Summary of the Most Noteworthy Scientific Research Results of AI for Science in 2023
Years pass, and the glory is renewed. In the past 2023,AI for Science It brought too many surprises and also planted the seeds for more imagination.
Starting from 2020, AlphaFold The research projects represented by AI for Science have pushed AI for Science to the main stage of AI application. In the past two years, basic disciplines from biomedicine to astronomy and meteorology to materials chemistry have become new battlefields for AI. In this process, the ability of AI has also been embodied as a sharp blade, which can even break the shackles that have troubled people for half a century, greatly accelerating the progress of scientific research.
With such successful examples, after entering 2023, AI's journey in the field of scientific research has become smoother. More and more research teams have begun to seek the help of AI, which has given rise to more high-value results.
As one of the earliest communities to pay attention to AI for Science,"HyperAI Super Neural" continues to record its latest progress by interpreting cutting-edge papersOn the one hand, it aims to share the latest achievements and research methods universally, and on the other hand, it also hopes to enable more teams to see the help of AI in scientific research and contribute to the development of AI for Science in China.
The end of the year and the beginning of the next year are a good time to reflect on the past and learn about the future.We have classified and summarized the cutting-edge papers interpreted by "HyperAI Super Neural" in 2023 to facilitate retrieval by readers in different scientific research fields.
Follow the WeChat public account and reply in the background 「2023 ScienceAI」All papers can be downloaded in one package. In addition, the datasets used in some papers can be downloaded from the official website of "HyperAI Super Neural".
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AI+ Biomedicine
Machine learning model accurately predicts the drug release rate of long-acting injections and accelerates the development of long-acting injections
Machine learning models to accelerate the design of polymeric long-acting injectables
*source：Nature Communications
*author: Researchers at the University of Toronto
*paper:
https://www.nature.com/articles/s41467-022-35343-w
Machine learning algorithm effectively predicts plant resistance to malaria with an accuracy of 0.67
Machine learning enhances prediction of plants as potential sources of antimalarials
*source：Frontiers in Plant Science
*author: Researchers from Kew Gardens, Kew and the University of St Andrews
*paper:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248027
A differentiation system based on live cell bright-field dynamic imaging and machine learning to regulate and optimize the differentiation process of pluripotent stem cells in real time
A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems
*source：Cell Discovery
*author: Zhao Yang's group and Zhang Yu's group from Peking University and Liu Yiyan's group from Beijing Jiaotong University
*paper:
https://www.nature.com/articles/s41421-023-00543-1
Applying machine learning models to predict bio-ink printability and improving prediction rates
Predicting pharmaceutical inkjet printing outcomes using machine learning
*source：International Journal of Pharmaceutics: X
*author: Researchers from the University of Santiago de Compostela and University College London
*paper:
https://www.sciencedirect.com/science/article/pii/S2590156723000257
Using deep learning to screen approximately 7,500 molecules, a new antibiotic was identified that inhibits Acinetobacter baumannii
Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii
*source：Nature Chemical Biology
*author：Researchers from McMaster University and MIT
*Interpretation:AI fights superbugs: McMaster University uses deep learning to discover a new antibiotic abaucin (click to read the original article)
*paper:
https://www.nature.com/articles/s41589-023-01349-8#access-options
Using machine learning, we found three Senolytics, and validated its anti-aging effects in human cell lines
Discovery of Senolytics using machine learning
*source：Nature Communications
*author: Dr. James L. Kirkland, Mayo Clinic, et al.
*paper:
https://www.nature.com/articles/s41467-023-39120-1
Quantifying the amount and location of dopamine release using machine learning
Identifying Neural Signatures of Dopamine Signaling with Machine Learning
*source：ACS Chemical Neuroscience
*author：Research team from the University of California, Berkeley
*Interpretation:"Quantifying" happiness: UC Berkeley uses AI to track dopamine release and brain areas (click to read the original article)
*paper:
https://pubs.acs.org/doi/full/10.1021/acschemneuro.3c00001
Using graph neural networks, safe and effective anti-aging ingredients were screened from hundreds of thousands of compounds.
Discovering small-molecule senolytics with deep neural networks
*source：Nature Aging
*author: Researchers at MIT
*paper:
https://www.nature.com/articles/s43587-023-00415-z
DeepMind Using unsupervised learning to develop AlphaMissense, predicting 71 million gene mutations
Accurate proteome-wide missense variant effect prediction with AlphaMissense
*source：Science
*author: DeepMind
*Interpretation:DeepMind uses unsupervised learning to develop AlphaMissense, predicting 71 million gene mutations (click to read the original article)
*paper:
https://www.science.org/doi/10.1126/science.adg7492
Based on the Transformer regression network, combined with CGMD, the self-assembly characteristics of tens of billions of peptides were predicted.
Deep Learning Empowers the Discovery of Self-Assembling Peptides with Over 10 Trillion Sequences
*source：Advanced Science
*author: Li Wenbin's research group at West Lake University
*paper:
https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202301544
Macformer was developed based on Transformer, and the acyclic drug fitratinib was successfully macrocyclized, providing a new method for drug development.
Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery
*source：Nature Communication
*author：Li Honglin's research group at East China University of Science and Technology
*paper:
https://www.nature.com/articles/s41467-023-40219-8
Developing odor analysis AI based on graph neural network (GNN)
A principal odor map unifies diverse tasks in olfactory perception
*source：Science
*author：Osmo, a branch of Google Research
*paper:
https://www.science.org/doi/full/10.1126/science.ade4401
Develop algorithms for GPCRs-G protein selectivity and investigate the structural basis of selectivity
Rules and mechanisms governing G protein coupling selectivity of GPCRs
*source：Cell Reports
*author: Researchers at the University of Florida
*Interpretation:The University of Florida uses neural networks to decipher GPCR-G protein coupling selectivity (click to read the original article)
*paper:
https://doi.org/10.1016/j.celrep.2023.113173
Fast automatic scanning kit FAST allows AI to automatically identify the scanning position and obtain sample information efficiently and accurately
Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy
*source：Nature Communications
*author：Researchers from Argonne National Laboratory
*paper:
https://www.nature.com/articles/s41467-023-40339-1
AI+ Healthcare
Gradient boosting machine model accurately predicts BPSD subsyndromes
Machine learning‑based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation
*source：Scientifc Reports
*author：Research team from Yonsei University, South Korea
*paper:
https://www.nature.com/articles/s41598-023-35194-5
Based on machine learning, a set of specific diagnostic biomarkers for breast cancer was obtained using feature selection strategies
Robust Feature Selection strategy detects a panel of microRNAs as putative diagnostic biomarkers in Breast Cancer
*source：CIBB 2023
*author: Researchers at the University of Naples Federico II, Italy
*Interpretation:Feature selection strategy: Finding new outlets for detecting breast cancer biomarkers (click to read the original article)
*paper:
https://www.researchgate.net/publication/372083934
Comparison of logistic regression model and three machine learning models to successfully predict the one-year mortality rate of elderly Chinese patients with coronary heart disease and diabetes or impaired glucose tolerance
Machine learning-based models to predict one-year mortality among Chinese older patients with coronary artery disease combined with impaired glucose tolerance or diabetes mellitus
*source：Cardiovascular Diabetology
*author: Researchers from Macheng People's Hospital, Hubei Province, China
*paper:
https://cardiab.biomedcentral.com/articles/10.1186/s12933-023-01854-z
A new brain-computer technology developed using AI allows a stroke patient who had been aphasic for 18 years to speak again
A high-performance neuroprosthesis for speech decoding and avatar control
*source：Nature
*author：UC team
*paper:
https://www.nature.com/articles/s41586-023-06443-4
Commercial AI Lunit reads mammograms as accurately as doctors
Performance of a Breast Cancer Detection AI Algorithm Using the Personal Performance in Mammographic Screening Scheme
*source：Radiology
*author：Research team from the University of Nottingham, UK
*Interpretation:"Pink Killer" wanted poster, AI's ability to read breast X-rays is comparable to that of doctors (click to read original article)
*paper:
https://pubs.rsna.org/doi/10.1148/radiol.223299
The Institute of Genomics of the Chinese Academy of Sciences established an open biomedical imaging archive
Self-supervised learning of hologram reconstruction using physics consistency
*source:bioRxiv
*author：Institute of Genomics, Chinese Academy of Sciences
*paper:
https://www.nature.com/articles/s42256-023-00704-7
RETFound, a retinal image-based model, predicts multiple systemic diseases
A foundation model for generalizable disease detection from retinal images
*source：Nature
*author: Yukun Zhou, PhD student at University College London and Moorfields Eye Hospital, et al.
*paper:
https://www.nature.com/articles/s41586-023-06555-x
Artificial intelligence detection of pancreatic cancer based on deep learning
Large-scale pancreatic cancer detection via non-contrast CT and deep learning
*source：Nature Medicine
*author: Alibaba Damo Academy cooperates with many domestic and foreign medical institutions
*paper:
https://www.nature.com/articles/s41591-023-02640-w
Optimizing the design of triboelectric nanogenerator tactile sensors for text recognition and Braille recognition
Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding
*source：Advanced Science
*author: Yang Geng and Xu Kaichen's research group at Zhejiang University
*Interpretation:Zhejiang University uses SVM to optimize tactile sensors, and the Braille recognition rate reaches 96.12% (click to read the original text)
*paper:
https://onlinelibrary.wiley.com/doi/10.1002/advs.202303949
AI+ Materials Chemistry
Combining multiple deep learning architectures to determine the internal structure of materials from surface observations
Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information
*source：Advanced Materials
*author: Researchers at MIT
*Interpretation:Fill in the blanks in the material space: MIT uses deep learning to solve non-destructive testing problems (click to read the original article)
*paper:
https://onlinelibrary.wiley.com/doi/full/10.1002/adma.202301449
Combining deep neural networks and natural language processing to develop corrosion-resistant alloys
Enhancing corrosion-resistant alloy design through natural language processing and deep learning
*source：Science Advances
*author: Researchers at the Max Planck Institute for Iron Research in Germany
*paper:
https://www.science.org/doi/10.1126/sciadv.adg7992
Based on the machine learning model, AI is trained to extract the structural parameters of porous materials to predict the water adsorption isotherm
Machine learning-assisted prediction of water adsorption isotherms and cooling performance
*source：Journal of Materials Chemistry A
*author：Li Song's research group at Huazhong University of Science and Technology
*paper:
https://pubs.rsc.org/en/content/articlelanding/2023/TA/D3TA03586G
Field-induced recursive embedding atomic neural network FIREANN accurately describes the changes in external field intensity and direction
Universal machine learning for the response of atomic systems to external fields
*source：Nature Communication
*author：Jiang Bin's research group at the University of Science and Technology of China
*paper:
https://www.nature.com/articles/s41467-023-42148-y
DeepMind releases deep learning tool GNoME, discovers 2.2 million new crystals
Scaling deep learning for materials discovery
*source: DeepMind
*author：Nature
*Interpretation:800 years ahead of humans? DeepMind releases GNoME, using deep learning to predict 2.2 million new crystals (click to read original article)
*paper:
https://www.nature.com/articles/s41586-023-06735-9
SEN machine learning model for high-precision material property prediction
Material symmetry recognition and property prediction accomplished by crystal capsule representation
*source：Nature Communications
*author：Li Huashan and Wang Biao's research group at Sun Yat-sen University
*paper:
https://www.nature.com/articles/s41467-023-40756-2
RetroExplainer algorithm for retrosynthetic prediction based on deep learning
Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
*source：Nature Communications
*author：Shandong University, University of Electronic Science and Technology of China Research Group
*paper:
https://www.nature.com/articles/s41467-023-41698-5
Optimizing cocatalysts for BiVO(4) photoanodes using machine learning
A comprehensive machine learning strategy for designing high-performance photoanode catalysts
*source：Journal of Materials Chemistry A
*author：Zhu Hongwei Research Group, Tsinghua University
*paper:
https://pubs.rsc.org/en/content/articlelanding/2023/TA/D3TA04148D
AI+ Plant and Animal Sciences
Population genetics based on machine learning reveals the formation mechanism of grape flavor
Adaptive and maladaptive introgression in grapevine domestication
*source：Proceedings of the National Academy of Sciences
*author：Researchers from the Shenzhen Agricultural Genomics Center of the Chinese Academy of Agricultural Sciences
*paper:
https://www.pnas.org/doi/abs/10.1073/pnas.2222041120
Using Python API and Computer Vision API to monitor the blooming of cherry blossoms in Japan
The spatiotemporal signature of cherry blossom flowering across Japan revealed via analysis of social network site images
*source：Flora
*author：Research team from Monash University, Australia
*paper:
https://www.sciencedirect.com/science/article/abs/pii/S0367253023001019
Review: Using AI to start bioinformatics research more efficiently
In addition to well-known bioinformatics advances such as AlphaFold, AI has a wealth of application cases in biological fields such as homology search, multiple alignment and phylogenetic construction, genome sequence analysis, and gene discovery. As a biological researcher, being able to skillfully integrate machine learning tools into data analysis will surely accelerate scientific discovery and improve scientific research efficiency.
*Recommended Reading:Bioinformatics | Start research more efficiently with AI (click to read original article)
A deep learning method based on a twin network automatically captures the embryonic development process
Uncovering developmental time and tempo using deep learning
*source：Nature Methods
*author: Systems biologist Patrick Müller and researchers at the University of Konstanz
*Interpretation:AI combined with embryos? Systems biologist Patrick Müller uses twin networks to study zebrafish embryos (click to read the original article)
*paper:
https://www.nature.com/articles/s41592-023-02083-8
Using more than 50,000 photos, we trained ArcFace, a facial recognition algorithm Classification Head multi-species image recognition model
A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species
*source：Methods in Ecology and Evolution
*author: Researchers from the University of Hawaii
*paper:
https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14167
Using data from 628 Labrador retrievers, three models were compared to identify behavioral characteristics that affect the performance of scent detection dogs.
Machine learning prediction and classification of behavioral selection in a canine olfactory detection program
*source：Scientific Reports
*author: Researchers from the Abigail Wexner Research Institute at Nationwide Children's Hospital and Rocky Vista University
*paper:
https://www.nature.com/articles/s41598-023-39112-7
AI camera alert system accurately distinguishes tigers from other species
Accurate proteome-wide missense variant effect prediction with AlphaMissense
*source：BioScience
*author: Researchers at Clemson University
*paper:
https://www.science.org/doi/10.1126/science.adg7492
BirdFlow uses computer modeling and eBird datasets to accurately predict the flight paths of migratory birds
BirdFlow: Learning seasonal bird movements from eBird data
*source：Methods in Ecology and Evolution
*author: Researchers from the University of Massachusetts and Cornell University
*paper:
https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14052
AI+ Agriculture, Forestry and Animal Husbandry
Using computer vision + deep learning to develop a dairy cow lameness detection system with an accuracy of 94%-100%
Deep learning pose estimation for multi-cattle lameness detection
*source：Nature
*author: Researchers from Newcastle University and Fera Scientific Ltd
*Interpretation:Newcastle University develops real-time, automated dairy cow lameness detection system using computer vision and deep learning
*paper:
https://www.nature.com/articles/s41598-023-31297-1
Drone + AI image analysis to detect forest pests
Testing early detection of pine processionary moth Thaumetopoea pityocampa nests using UAV-based methods
*source: NeoBiota
*author：Research Team of University of Lisbon
*Interpretation:Drone + AI image analysis: University of Lisbon efficiently detects forest pests (click to read original article)
*paper:
https://neobiota.pensoft.net/article/95692/
Combining laboratory observations with machine learning, they show that ultrasound waves emitted by tomato and tobacco plants under stress can propagate through the air
Sounds emitted by plants under stress are airborne and informative
*source：Cell
*author：Researchers from Tel Aviv University, Israel
*Interpretation:Tomatoes will "scream" under pressure. Tel Aviv University found that the plant kingdom is not silent (click to read the original article)
*paper:
https://doi.org/10.1016/j.cell.2023.03.009
Using the YOLOv5 algorithm, a model is designed to monitor sow posture and piglet birth
Sow Farrowing Early Warning and Supervision for Embedded Board Implementations
*source：Sensors
*author：Nanjing Agricultural University Research Team
*Interpretation:Sows know when to give birth. This time, NNU uses NVIDIA's edge AI Jetson (click to read original article)
*paper:
https://www.mdpi.com/1424-8220/23/2/727
Using convolutional neural networks to quickly and accurately count rice yields
Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images
*source：Plant Phenomics
*author：Researchers from Kyoto University
*paper:
https://spj.science.org/doi/10.34133/plantphenomics.0073
A systematic process for collecting plant phenotypic data using drones to predict optimal harvest dates
Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income
*source：Plant Phenomics
*author: Researchers from the University of Tokyo and Chiba University
*Interpretation:The University of Tokyo uses AI and drones to predict the best harvest date for crops (click to read the original article)
*paper:
https://spj.science.org/doi/10.34133/plantphenomics.0086#body-ref-B4
AI+ Meteorological Research
CSU-MLP, a machine learning model based on random forest, accurately forecasts severe weather in the medium term (4-8 days).
A new paradigm for medium-range severe weather forecasts: probabilistic random forest-based predictions
*source：Weather and Forecasting
*author: Researchers from Colorado State University and the National Oceanic and Atmospheric Administration
*paper:
https://arxiv.org/abs/2208.02383
Using global storm-analyzing simulations and machine learning to create a new algorithm to accurately predict extreme precipitation
Implicit learning of convective organization explains precipitation stochasticity
*source: PNAS
*author：Leap Lab, Columbia University
*paper:
https://www.pnas.org/doi/10.1073/pnas.2216158120
Overview: Gathering data from hailstorm centers to predict extreme weather using big models
As early as 2021, Alibaba Cloud revealed that the DAMO Academy and the National Meteorological Center jointly developed an AI algorithm for weather forecasting and successfully predicted multiple severe convective weather events. In September of the same year, Deepmind published an article in Nature, using a deep generative model to make real-time forecasts of rainfall.
At the beginning of 2023, Deepmind officially launched GraphCast, which can predict the global weather for the next 10 days with a resolution of 0.25° within one minute. In April, Nanjing University of Information Science and Technology and Shanghai Artificial Intelligence Laboratory jointly developed the "Fengwu" weather forecast model, which has a further reduction in error compared to GraphCast.
Subsequently, Huawei launched the "Pangu" meteorological model. Due to the introduction of a three-dimensional neural network in the model, the prediction accuracy of "Pangu" exceeded the most accurate NWP prediction system for the first time. Recently, Tsinghua University and Fudan University have successively released the "NowCastNet" and "Fuxi" models.
*Recommended Reading:Hailstorm Center collects data, large models support extreme weather forecasts, and "Storm Chasers" are on the rise (click to read original article)
Overview: Data-driven machine learning weather forecasting models
Numerical weather forecasting is the mainstream method of weather forecasting. It uses numerical integration to solve the state of the earth system grid by grid, which is a process of deductive reasoning. Since 2022, machine learning models in the field of weather forecasting have made a series of breakthroughs, some of which can rival the high-precision forecasts of the European Center for Medium-Range Weather Forecasts.
*Recommended Reading:Machine Learning vs. Numerical Weather Forecasting, How AI Changes the Existing Weather Forecasting Model (Click to Read Original Article)
AI + Astronomy
Using simulated data to train computer vision algorithms to sharpen astronomical images
Galaxy image deconvolution for weak gravitational lensing with unrolled plug-and-play ADMM
*source: Monthly Notices of the Royal Astronomical Society
*author：Research team from Tsinghua University and Northwestern University
*paper:
https://www.nature.com/articles/s41421-023-00543-1
The PRIMO algorithm learns the laws of light propagation around a black hole and reconstructs a clearer black hole image
The Image of the M87 Black Hole Reconstructed with PRIMO
*source:The Astrophysical Journal Letters
*author：Princeton Institute for Advanced Study research team
*paper:
https://iopscience.iop.org/article/10.3847/2041-8213/acc32d/pdf
Using the unsupervised machine learning algorithm Astronomaly, we found anomalies that were previously overlooked
Astronomally at Scale: Searching for Anomalies Amongst 4 Million Galaxies
*source: arXiv
*author：Researchers at the University of the Western Cape
*Interpretation:Astronomaly: Using CNN and active learning to identify anomalies in 4 million galaxy images (click to read the original article)
*paper:
https://arxiv.org/abs/2309.08660
AI+ Energy and Environment
Use machine learning to discover mineral combination patterns to predict mineral locations
Predicting new mineral occurrences and planetary analog environments via mineral association analysis
*source：PNAS Nexus
*author: Researchers from the Carnegie Institution for Science in Washington and the University of Arizona
*paper:
https://academic.oup.com/pnasnexus/article/2/5/pgad110/7163824?login=true
Using physical and machine learning models to predict pollution losses caused by the accumulation of dirt and other materials on the surface of photovoltaic panels in arid climates
Characterizing soiling losses for photovoltaic systems in dry climates: A case study in Cyprus
*source：Solar Energy
*author: Researchers at the University of Cyprus
*paper:
https://www.sciencedirect.com/science/article/pii/S0038092X23001883
Predicting the emission of harmful amine gases in the carbon capture process through machine learning methods
Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant
*source：ScienceAdvances
*author: A research team from EPFL and Heriot-Watt University
*Interpretation:8-year battle for a post-90s PhD student: Using machine learning to boost chemical research (click to read original article)
*paper:
https://www.science.org/doi/10.1126/sciadv.adc9576
AI + Natural Disasters
Superimposable neural network to analyze influencing factors in natural disasters
Landslide susceptibility modeling by interpretable neural network
*source：Communications Earth & Environment
*author: Researchers at the University of California, Los Angeles
*Interpretation:The black box becomes transparent: UCLA develops an interpretable neural network SNN to predict landslides (click to read the original article)
*paper:
https://www.nature.com/articles/s43247-023-00806-5
Using explainable AI, we analyzed different geographical factors in Gippsland, Australia, and obtained a local wildfire probability distribution map.
Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model
*source: ScienceDirect
*author：Researchers from Australian National University and University of Technology Sydney
*paper:
https://www.sciencedirect.com/science/article/pii/S0048969723016224
other
Direct and inverse problems of AI in meta-optics, data analysis based on metasurface systems
Artificial Intelligence in Meta-optics
*source：ACS Publications
*author：Researchers from City University of Hong Kong
*Interpretation:AI is adding to the game, and super-optics is entering a booming era (click to read the original article)
*paper:
https://pubs.acs.org/doi/10.1021/acs.chemrev.2c00012
Ithaca assists epigraphers with text restoration, chronological and geographical attribution
Restoring and attributing ancient texts using deep neural networks
*source：Nature
*author: Researchers from DeepMind and University of Venice Foscari
*Interpretation:New interpretation of the millennium code, DeepMind develops Ithaca to decipher Greek inscriptions (click to read the original article)
*paper:
https://www.nature.com/articles/s41586-022-04448-z
30 scholars jointly published a Nature review, reviewing 10 years and deconstructing how AI reshaped the scientific research paradigm
Hanchen Wang, a postdoctoral fellow from the School of Computer Science and Genomics at Stanford University, Tianfan Fu from the Department of Computer Science and Engineering at the Georgia Institute of Technology, and Yuanqi Du from the Department of Computer Science at Cornell University, along with 30 others, reviewed the role of AI in basic scientific research over the past decade and pointed out the challenges and shortcomings that still exist.
*Recommended Reading:30 scholars jointly published a Nature review, reviewing 10 years and deconstructing how AI reshaped the scientific research paradigm (click to read the original article)
Policy: The Ministry of Science and Technology, together with the National Natural Science Foundation of China, launched the "AI for Science" special deployment project
On March 27, Xinhua News Agency reported that in order to implement the national "New Generation Artificial Intelligence Development Plan", the Ministry of Science and Technology, together with the National Natural Science Foundation of China, recently launched the "Artificial Intelligence Driven Scientific Research" (AI for Science) special deployment work.
This time, my country has laid out the AI for Science cutting-edge technology research and development system, which will closely integrate key issues in basic disciplines such as mathematics, physics, chemistry, and astronomy, and focus on the scientific research needs in key areas such as drug development, gene research, biological breeding, and new material development. In this regard, Xu Bo, director of the Institute of Automation of the Chinese Academy of Sciences, explained that the fields of new drug creation, gene research, biological breeding, and new material development are important directions with urgent needs, outstanding progress, and representativeness in the combination of artificial intelligence and scientific research.
The above is the cutting-edge paper that "HyperAI Super Neural" will interpret for you in 2023.Follow the WeChat official account and reply "2023 ScienceAI" in the background to download all the papers in a package.
In 2024, we will continue to focus on the latest scientific research results and related applications of AI for Science, so stay tuned~