with the large language model‐powered LMExt. A dataset is built with mined thermodynamic properties. Subsequently, a machine learning model is trained to predict formation enthalpy from the dataset.
Juejing Liu, Haydn Anderson, Noah I. Waxman, Vsevolod Kovalev, Byron Fisher, Elizabeth Li, Xiaofeng Guo   +6 more
wiley   +1 more source

Navigating Nitration Chemistry: A Practical Guide to Reagents, Mechanisms, and Selectivity

Angewandte Chemie, EarlyView.
This review highlights key contributions of modern nitration chemistry, emphasizing sustainable mechanistic platforms and comparing the performance of both organic and inorganic reagents across aromatic, ipso‐, olefin, alkyne, and heteroatom nitration. It provides the community with a clearer, unified perspective on current advances and facilitates the
Harry Lecomte, Anthony J. Fernandes, Dmitry Katayev   +2 more
wiley   +2 more sources

Computational chemistry methods based on MNDO and tools for improving their accuracy. [PDF]

J Mol Model
Stewart JJP.
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

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

Lennard-Jones Parameter Fitting for Gold/Water Interaction Based on Structural Analysis: A QM, MM, and QM/MM Study. [PDF]

Nanomaterials (Basel)
I Blazquez PB, Pedron FN, Torres AA, de Moraes EE, Cole I, Martins EF.   +5 more
europepmc   +1 more source