Nature Computational Science
@natcomputsci.nature.com
A @natureportfolio.nature.com journal on mathematical models and computational methods/tools that help advance science in multiple disciplines. https://www.nature.com/natcomputsci
An accompanying News & Views by Tong Zhao and Yan Zeng is also available for this paper! www.nature.com/articles/s43... #chemsky
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Digital twins for self-driving chemistry laboratories - Nature Computational Science
Digital twins of self-driving chemistry laboratories may help reduce reliance on costly real-world experimentation and enable the testing of hypothetical automated workflows in silico.
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January 5, 2026 at 7:35 PM
An accompanying News & Views by Tong Zhao and Yan Zeng is also available for this paper! www.nature.com/articles/s43... #chemsky
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An accompanying Research Briefing is also available for this paper! www.nature.com/articles/s43... 🖥️ 🧬
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Mapping cell–cell communication networks onto cell-state transition trajectories via a dynamic model - Nature Computational Science
The physics-informed coevolution-learning algorithm-based framework CCCvelo infers the spatio-temporal dynamics of cell-state transitions driven by both intercellular communication and intracellular t...
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January 5, 2026 at 6:11 PM
An accompanying Research Briefing is also available for this paper! www.nature.com/articles/s43... 🖥️ 🧬
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Finally, the Focus also includes additional material published on the topic of quantum mechanics. We very much hope you enjoy it! ⚛️🛠️
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www.nature.com/collections/...
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Expanding the Frontiers of Computation with Quantum Mechanics
This year marks 100 years since the advent of quantum mechanics, which underpins much of modern quantum physics, chemistry, computing methods and technologies.
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December 23, 2025 at 12:25 AM
Finally, the Focus also includes additional material published on the topic of quantum mechanics. We very much hope you enjoy it! ⚛️🛠️
www.nature.com/collections/...
🧵(13/13)
www.nature.com/collections/...
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An accompanying News & Views by Vishwanathan Akshay and Mile Gu is also available for this paper! www.nature.com/articles/s43... ⚛️
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Improving the balance of trade-offs in multi-objective optimization with quantum computing - Nature Computational Science
A recent study demonstrates the applicability of quantum computers for multi-objective optimization, bringing quantum computing a step closer towards practical applications.
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December 23, 2025 at 12:25 AM
An accompanying News & Views by Vishwanathan Akshay and Mile Gu is also available for this paper! www.nature.com/articles/s43... ⚛️
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Stefan Woerner and colleagues investigate using quantum computing to tackle multi-objective optimization, showing promising results on IBM Quantum computer when compared to classical methods. www.nature.com/articles/s43... ⚛
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Quantum approximate multi-objective optimization - Nature Computational Science
This study explores the use of quantum computing to address multi-objective optimization challenges. By using a low-depth quantum approximate optimization algorithm to approximate the optimal Pareto f...
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December 23, 2025 at 12:25 AM
Stefan Woerner and colleagues investigate using quantum computing to tackle multi-objective optimization, showing promising results on IBM Quantum computer when compared to classical methods. www.nature.com/articles/s43... ⚛
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An accompanying News & Views by Xiu-Hao Deng and Yuan Xu is also available for this paper! www.nature.com/articles/s43... ⚛
🧵(10/13)
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Efficiently decoding quantum errors with machine learning - Nature Computational Science
Quantum computers are inching closer to practical deployment, but shielding fragile quantum information from errors is still very challenging. Now, a machine-learning-based decoder offers a strategy f...
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December 23, 2025 at 12:25 AM
An accompanying News & Views by Xiu-Hao Deng and Yuan Xu is also available for this paper! www.nature.com/articles/s43... ⚛
🧵(10/13)
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In another Article, Yiqing Zhou, Eun-Ah Kim, and colleagues report a machine learning decoder for correcting errors in quantum logical circuits with entangling gates. www.nature.com/articles/s43... ⚛️
🧵(9/13)
🧵(9/13)
Learning to decode logical circuits - Nature Computational Science
This study reports a machine learning decoder that efficiently corrects errors in quantum logical circuits with entangling gates. The Multi-Core Circuit Decoder achieves competitive accuracy while run...
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December 23, 2025 at 12:25 AM
In another Article, Yiqing Zhou, Eun-Ah Kim, and colleagues report a machine learning decoder for correcting errors in quantum logical circuits with entangling gates. www.nature.com/articles/s43... ⚛️
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An accompanying News & Views by Yubing Qian and Ji Chen is also available for this paper! www.nature.com/articles/s43... ⚛
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Down to one network for computing crystalline materials - Nature Computational Science
A recent study proposes using a single neural network to model and compute a wide range of solid-state materials, demonstrating exceptional transferability and substantially reduced computational cost...
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December 23, 2025 at 12:25 AM
An accompanying News & Views by Yubing Qian and Ji Chen is also available for this paper! www.nature.com/articles/s43... ⚛
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Philipp Grohs and colleagues present an approach that reduces the computational cost to model and compute crystalline materials, such as graphene or lithium hydride, by a factor of 50 compared with previous work. www.nature.com/articles/s43... ⚛️
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Transferable neural wavefunctions for solids - Nature Computational Science
Investigating crystalline materials often requires calculations for many variations of a system, substantially increasing the computational burden. By training a transferable neural wavefunction acros...
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December 23, 2025 at 12:25 AM
Philipp Grohs and colleagues present an approach that reduces the computational cost to model and compute crystalline materials, such as graphene or lithium hydride, by a factor of 50 compared with previous work. www.nature.com/articles/s43... ⚛️
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In a Review, Yong Xu and colleagues discuss the integration of deep learning into first-principles electronic structure calculations, as well as the challenges that must be overcome to ensure more efficient large-scale simulations. www.nature.com/articles/s43... #chemsky
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🧵(6/13)
Deep-learning electronic structure calculations - Nature Computational Science
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-pr...
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December 23, 2025 at 12:25 AM
In a Review, Yong Xu and colleagues discuss the integration of deep learning into first-principles electronic structure calculations, as well as the challenges that must be overcome to ensure more efficient large-scale simulations. www.nature.com/articles/s43... #chemsky
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In a Perspective, @olexandr.bsky.social and colleagues discuss the development of MLIPs through the lens of four key challenges: chemical accuracy, computational efficiency, interpretability, and universal generalizability. www.nature.com/articles/s43... #compchem #chemsky
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Machine learning interatomic potentials at the centennial crossroads of quantum mechanics - Nature Computational Science
As quantum mechanics marks its centennial, machine learning interatomic potentials are emerging as transformative tools bridging quantum accuracy with classical efficiency. This Perspective explores t...
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December 23, 2025 at 12:25 AM
In a Perspective, @olexandr.bsky.social and colleagues discuss the development of MLIPs through the lens of four key challenges: chemical accuracy, computational efficiency, interpretability, and universal generalizability. www.nature.com/articles/s43... #compchem #chemsky
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In a Perspective, Mikhail Lukin et al. outline recent breakthroughs and explore the opportunities to reduce the overhead of quantum error correction by co-designing across algorithms, error correction schemes, and hardware architecture. www.nature.com/articles/s43... ⚛️
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Opportunities in full-stack design of low-overhead fault-tolerant quantum computation - Nature Computational Science
Quantum error correction is vital for scalable quantum computing, but it incurs high resource overheads. This Perspective outlines recent breakthroughs and explores the opportunities to reduce the ove...
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December 23, 2025 at 12:25 AM
In a Perspective, Mikhail Lukin et al. outline recent breakthroughs and explore the opportunities to reduce the overhead of quantum error correction by co-designing across algorithms, error correction schemes, and hardware architecture. www.nature.com/articles/s43... ⚛️
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Quantum ML is being explored to assess whether quantum resources can enhance learning and inference. Dong-Lin Deng and colleagues discuss pressing challenges and outline potential pathways toward future practical applications. www.nature.com/articles/s43... ⚛️ #ArtificialIntelligence
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Pitfalls and prospects of quantum machine learning - Nature Computational Science
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...
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December 23, 2025 at 12:25 AM
Quantum ML is being explored to assess whether quantum resources can enhance learning and inference. Dong-Lin Deng and colleagues discuss pressing challenges and outline potential pathways toward future practical applications. www.nature.com/articles/s43... ⚛️ #ArtificialIntelligence
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While the advances highlighted in this Focus are not exhaustive, they demonstrate the enduring impact of quantum mechanics and its expanding frontier. www.nature.com/articles/s43... ⚛️
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Reshaping computation with quantum mechanics - Nature Computational Science
As quantum mechanics marks its centennial, this issue of Nature Computational Science features a Focus that outlines the impact of quantum mechanics in advancing computing technologies, while discussi...
www.nature.com
December 23, 2025 at 12:25 AM
While the advances highlighted in this Focus are not exhaustive, they demonstrate the enduring impact of quantum mechanics and its expanding frontier. www.nature.com/articles/s43... ⚛️
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An accompanying News & Views for this paper from Jeremie Alexander and Jonathan Stokes is now available! www.nature.com/articles/s43... #chemsky
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AI-guided molecular design with recipes included - Nature Computational Science
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...
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December 18, 2025 at 1:42 PM
An accompanying News & Views for this paper from Jeremie Alexander and Jonathan Stokes is now available! www.nature.com/articles/s43... #chemsky
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An accompanying News & Views by Vishwanathan Akshay and Mile Gu for this paper is now available! www.nature.com/articles/s43... ⚛️
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Improving the balance of trade-offs in multi-objective optimization with quantum computing - Nature Computational Science
A recent study demonstrates the applicability of quantum computers for multi-objective optimization, bringing quantum computing a step closer towards practical applications.
www.nature.com
December 16, 2025 at 5:50 PM
An accompanying News & Views by Vishwanathan Akshay and Mile Gu for this paper is now available! www.nature.com/articles/s43... ⚛️
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An accompanying News & Views by Zijing Gao and Rui Jiang for this paper is now available! www.nature.com/articles/s43... 🖥️ 🧬
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Harnessing LLMs to decode genetic perturbations - Nature Computational Science
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 n...
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December 16, 2025 at 5:47 PM
An accompanying News & Views by Zijing Gao and Rui Jiang for this paper is now available! www.nature.com/articles/s43... 🖥️ 🧬
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