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ORCIDNature Computational Science, Volume 6
Volume 6, Number 1, January 2026
- Turning five. 1
- Sophia Chen:
The afterlife of 20 million AI chips. 2-5 - Omer San
, Adil Rasheed, Eda Bozdemir, Jun Deng:
The evolution of digital twins from reactive to agentic systems. 6-10 - Zijing Gao, Rui Jiang:
Harnessing LLMs to decode genetic perturbations. 11-12 - Jeremie Alexander, Jonathan M. Stokes:
AI-guided molecular design with recipes included. 13-14 - Tong Zhao, Yan Zeng:
Digital twins for self-driving chemistry laboratories. 15-16 - Dinghao Wang, Qingrun Zhang:
Decoding omics via representation learning. 17-18 - Deep learning accelerates discovery of complex nanomaterials. 19-20
- Ouyang Zhu, Jun Li:
Scouter predicts transcriptional responses to genetic perturbations with large language model embeddings. 21-28 - Yuchen Zhu, Shuwang Li, Jihong Chen, Donghai Zhao, Xiaorui Wang, Yitong Li, Yifei Liu, Yue Kong, Beichen Zhang, Chang Liu, Tingjun Hou, Chang-Yu Hsieh:
SynGFN: learning across chemical space with generative flow-based molecular discovery. 29-38 - Yixuan Wang, Xinyuan Liu, Yimin Fan, Binghui Xie, James Cheng, Kam Chung Wong, Pak Hang Peter Cheung, Irwin King, Yu Li:
Predicting drug responses of unseen cell types through transfer learning with foundation models. 39-52 - Kai Ruan, Yilong Xu, Ze-Feng Gao, Yang Liu, Yike Guo, Ji-Rong Wen, Hao Sun:
Discovering physical laws with parallel symbolic enumeration. 53-66 - Kourosh Darvish, Arjun Sohal, Abhijoy Mandal, Hatem Fakhruldeen, Nikola Radulov, Zhengxue Zhou, Satheeshkumar Veeramani, Joshua Choi, Sijie Han, Brayden Zhang, Jeeyeoun Chae, Alex Wright, Yijie Wang, Hossein Darvish, Yuchi Zhao, Gary Tom, Han Hao, Miroslav Bogdanovic, Gabriella Pizzuto, Andrew I. Cooper, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg:
MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation. 67-82 - Eric Sivonxay, Lucas Attia, Evan Walter Clark Spotte-Smith, Benjamín Sánchez-Lengeling, Xiaojing Xia, Daniel Barter, Emory M. Chan, Samuel M. Blau:
Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs. 83-95 - Maximilian Josef Joas, Neringa Jurenaite, Dusan Prascevic, Nico Scherf, Jan Ewald:
AUTOENCODIX: a generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond. 96-108 - Eva Portelance, Masoud Jasbi:
Publisher Correction: On the compatibility of generative AI and generative linguistics. 109
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