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ORCIDNature Computational Science, Volume 3
Volume 3, Number 1, 2023
- Cover runners-up of 2022. 1
- Fernando Chirigati:
Leveraging the power of crowds. 2 - Ananya Rastogi:
Deep learning to estimate brain age. 3 - Kaitlin McCardle:
Uncovering contaminants via machine learning. 4 - Jie Pan:
Large language model for molecular chemistry. 5 - Stefan Vuckovic:
Using AI to navigate through the DFA zoo. 6-7 - Yang Yang
, Zewen K. Tuong, Di Yu:
Dimensionality reduction under scrutiny. 8-9 - Towards a purely physics-based computational binding affinity estimation. 10-11
- Mingjian Wen, Evan Walter Clark Spotte-Smith, Samuel M. Blau, Matthew J. McDermott, Aditi S. Krishnapriyan, Kristin A. Persson:
Chemical reaction networks and opportunities for machine learning. 12-24 - Alexander Miessen, Pauline J. Ollitrault, Francesco Tacchino, Ivano Tavernelli:
Quantum algorithms for quantum dynamics. 25-37 - Chenru Duan, Aditya Nandy, Ralf Meyer, Naveen Arunachalam, Heather J. Kulik:
A transferable recommender approach for selecting the best density functional approximations in chemical discovery. 38-47 - Yiwei Liu, Cheng Zhang, Zhonghua Liu, Donald G. Truhlar, Ying Wang, Xiao He:
Supervised learning of a chemistry functional with damped dispersion. 48-58 - Vivek Govind Kumar, Adithya Polasa, Shilpi Agrawal, Thallapuranam K. Suresh Kumar, Mahmoud Moradi:
Binding affinity estimation from restrained umbrella sampling simulations. 59-70 - Tze Hui Koh, William E. Bishop, Takashi Kawashima, Brian B. Jeon, Ranjani Srinivasan, Yu Mu, Ziqiang Wei, Sandra J. Kuhlman, Misha B. Ahrens, Steven M. Chase, Byron M. Yu:
Dimensionality reduction of calcium-imaged neuronal population activity. 71-85 - Eric D. Sun, Rong Ma, James Zou:
Dynamic visualization of high-dimensional data. 86-100 - R. Patrick Xian, Vincent Stimper, Marios Zacharias, Maciej Dendzik, Shuo Dong, Samuel Beaulieu, Bernhard Schölkopf, Martin Wolf, Laurenz Rettig, Christian Carbogno, Stefan Bauer, Ralph Ernstorfer:
A machine learning route between band mapping and band structure. 101-114
Volume 3, Number 2, 2023
- Why can't we predict earthquakes? 115
- Fernando Chirigati:
Advancing the science of synthesis. 116-117 - Fernando Chirigati:
A successful few drive urban scaling. 118 - Kaitlin McCardle:
Advancing organic reaction discovery. 119 - Jie Pan:
Effective phase analyzer learned from stock images. 120 - Ananya Rastogi:
When and where the climate threshold will be reached. 121 - Yang Jiao:
Evolving wave networks for materials design. 122-123 - Yunan Luo:
Sensing the shape of functional proteins with topology. 124-125 - Sandeep Choubey:
Gene regulation meets Bayesian non-parametrics. 126-127 - Sunkyu Yu:
Evolving scattering networks for engineering disorder. 128-138 - Julia Westermayr, Joe Gilkes, Rhyan Barrett, Reinhard Johann Maurer:
High-throughput property-driven generative design of functional organic molecules. 139-148 - Yuchi Qiu, Guo-Wei Wei:
Persistent spectral theory-guided protein engineering. 149-163 - Tal Einav, Adrian Creanga, Sarah F. Andrews, Adrian B. McDermott, Masaru Kanekiyo:
Harnessing low dimensionality to visualize the antibody-virus landscape for influenza. 164-173 - Zeliha Kilic, Max Schweiger, Camille Moyer, Douglas Shepherd, Steve Pressé:
Gene expression model inference from snapshot RNA data using Bayesian non-parametrics. 174-183
Volume 3, Number 3, 2023
- Guiding element mixing. 185-186
- Kaitlin McCardle:
Understanding cancer drivers and passengers. 187-188 - Kaitlin McCardle:
Accounting for quantum effects in catalysis. 189 - Simon L. Batzner:
Biasing energy surfaces towards the unknown. 190-191 - Revealing trajectories of the mind via non-linear manifolds of brain activity. 192-193
- Oscillations and avalanches coexist in brain networks close to criticality. 194-195
- A single-cell-resolution mathematical model of the CA1 human hippocampus. 196-197
- Dierk Raabe, Jaber Rezaei Mianroodi, Jörg Neugebauer:
Accelerating the design of compositionally complex materials via physics-informed artificial intelligence. 198-209 - Xie Zhang, Jun Kang, Suhuai Wei:
Defect modeling and control in structurally and compositionally complex materials. 210-220 - Alberto Ferrari, Fritz Körmann, Mark Asta, Jörg Neugebauer:
Simulating short-range order in compositionally complex materials. 221-229 - Maksim Kulichenko, Kipton Barros, Nicholas Lubbers, Ying Wai Li, Richard A. Messerly, Sergei Tretiak, Justin S. Smith, Benjamin T. Nebgen:
Uncertainty-driven dynamics for active learning of interatomic potentials. 230-239 - Erica L. Busch, Jessie Huang, Andrew Benz, Tom Wallenstein, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy, Nicholas B. Turk-Browne:
Multi-view manifold learning of human brain-state trajectories. 240-253 - Fabrizio Lombardi, Selver Pepic, Oren Shriki, Gasper Tkacik, Daniele De Martino:
Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain. 254-263 - Daniela Gandolfi, Jonathan Mapelli, Sergio M. G. Solinas, Paul Triebkorn, Egidio D'Angelo, Viktor K. Jirsa, Michele Migliore:
Full-scale scaffold model of the human hippocampus CA1 area. 264-276
Volume 3, Number 4, 2023
- Computationally probing moiré magnets. 277-278
- Fernando Chirigati:
Moving toward safer driverless vehicles. 279 - Jie Pan:
Transfer learning for metal-organic frameworks. 280 - Kaitlin McCardle:
Learning properties of metal complexes. 281 - David Soriano:
Uncovering magnetic interactions in moiré magnets. 282-284 - Ali Rahnavard:
Omics correlation for efficient network construction. 285-286 - A deep-learning method for studying magnetic superstructures. 287-288
- A machine learning algorithm for studying how molecules self-assemble and function. 289-290
- Sharon Hammes-Schiffer:
Exploring proton-coupled electron transfer at multiple scales. 291-300 - Maalavika Pillai, Emilia Hojel, Mohit Kumar Jolly, Yogesh Goyal:
Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. 301-313 - Baishun Yang, Yang Li, Hongjun Xiang, Haiqing Lin, Bing Huang:
Moiré magnetic exchange interactions in twisted magnets. 314-320 - He Li, Zechen Tang, Xiaoxun Gong, Nianlong Zou, Wenhui Duan, Yong Xu:
Deep-learning electronic-structure calculation of magnetic superstructures. 321-327 - Alec F. White, Chenghan Li, Xing Zhang, Garnet Kin-Lic Chan:
Quantum harmonic free energies for biomolecules and nanomaterials. 328-333 - Hendrik Jung, Roberto Covino, A. Arjun, Christian Leitold, Christoph Dellago, Peter G. Bolhuis, Gerhard Hummer:
Machine-guided path sampling to discover mechanisms of molecular self-organization. 334-345 - Martin Becker, Huda Nassar, Camilo Espinosa, Ina A. Stelzer, Dorien Feyaerts, Eloïse Berson, Neda Hajiakhoond Bidoki, Alan L. Chang, Geetha Saarunya, Anthony Culos, Davide De Francesco, Ramin Fallahzadeh, Qun Liu, Yeasul Kim, Ivana Maric, Samson Mataraso, Seyedeh Neelufar Payrovnaziri, Thanaphong Phongpreecha, Neal G. Ravindra, Natalie Stanley, Sayane Shome, Yuqi Tan, Melan Thuraiappah, Maria Xenochristou, Lei Xue, Gary M. Shaw, David K. Stevenson, Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour:
Large-scale correlation network construction for unraveling the coordination of complex biological systems. 346-359
Volume 3, Number 5, 2023
- Experimental validation, anyone? 361
- Nicholas David, Wenhao Sun, Connor W. Coley:
The promise and pitfalls of AI for molecular and materials synthesis. 362-364 - Ananya Rastogi:
Exploring robust pattern formation. 365 - Ava P. Amini, Kevin K. Yang:
From noise to protein with image models. 366-367 - Alberto Corigliano:
Discovering kirigami patterns. 368-369 - A machine learning-based model for the quantification of mental conflict. 370-371
- A graph neural network for predicting the adsorption energy of molecules on metal surfaces. 372-373
- Guido Caldarelli, Elsa Arcaute, Marc Barthelemy, Michael Batty, Carlos Gershenson, Dirk Helbing, Stefano Mancuso, Yamir Moreno, José J. Ramasco, Céline Rozenblat, Ángel Sánchez, José Luis Fernández-Villacañas:
The role of complexity for digital twins of cities. 374-381 - Jin Sub Lee, Jisun Kim, Philip M. Kim:
Score-based generative modeling for de novo protein design. 382-392 - Jacob Saldinger, Matt Raymond, Paolo Elvati, Angela Violi:
Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles. 393-402 - Gengjie Jia, Yu Li, Xue Zhong, Kanix Wang, Milton Pividori, Rabab Alomairy, Aniello Esposito, Hatem Ltaief, Chikashi Terao, Masato Akiyama, Koichi Matsuda, David E. Keyes, Hae Kyung Im, Takashi Gojobori, Yoichiro Kamatani, Michiaki Kubo, Nancy J. Cox, James A. Evans, Xin Gao, Andrey Rzhetsky:
The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci. 403-417 - Yuki Konaka, Honda Naoki:
Decoding reward-curiosity conflict in decision-making from irrational behaviors. 418-432 - Sergio Pablo-García, Santiago Morandi, Rodrigo A. Vargas-Hernández, Kjell Jorner, Zarko Ivkovic, Núria López, Alán Aspuru-Guzik:
Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks. 433-442 - Levi H. Dudte, Gary P. T. Choi, Kaitlyn P. Becker, L. Mahadevan:
An additive framework for kirigami design. 443-454
Volume 3, Number 6, 2023
- A computational quest for a sustainable world. 455-456
- Sophia Chen:
Are quantum computers really energy efficient? 457-460 - Edmundo Molina-Perez:
Harnessing the power of decision-support tools to trigger climate action. 461-463 - Kaitlin McCardle:
Collaborations drive energy storage research. 464-466 - Kaitlin McCardle:
People, places, and the planet. 467-469 - Kaitlin McCardle:
Little by little, a bird builds its nest. 470-472 - Jie Pan:
Towards a welcoming climate for LGBTQ people. 473-475 - Ananya Rastogi:
Particle picking in tomograms. 476 - Fernando Chirigati:
Experts go deeper. 477 - Kaitlin McCardle:
Building molecules across the periodic table. 478 - Jie Pan:
Treating quantum states as human faces. 479 - Marco Bernardi:
Computing electron dynamics in momentum space. 480-481 - A single-cell transcriptomic meta-analysis platform for inflammatory bowel disease. 482-483
- Why bigger quantum neural networks do better. 484-485
- Matthew MacLeod, Prado Domercq, Sam Harrison, Antonia Praetorius:
Computational models to confront the complex pollution footprint of plastic in the environment. 486-494 - Jakub Kubecka, Yosef Knattrup, Morten Engsvang, Andreas Buchgraitz Jensen, Daniel Ayoubi, Haide Wu, Ove Christiansen, Jonas Elm:
Current and future machine learning approaches for modeling atmospheric cluster formation. 495-503 - Ke R. Yang, Gregory W. Kyro, Victor S. Batista:
The landscape of computational approaches for artificial photosynthesis. 504-513 - Loïc Lannelongue, Hans-Erik G. Aronson, Alex Bateman, Ewan Birney, Talia Caplan, Martin Juckes, Johanna R. McEntyre, Andrew D. Morris, Gerry Reilly, Michael Inouye:
GREENER principles for environmentally sustainable computational science. 514-521 - Hu Nie, Peilu Lin, Yu Zhang, Yihong Wan, Jiesheng Li, Chengqian Yin, Lei Zhang:
Single-cell meta-analysis of inflammatory bowel disease with scIBD. 522-531 - Zhenfa Zheng, Yongliang Shi, Jin-Jian Zhou, Oleg V. Prezhdo, Qijing Zheng, Jin Zhao:
Ab initio real-time quantum dynamics of charge carriers in momentum space. 532-541 - Martin Larocca, Nathan Ju, Diego García-Martín, Patrick J. Coles, Marco Cerezo:
Theory of overparametrization in quantum neural networks. 542-551 - Luca Cappelletti, Tommaso Fontana, Elena Casiraghi, Vida Ravanmehr, Tiffany J. Callahan, Carlos Cano, Marcin P. Joachimiak, Christopher J. Mungall, Peter N. Robinson, Justin T. Reese, Giorgio Valentini:
GRAPE for fast and scalable graph processing and random-walk-based embedding. 552-568 - He Li, Zechen Tang, Xiaoxun Gong, Nianlong Zou, Wenhui Duan, Yong Xu:
Author Correction: Deep-learning electronic-structure calculation of magnetic superstructures. 569
Volume 3, Number 7, 2023
- Of data and transparency. 571
- Da Yan, Adam D. Smith, Cheng-Chien Chen:
Structure prediction and materials design with generative neural networks. 572-574 - Jie Pan:
Physics-inspired model for online content dynamics. 575 - Fernando Chirigati:
A language model for medical predictive tasks. 576 - Kaitlin McCardle:
Shedding light on microbiome-drug interactions. 577 - James P. Bagrow:
Using fast and slow data to unfold city dynamics. 578-579 - Chris C. R. Smith:
Machine learning speeds up genetic structure analysis. 580-581 - Zheyang Zhang, Jialiang Huang:
Cellular deconvolution with continuous transitions. 582-583 - Pawel F. Przytycki:
Uncovering the genetic circuits that drive diseases. 584-585 - A software resource for large graph processing and analysis. 586-587
- Luca Pappalardo, Ed Manley, Vedran Sekara, Laura Alessandretti:
Future directions in human mobility science. 588-600 - Alex Chohlas-Wood, Madison Coots, Sharad Goel, Julian Nyarko:
Designing equitable algorithms. 601-610 - Yanyan Xu, Luis E. Olmos, David Mateo, Alberto Hernando, Xiaokang Yang, Marta C. González:
Urban dynamics through the lens of human mobility. 611-620 - Albert Dominguez Mantes, Daniel Mas Montserrat, Carlos D. Bustamante, Xavier Giró-i-Nieto, Alexander G. Ioannidis:
Neural ADMIXTURE for rapid genomic clustering. 621-629 - Liyang Song, Xiwei Sun, Ting Qi, Jian Yang:
Mixed model-based deconvolution of cell-state abundances (MeDuSA) along a one-dimensional trajectory. 630-643 - Xi Chen, Yuan Wang, Antonio Cappuccio, Wan-Sze Cheng, Frederique Ruf Zamojski, Venugopalan D. Nair, Clare M. Miller, Aliza B. Rubenstein, German Nudelman, Alicja Tadych, Chandra L. Theesfeld, Alexandria Vornholt, Mary-Catherine George, Felicia Ruffin, Michael Dagher, Daniel G. Chawla, Alessandra Soares-Schanoski, Rachel R. Spurbeck, Lishomwa C. Ndhlovu, Robert P. Sebra, Steven H. Kleinstein, Andrew G. Letizia, Irene Ramos, Vance G. Fowler, Christopher W. Woods, Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon:
Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data. 644-657 - Gengjie Jia, Yu Li, Xue Zhong, Kanix Wang, Milton Pividori, Rabab Alomairy, Aniello Esposito, Hatem Ltaief, Chikashi Terao, Masato Akiyama, Koichi Matsuda, David E. Keyes, Hae Kyung Im, Takashi Gojobori, Yoichiro Kamatani, Michiaki Kubo, Nancy J. Cox, James A. Evans, Xin Gao, Andrey Rzhetsky:
Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci. 658
Volume 3, Number 8, 2023
- The carbon footprint of computational research. 659
- Michelle N. Meyer, John Basl, David R. Choffnes, Christo Wilson, David M. J. Lazer:
Enhancing the ethics of user-sourced online data collection and sharing. 660-664 - Jie Pan:
Optimal crystal structure solutions. 665 - Ananya Rastogi:
Estimating cell growth rate. 666 - Liguo Wang:
Reference-guided search for open reading frames. 667-668 - Luer Zhong, Rhonda Bacher:
Leveraging remeasured samples in biomedical studies. 669-670 - Energy materials screening with defect graph neural networks. 671-672
- An imaging-based approach to measure atmospheric turbulence. 673-674
- Matthew D. Witman, Anuj Goyal, Tadashi Ogitsu, Anthony H. McDaniel, Stephan Lany:
Defect graph neural networks for materials discovery in high-temperature clean-energy applications. 675-686 - Yadong Wang, Darui Jin, Junzhang Chen, Xiangzhi Bai:
Revelation of hidden 2D atmospheric turbulence strength fields from turbulence effects in infrared imaging. 687-699 - Ales Varabyou, Beril Erdogdu, Steven L. Salzberg, Mihaela Pertea:
Investigating open reading frames in known and novel transcripts using ORFanage. 700-708 - Hanxuan Ye, Xianyang Zhang, Chen Wang, Ellen L. Goode, Jun Chen:
Batch-effect correction with sample remeasurement in highly confounded case-control studies. 709-719 - Liguo Wang:
Publisher Correction: Reference-guided search for open reading frames. 720
Volume 3, Number 9, 2023
- The next seven years. 721
- John McCloskey, Mark Pelling, Carmine Galasso, Gemma Cremen, Emin Yahya Mentese, Max Hope, Thaisa Comelli, Tanvi Deshpande, Ramesh Guragain, Alejandro Barcena, Roberto Gentile:
Reducing disaster risk for the poor in tomorrow's cities with computational science. 722-725 - Elisa Omodei:
Using computational tools to monitor and improve access to quality food and water. 726-728 - Fernando Chirigati:
Towards equitable education. 729-730 - Fernando Chirigati:
Greener urban communities. 731-732 - Fernando Chirigati:
From data to conservation laws. 733 - Kaitlin McCardle:
Conversations that explain predictions. 734 - Paolo Santi:
AI improves the design of urban communities. 735-736 - Benjamin Allen:
Flipping the intuition for games on dynamic networks. 737-738 - Shina Caroline Lynn Kamerlin:
Progress in using deep learning to treat cancer. 739-740 - Bassel Ghaddar, Martin J. Blaser, Subhajyoti De:
Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues. 741-747 - Yu Zheng, Yuming Lin, Liang Zhao, Tinghai Wu, Depeng Jin, Yong Li:
Spatial planning of urban communities via deep reinforcement learning. 748-762 - Qi Su, Alex McAvoy, Joshua B. Plotkin:
Strategy evolution on dynamic networks. 763-776 - Hanieh Mazloom-Farsibaf, Qiongjing Zou, Rebecca Hsieh, Gaudenz Danuser, Meghan K. Driscoll:
Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface. 777-788 - Xujun Zhang, Odin Zhang, Chao Shen, Wanglin Qu, Shicheng Chen, Hanqun Cao, Yu Kang, Zhe Wang, Ercheng Wang, Jintu Zhang, Yafeng Deng, Furui Liu, Tianyue Wang, Hongyan Du, Langcheng Wang, Peichen Pan, Guangyong Chen, Chang-Yu Hsieh, Tingjun Hou:
Efficient and accurate large library ligand docking with KarmaDock. 789-804 - Xi Chen, Yuan Wang, Antonio Cappuccio, Wan-Sze Cheng, Frederique Ruf Zamojski, Venugopalan D. Nair, Clare M. Miller, Aliza B. Rubenstein, German Nudelman, Alicja Tadych, Chandra L. Theesfeld, Alexandria Vornholt, Mary-Catherine George, Felicia Ruffin, Michael Dagher, Daniel G. Chawla, Alessandra Soares-Schanoski, Rachel R. Spurbeck, Lishomwa C. Ndhlovu, Robert P. Sebra, Steven H. Kleinstein, Andrew G. Letizia, Irene Ramos, Vance G. Fowler, Christopher W. Woods, Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon:
Author Correction: Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data. 805
Volume 3, Number 10, 2023
- Ada Lovelace, a role model for the ages. 807
- Ananya Rastogi:
Moving towards better communication. 808-809 - Ananya Rastogi:
Tackling environmental challenges, one molecule at a time. 810-812 - Ananya Rastogi:
Laying the foundations of programming and system design. 813-814 - Kaitlin McCardle:
Drug discovery with limited resources. 815 - Jie Pan:
Adding charge to neural network potentials. 816 - Ananya Rastogi:
The waiting game. 817 - Fernando Chirigati:
Alerting for imminent earthquakes. 818 - Zhaorong Fu, Jueming Bao, Jianwei Wang:
Programmability empowering quantum boson sampling. 819-820 - Ganna Gryn'ova:
Crafting molecular architectures with guided diffusion. 821-822 - Brennan Klein:
A consolidated framework for quantifying interaction dynamics. 823-824 - Measuring three-dimensional organization on cell surfaces with cellular harmonics. 825-826
- AI-powered structure-based drug design inspired by the lock-and-key model. 827-828
- Efficient prediction of relative ligand binding affinity in drug discovery. 829-830
- STAligner enables the integration and alignment of multiple spatial transcriptomics datasets. 831-832
- Thilo Hagendorff, Sarah Fabi, Michal Kosinski:
Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT. 833-838 - Shang Yu, Zhi-Peng Zhong, Yuhua Fang, Raj B. Patel, Qing-Peng Li, Wei Liu, Zhenghao Li, Liang Xu, Steven Sagona-Stophel, Ewan Mer, Sarah E. Thomas, Yu Meng, Zhi-Peng Li, Yuan-Ze Yang, Zhaoan Wang, Nai-Jie Guo, Wen-Hao Zhang, Geoffrey K. Tranmer, Ying Dong, Yi-Tao Wang, Jian-Shun Tang, Chuan-Feng Li, Ian A. Walmsley, Guang-Can Guo:
A universal programmable Gaussian boson sampler for drug discovery. 839-848 - Odin Zhang, Tianyue Wang, Gaoqi Weng, Dejun Jiang, Ning Wang, Xiaorui Wang, Huifeng Zhao, Jialu Wu, Ercheng Wang, Guangyong Chen, Yafeng Deng, Peichen Pan, Yu Kang, Chang-Yu Hsieh, Tingjun Hou:
Learning on topological surface and geometric structure for 3D molecular generation. 849-859 - Jie Yu, Zhaojun Li, Geng Chen, Xiangtai Kong, Jie Hu, Dingyan Wang, Duanhua Cao, Yanbei Li, Ruifeng Huo, Gang Wang, Xiaohong Liu, Hualiang Jiang, Xutong Li, Xiaomin Luo, Mingyue Zheng:
Computing the relative binding affinity of ligands based on a pairwise binding comparison network. 860-872 - Tomer Weiss, Eduardo Mayo Yanes, Sabyasachi Chakraborty, Luca Cosmo, Alex M. Bronstein, Renana Gershoni-Poranne:
Guided diffusion for inverse molecular design. 873-882 - Oliver M. Cliff, Annie G. Bryant, Joseph T. Lizier, Naotsugu Tsuchiya, Ben D. Fulcher:
Unifying pairwise interactions in complex dynamics. 883-893 - Xiang Zhou, Kangning Dong, Shihua Zhang:
Integrating spatial transcriptomics data across different conditions, technologies and developmental stages. 894-906
Volume 3, Number 11, 2023
- Code sharing in the spotlight. 907
- Yash Raj Shrestha, Georg von Krogh, Stefan Feuerriegel:
Building open-source AI. 908-911 - Fernando Chirigati:
Memory and computation together at last. 912 - Jie Pan:
A full-stack platform for spiking deep learning. 913 - Ananya Rastogi:
Impact of spillovers on technological changes. 914 - Kaitlin McCardle:
Predicting electronic structure calculation results. 915 - T. Matthew Evans:
Investigating the elegance of empty space. 916-917 - Conrard Giresse Tetsassi Feugmo:
Accurately predicting molecular spectra with deep learning. 918-919 - A software framework for end-to-end genomic sequence analysis with deep learning. 920-921
- Agnieszka Ilnicka, Gisbert Schneider:
Designing molecules with autoencoder networks. 922-933 - Sean D. Griesemer, Yi Xia, Chris Wolverton:
Accelerating the prediction of stable materials with machine learning. 934-945 - Adam R. Klie, David Laub, James V. Talwar, Hayden Stites, Tobias Jores, Joe J. Solvason, Emma K. Farley, Hannah Carter:
Predictive analyses of regulatory sequences with EUGENe. 946-956 - Zihan Zou, Yujin Zhang, Lijun Liang, Mingzhi Wei, Jiancai Leng, Jun Jiang, Yi Luo, Wei Hu:
A deep learning model for predicting selected organic molecular spectra. 957-964 - Xingyi Guan, Joseph P. Heindel, Taehee Ko, Chao Yang, Teresa Head-Gordon:
Using machine learning to go beyond potential energy surface benchmarking for chemical reactivity. 965-974 - Lindsay Riley, Peter Cheng, Tatiana Segura:
Identification and analysis of 3D pores in packed particulate materials. 975-992
Volume 3, Number 12, 2023
- Formats for reporting primary research. 993
- Gaomou Xu, Tao Wang:
Fewer false positives in electrocatalytic nitrogen reduction by synergizing theory and experiment. 994-997 - Tsz Wai Ko, Shyue Ping Ong:
Recent advances and outstanding challenges for machine learning interatomic potentials. 998-1000 - Ananya Rastogi:
From inception to current challenges in bioinformatics. 1001-1002 - Jie Pan:
Towards programmed protein generation. 1003 - Kaitlin McCardle:
Shedding light on GNN affinity predictions. 1004 - Fernando Chirigati:
One algorithm to play them all. 1005 - Ananya Rastogi:
Drawing statistically valid conclusions with ML. 1006 - Nguyen-Quoc-Khanh Le:
Predicting emerging drug interactions using GNNs. 1007-1008 - Mie Andersen:
Machine learning speeds up search for surface structure. 1009-1010 - David J. Wen, Christina V. Theodoris:
Interpretable model of CRISPR-Cas9 enzymatic reactions. 1011-1012 - Lachlan Whitehead:
Moving towards a generalized denoising network for microscopy. 1013-1014 - Yang Jeong Park, Hyungi Kim, Jeonghee Jo, Sungroh Yoon:
Deep contrastive learning of molecular conformation for efficient property prediction. 1015-1022 - Yongqi Zhang, Quanming Yao, Ling Yue, Xian Wu, Ziheng Zhang, Zhenxi Lin, Yefeng Zheng:
Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network. 1023-1033 - Xiaochen Du, James K. Damewood, Jaclyn R. Lunger, Reisel Millan, Bilge Yildiz, Lin Li, Rafael Gómez-Bombarelli:
Machine-learning-accelerated simulations to enable automatic surface reconstruction. 1034-1044 - Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik:
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. 1045-1055 - Zijun Zhang, Adam R. Lamson, Michael J. Shelley, Olga G. Troyanskaya:
Interpretable neural architecture search and transfer learning for understanding CRISPR-Cas9 off-target enzymatic reactions. 1056-1066 - Xinyang Li, Xiaowan Hu, Xingye Chen, Jiaqi Fan, Zhifeng Zhao, Jiamin Wu, Haoqian Wang, Qionghai Dai:
Spatial redundancy transformer for self-supervised fluorescence image denoising. 1067-1080
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