Biomass waste is a valuable renewable energy resource, but its efficient conversion is hindered by feedstock variability, complex non-linear reactions, and the limited adaptability of conventional control systems. This chapter examines how machine learning improves biomass conversion through intelligent process monitor...
P. V. Gopirajan, G. Venkatesan, C. Santhosh Kumar· Advances in Waste Biomass Co...· 0 citations
Real-time artificial intelligence (AI) digital twins for heavy power equipment require high-fidelity forward simulation for anomaly detection, yet traditional solvers suffer from prohibitive latencies. To bridge this gap, we present a physics-embedded deep learning framework achieving an approximate 970-fold accelerati...
Yong-Hao Zhang, Fu-Qiang Ren, Hong-Shun Liu et al.· Engineering applications of...· 0 citations
EdenGNN (Equivariant Density Graph Neural Network) is an open framework for accurate and efficient charge density prediction. Integrated with DFT softwares, it can predict electronic structures directly from atomic configurations.
Xiwen Li· Zenodo (CERN European Organi...· 0 citations
In this paper, transformer-based graph neural networks are applied across different data types, such as text, images, and metadata, to automatically infer the structure of social networks from publicly available OSINT data. A pipeline was built that first processes text with BERT and images with ResNet, then combines t...
Azat Abutalifov, Anel Aidos, Assel Mukasheva et al.· EEPES 2026· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
An end-to-end machine learning workflow for the development of potential energy surfaces at the DLPNO-CCSD(T)/aug-cc-pVTZ level is described and applied to hydroxide-water complexes with two to five water molecules. This workflow utilizes a two-part machine learning process, achieving both accuracy and efficiency by co...
Greta M. Jacobson, Chenrui Shao, Tanya I. Bakalov et al.· ChemRxiv· 0 citations
Rational therapeutic target discovery and systems biology face a decisive epistemological challenge: the fragmentation between the kinetic dynamics of intracellular pathways and the spatial-geometric complexity of macromolecular interactions. This work outlines an integrated analytical architecture that overcomes this...
Enrico Catalano· Zenodo (CERN European Organi...· 0 citations
Identifying crystalline materials with intrinsically low lattice thermal conductivity (κL) remains a major challenge in thermoelectric design due to complex phonon transport mechanisms and the limited availability of high-quality datasets. Although first-principles methods provide accurate κL predictions, their high co...
The integration of artificial intelligence (AI) and deep learning with the biomedical research marks a critical inflection point, shifting the paradigm from addressing symptoms to predicting and acting at the molecular level. In many diagnostic models, the chain reaction triggered by a chronic infection is often overlo...
Rohit Shamsundar Tawade, Abhijeet P. Goad, Maryston Gregory Sequeira, Dr. Anupam Kumar· Zenodo (CERN European Organi...· 0 citations
The integration of artificial intelligence (AI) and deep learning with the biomedical research marks a critical inflection point, shifting the paradigm from addressing symptoms to predicting and acting at the molecular level. In many diagnostic models, the chain reaction triggered by a chronic infection is often overlo...
Rohit Shamsundar Tawade, Abhijeet P. Goad, Maryston Gregory Sequeira, Dr. Anupam Kumar· Zenodo (CERN European Organi...· 0 citations
This study advances machine learned density functional development from molecule-specific to geometry-specific modeling of conformational effects in electronic structures by introducing GNN-ωPBE, a second machine learned RSH functional that generates geometry-specific ω values directly from atomic numbers and three-dim...
Xi Cheng, Si-Qi Chen, Xian-Qi Deng et al.· ACS Organic & Inorganic...· 0 citations
A graph-aware and sequence-aware deep learning framework that combines a convolutional neural network backbone for spatial feature extraction, a Graph Attention Network for lesion-structure modelling, and a Bidirectional Long Short-Term Memory (BiLSTM) module for ordered-view or slice-sequence representation learning i...
Chetanpal Singh, Santoso Wibowo, S. Grandhi et al.· Journal of Imaging· 0 citations
The integration of artificial intelligence (AI) and deep learning with the biomedical research marks a critical inflection point, shifting the paradigm from addressing symptoms to predicting and acting at the molecular level. In many diagnostic models, the chain reaction triggered by a chronic infection is often overlo...
Rohit Shamsundar Tawade, Abhijeet P. Goad, Maryston Gregory Sequeira, Dr. Anupam Kumar· Zenodo (CERN European Organi...· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduJul 15, 2026
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
