Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

We are excited to share our first big milestone in solving a grand challenge that has hampered the predictive power of computational chemistry, biochemistry, and materials science for decades. By ...

Conclusions By applying deep-learning techniques to a novel data set, we trained a number of models capable of accurately predicting the nonlinear olfactory qualities of aroma-chemical blends while ...

Therefore, it can be concluded that conducting Arduino experiments in a mixed virtual-physical environment results in equivalent learning gains in declarative knowledge as those attained by using VL ...

Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides The following represents disclosure information provided by authors of ...

Deep learning (DL), a branch of machine learning (ML), is the core technology in today’s technological advancements and innovations. Deep learning-based approaches are the state-of-the-art methods ...

Property prediction for materials under realistic conditions has been a long-standing challenge within the digital transformation of materials design. MatterSim investigates atomic interactions from ...

Therefore, this paper: 1) analyzes the collaborative elements within the end-edge-cloud computing system for deep learning, and proposes collaborative training, inference, and updating methods and ...

Single-step retrosynthesis in organic chemistry increasingly benefits from deep learning (DL) techniques in computer-aided synthesis design. While template-free DL models are flexible and promising ...

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