First-principles electronic structure calculations have profoundly advanced research in physics, chemistry and materials science, yet their further development remains constrained by the accuracy–efficiency dilemma. Here we highlight recent breakthroughs in deep-learning methodologies that address this challenge, including the deep-learning quantum Monte Carlo method for the accurate study of correlated electrons and deep-learning density functional theory for efficient large-scale material simulations. These advances extend the reach of first-principles calculations to unprecedented scales and complexity, enhancing the impact of quantum mechanics in scientific discovery. This Review explores the integration of deep learning in first-principles electronic structure calculations, addressing the accuracy–efficiency dilemma of traditional algorithms and extending first-principles methods to unprecedented scales and complexity.
{"title":"Deep-learning electronic structure calculations","authors":"Zechen Tang, Haoxiang Chen, Yang Li, Yubing Qian, Yuxiang Wang, Weizhong Fu, Jialin Li, Chen Si, Wenhui Duan, Ji Chen, Yong Xu","doi":"10.1038/s43588-025-00932-4","DOIUrl":"10.1038/s43588-025-00932-4","url":null,"abstract":"First-principles electronic structure calculations have profoundly advanced research in physics, chemistry and materials science, yet their further development remains constrained by the accuracy–efficiency dilemma. Here we highlight recent breakthroughs in deep-learning methodologies that address this challenge, including the deep-learning quantum Monte Carlo method for the accurate study of correlated electrons and deep-learning density functional theory for efficient large-scale material simulations. These advances extend the reach of first-principles calculations to unprecedented scales and complexity, enhancing the impact of quantum mechanics in scientific discovery. This Review explores the integration of deep learning in first-principles electronic structure calculations, addressing the accuracy–efficiency dilemma of traditional algorithms and extending first-principles methods to unprecedented scales and complexity.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 12","pages":"1133-1146"},"PeriodicalIF":18.3,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1038/s43588-025-00914-6
Weikang Li, Yixuan Ma, Dong-Ling Deng
Quantum machine learning is being actively explored to assess whether quantum resources can enhance learning and inference, yet major obstacles remain. Here, we discuss pressing challenges and outline potential pathways toward future practical applications.
{"title":"Pitfalls and prospects of quantum machine learning","authors":"Weikang Li, Yixuan Ma, Dong-Ling Deng","doi":"10.1038/s43588-025-00914-6","DOIUrl":"10.1038/s43588-025-00914-6","url":null,"abstract":"Quantum machine learning is being actively explored to assess whether quantum resources can enhance learning and inference, yet major obstacles remain. Here, we discuss pressing challenges and outline potential pathways toward future practical applications.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 12","pages":"1095-1097"},"PeriodicalIF":18.3,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1038/s43588-025-00940-4
Sophia Chen
Data-center operators try to recycle retired hardware, but a broken global recycling infrastructure stands in the way.
数据中心运营商试图回收退役硬件，但全球回收基础设施的缺陷阻碍了这一进程。
{"title":"The afterlife of 20 million AI chips","authors":"Sophia Chen","doi":"10.1038/s43588-025-00940-4","DOIUrl":"10.1038/s43588-025-00940-4","url":null,"abstract":"Data-center operators try to recycle retired hardware, but a broken global recycling infrastructure stands in the way.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 1","pages":"2-5"},"PeriodicalIF":18.3,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145783912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1038/s43588-025-00928-0
Jeremie Alexander, Jonathan M. Stokes
SynGFN integrates synthesis constraints directly into the chemical design process. The result is a generative framework that produces diverse, high-quality molecules that can be readily synthesized in the laboratory.
{"title":"AI-guided molecular design with recipes included","authors":"Jeremie Alexander, Jonathan M. Stokes","doi":"10.1038/s43588-025-00928-0","DOIUrl":"10.1038/s43588-025-00928-0","url":null,"abstract":"SynGFN integrates synthesis constraints directly into the chemical design process. The result is a generative framework that produces diverse, high-quality molecules that can be readily synthesized in the laboratory.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 1","pages":"13-14"},"PeriodicalIF":18.3,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145783857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1038/s43588-025-00936-0
Vishwanathan Akshay, Mile Gu
A recent study demonstrates the applicability of quantum computers for multi-objective optimization, bringing quantum computing a step closer towards practical applications.
最近的一项研究证明了量子计算机对多目标优化的适用性，使量子计算向实际应用更近了一步。
{"title":"Improving the balance of trade-offs in multi-objective optimization with quantum computing","authors":"Vishwanathan Akshay, Mile Gu","doi":"10.1038/s43588-025-00936-0","DOIUrl":"10.1038/s43588-025-00936-0","url":null,"abstract":"A recent study demonstrates the applicability of quantum computers for multi-objective optimization, bringing quantum computing a step closer towards practical applications.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 12","pages":"1102-1103"},"PeriodicalIF":18.3,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1038/s43588-025-00935-1
{"title":"Toward a domain-grounded AI collaborator with SciSciGPT.","authors":"","doi":"10.1038/s43588-025-00935-1","DOIUrl":"https://doi.org/10.1038/s43588-025-00935-1","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1038/s43588-025-00910-w
Zijing Gao, Rui Jiang
Scouter, a deep learning approach, predicts transcriptional responses to genetic perturbations by integrating large language model (LLM)-based gene embeddings with a lightweight compressor–generator neural network, providing valuable insights into the application of LLMs to biological research.
{"title":"Harnessing LLMs to decode genetic perturbations","authors":"Zijing Gao, Rui Jiang","doi":"10.1038/s43588-025-00910-w","DOIUrl":"10.1038/s43588-025-00910-w","url":null,"abstract":"Scouter, a deep learning approach, predicts transcriptional responses to genetic perturbations by integrating large language model (LLM)-based gene embeddings with a lightweight compressor–generator neural network, providing valuable insights into the application of LLMs to biological research.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 1","pages":"11-12"},"PeriodicalIF":18.3,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1038/s43588-025-00918-2
A physics-infused heterogeneous graph neural network has been developed to address challenges in designing complex nanomaterials with spatially varying compositions. This fully differentiable model enables the rapid optimization and discovery of photon upconverting nanoparticle heterostructures that are 6.5-fold brighter than any nanoparticle in the training set.
{"title":"Deep learning accelerates discovery of complex nanomaterials","authors":"","doi":"10.1038/s43588-025-00918-2","DOIUrl":"10.1038/s43588-025-00918-2","url":null,"abstract":"A physics-infused heterogeneous graph neural network has been developed to address challenges in designing complex nanomaterials with spatially varying compositions. This fully differentiable model enables the rapid optimization and discovery of photon upconverting nanoparticle heterostructures that are 6.5-fold brighter than any nanoparticle in the training set.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 1","pages":"19-20"},"PeriodicalIF":18.3,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145764732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1038/s43588-025-00922-6
Luca Manneschi, Matthew O. A. Ellis
A recent study demonstrates the efficiency of quantum-mechanical modeling of material properties by mapping the problem onto neuromorphic device architectures.
最近的一项研究通过将问题映射到神经形态器件架构上，证明了材料特性量子力学建模的效率。
{"title":"Predicting physics efficiently with hybrid hardware","authors":"Luca Manneschi, Matthew O. A. Ellis","doi":"10.1038/s43588-025-00922-6","DOIUrl":"10.1038/s43588-025-00922-6","url":null,"abstract":"A recent study demonstrates the efficiency of quantum-mechanical modeling of material properties by mapping the problem onto neuromorphic device architectures.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 12","pages":"1104-1105"},"PeriodicalIF":18.3,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145727761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1038/s43588-025-00909-3
Dinghao Wang, Qingrun Zhang
A framework called AUTOENCODIX benchmarks diverse autoencoder architectures in biological molecular profiling data, enabling insights from complex, multi-layered data.
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