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Nature Machine Intelligence
Scopus
ISSN 2522-5839 · Nature Portfolio · 43 articles in the index
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Leveraging large language models for predictive chemistry
Abstract Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemical knowledge for each application…
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Augmenting large language models with chemistry tools
Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ChemCrow, an LLM chemistry agent designed…
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Multimodal data fusion for cancer biomarker discovery with deep learning
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Parameter-efficient fine-tuning of large-scale pre-trained language models
Abstract With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning paradigm, it has been continuously shown that larger models tend to yield better performance. However, as PLMs scale up, fine-tuning and storing all the parameters is prohibitively costly and…
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CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
Abstract Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics…
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Inverse design of nonlinear mechanical metamaterials via video denoising diffusion models
Abstract The accelerated inverse design of complex material properties—such as identifying a material with a given stress–strain response over a nonlinear deformation path—holds great potential for addressing challenges from soft robotics to biomedical implants and impact mitigation. Although…
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Development of metaverse for intelligent healthcare
The metaverse integrates physical and virtual realities, enabling humans and their avatars to interact in an environment supported by technologies such as high-speed internet, virtual reality, augmented reality, mixed and extended reality, blockchain, digital twins and artificial intelligence (AI)…
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Molecular contrastive learning of representations via graph neural networks
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Three types of incremental learning
Incrementally learning new information from a non-stationary stream of data, referred to as 'continual learning', is a key feature of natural intelligence, but a challenging problem for deep neural networks. In recent years, numerous deep learning methods for continual learning have been proposed…
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Geometry-enhanced molecular representation learning for property prediction
Abstract Effective molecular representation learning is of great importance to facilitate molecular property prediction. Recent advances for molecular representation learning have shown great promise in applying graph neural networks to model molecules. Moreover, a few recent studies design…
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The transformational role of GPU computing and deep learning in drug discovery
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A soft thumb-sized vision-based sensor with accurate all-round force perception
Abstract Vision-based haptic sensors have emerged as a promising approach to robotic touch due to affordable high-resolution cameras and successful computer vision techniques; however, their physical design and the information they provide do not yet meet the requirements of real applications. We…
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Benchmarking saliency methods for chest X-ray interpretation
Abstract Saliency methods, which produce heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. However, rigorous investigation of the accuracy and reliability of these strategies is…
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Inverse design of nanoporous crystalline reticular materials with deep generative models