Results 221 to 230 of about 28,903 (292)

Decoupling Size from Shape: Cellular Sheaf Laplacians as Ligand Geometry Descriptors for Binding Affinity Prediction. [PDF]

Int J Mol Sci
Akgüller Ö, Balcı MA, Cioca G.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Full Asymmetric Radiation Control Through Multi-Channel Bound States in the Continuum. [PDF]

Nanophotonics
Wang B, Bao Y.
europepmc   +1 more source

Topological Properties of International Commodity Market: How Uncertainty Affects the Linkages?

Agribusiness, EarlyView.
ABSTRACT The study aims to explore the network topology of the international commodity market by examining the interconnections among 21 commodity futures across various categories, including energy, precious and industrial metals, and agriculture. We analyze the market structure of these commodity futures under both low and high uncertainty conditions
Ibrahim Yagli, Bayram Deviren
wiley   +1 more source

Chaotic and Stochastic Components in an Influenza Surveillance Series: Nonlinear Dynamics and Predictive Modeling Study. [PDF]

JMIRx Med
Goncalves CPDS, Rouco C.
europepmc   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

AIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim, Silabrata Pahari, Joseph Sang‐Il Kwon   +2 more
wiley   +1 more source

The two dragons of cognition: recursive condensation for predictive processing. [PDF]

Front Comput Neurosci
Li X.
europepmc   +1 more source

Automated generative process synthesis via transformer‐based dual‐loop simulation and optimization

AIChE Journal, EarlyView.
Abstract This study presents a novel framework for automated generative process synthesis, addressing the complexity of simultaneously optimizing discrete topologies and continuous operating variables. To overcome conventional superstructure limitations, we propose a dual‐loop architecture integrating generative transformers with rigorous process ...
Yeong Woo Son, Chan Kim, Ji Hun Pak, Hain Lee, Jong Min Lee   +4 more
wiley   +1 more source

Emergent altermagnetism and topological response in Janus MnPSX monolayers. [PDF]

Sci Rep
Guerrero-Sanchez J, Ponce-Perez R, Hoat DM, Gonzalez-Hernandez R.   +3 more
europepmc   +1 more source

GraphRAG for engineering diagrams: ChatP&ID enables LLM interaction with P&IDs

AIChE Journal, EarlyView.
Abstract Piping and Instrumentation Diagrams (P&IDs) are central to process engineering workflows, yet extracting information from them remains a tedious and time‐consuming task. This work introduces ChatP&ID, a framework enabling natural‐language interaction with smart P&IDs through Graph Retrieval‐Augmented Generation (GraphRAG), to our knowledge ...
Achmad Anggawirya Alimin, Artur M. Schweidtmann   +1 more
wiley   +1 more source