Editing Neural network (machine learning) (section)

== Applications ==
Because of their ability to reproduce and model nonlinear processes, artificial neural networks have found applications in many disciplines. These include:
* [[Function approximation]],<ref>{{cite book |last1=Esch |first1=Robin |title=Handbook of Applied Mathematics |chapter=Functional Approximation |date=1990 |publisher=Springer US |location=Boston, MA |isbn=978-1-4684-1423-3 |pages=928–987 |doi=10.1007/978-1-4684-1423-3_17 |edition=Springer US}}</ref> or [[regression analysis]],<ref>{{cite book |last1=Sarstedt |first1=Marko |last2=Moo |first2=Erik |title=A Concise Guide to Market Research |chapter=Regression Analysis |series=Springer Texts in Business and Economics |date=2019 |publisher=Springer Berlin Heidelberg |pages=209–256 |doi=10.1007/978-3-662-56707-4_7 |isbn=978-3-662-56706-7 |s2cid=240396965 |chapter-url=https://link.springer.com/chapter/10.1007/978-3-662-56707-4_7#Sec1 |access-date=20 March 2023 |archive-date=20 March 2023 |archive-url=https://web.archive.org/web/20230320212723/https://link.springer.com/chapter/10.1007/978-3-662-56707-4_7#Sec1 |url-status=live }}</ref> (including [[Time series#Prediction and forecasting|time series prediction]], [[fitness approximation]],<ref>{{cite book |last1=Tian |first1=Jie |last2=Tan |first2=Yin |last3=Sun |first3=Chaoli |last4=Zeng |first4=Jianchao |last5=Jin |first5=Yaochu |title=2016 IEEE Symposium Series on Computational Intelligence (SSCI) |chapter=A self-adaptive similarity-based fitness approximation for evolutionary optimization |date=December 2016 |pages=1–8 |doi=10.1109/SSCI.2016.7850209 |isbn=978-1-5090-4240-1 |s2cid=14948018 |chapter-url=https://ieeexplore.ieee.org/document/7850209 |access-date=22 March 2023 |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519082200/https://ieeexplore.ieee.org/document/7850209 |url-status=live }}</ref> and modeling)
* [[Data processing]]<ref>{{cite book |last1=Alaloul |first1=Wesam Salah |last2=Qureshi |first2=Abdul Hannan |title=Dynamic Data Assimilation – Beating the Uncertainties |chapter=Data Processing Using Artificial Neural Networks |date=2019 |doi=10.5772/intechopen.91935 |isbn=978-1-83968-083-0 |s2cid=219735060 |chapter-url=https://www.intechopen.com/chapters/71673 |access-date=20 March 2023 |archive-date=20 March 2023 |archive-url=https://web.archive.org/web/20230320212722/https://www.intechopen.com/chapters/71673 |url-status=live }}</ref> (including filtering, clustering, [[blind source separation]],<ref>{{cite book |last1=Pal |first1=Madhab |last2=Roy |first2=Rajib |last3=Basu |first3=Joyanta |last4=Bepari |first4=Milton S. |title=2013 International Conference Oriental COCOSDA held jointly with 2013 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE) |chapter=Blind source separation: A review and analysis |date=2013 |publisher=IEEE |pages=1–5 |doi=10.1109/ICSDA.2013.6709849 |isbn=978-1-4799-2378-6 |s2cid=37566823 |chapter-url=https://ieeexplore.ieee.org/document/6709849 |access-date=20 March 2023 |archive-date=20 March 2023 |archive-url=https://web.archive.org/web/20230320212720/https://ieeexplore.ieee.org/document/6709849 |url-status=live }}</ref> and compression)
* [[Nonlinear system identification]]<ref name="SAB1">{{cite book |last=Billings |first=S. A. |title=Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains |publisher=Wiley |year=2013 |isbn=978-1-119-94359-4 }}</ref> and control (including vehicle control, trajectory prediction,<ref>{{cite journal|last1=Zissis|first1=Dimitrios|title=A cloud based architecture capable of perceiving and predicting multiple vessel behaviour|journal=Applied Soft Computing|date=October 2015|volume=35|doi=10.1016/j.asoc.2015.07.002|pages=652–661|url=https://zenodo.org/record/848743|access-date=18 July 2019|archive-date=26 July 2020|archive-url=https://web.archive.org/web/20200726091505/https://zenodo.org/record/848743|url-status=live}}</ref> [[adaptive control]], [[process control]], and [[natural resource management]])
* [[Pattern recognition]] (including radar systems, [[Facial recognition system|face identification]], signal classification,<ref>{{cite journal|last=Sengupta |first=Nandini|author2=Sahidullah, Md|author3=Saha, Goutam|title=Lung sound classification using cepstral-based statistical features|journal=Computers in Biology and Medicine|date=August 2016|volume=75|issue=1 |pages=118–129|doi=10.1016/j.compbiomed.2016.05.013 |pmid=27286184}}</ref> [[novelty detection]], [[3D reconstruction]],<ref>Choy, Christopher B., et al. "[https://arxiv.org/abs/1604.00449 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction] {{Webarchive|url=https://web.archive.org/web/20200726091721/https://arxiv.org/abs/1604.00449 |date=26 July 2020 }}." European conference on computer vision. Springer, Cham, 2016.</ref> object recognition, and sequential decision making<ref name ="TurekNeuralNet">{{cite journal|author=Turek, Fred D.|title=Introduction to Neural Net Machine Vision|url=http://www.vision-systems.com/articles/print/volume-12/issue-3/features/introduction-to-neural-net-machine-vision.html|access-date=5 March 2013|journal=Vision Systems Design|date=March 2007|volume=12|number=3|archive-date=16 May 2013|archive-url=https://web.archive.org/web/20130516124148/http://www.vision-systems.com/articles/print/volume-12/issue-3/features/introduction-to-neural-net-machine-vision.html|url-status=live}}</ref>)
* Sequence recognition (including [[Gesture recognition|gesture]], [[Speech recognition|speech]], and handwritten and printed text recognition<ref>{{Cite book|last1=Maitra|first1=Durjoy S.|last2=Bhattacharya|first2=Ujjwal|last3=Parui|first3=Swapan K.|title=2015 13th International Conference on Document Analysis and Recognition (ICDAR)|chapter=CNN based common approach to handwritten character recognition of multiple scripts|date=August 2015|chapter-url=https://ieeexplore.ieee.org/document/7333916|pages=1021–1025|doi=10.1109/ICDAR.2015.7333916|isbn=978-1-4799-1805-8|s2cid=25739012|access-date=18 March 2021|archive-date=16 October 2023|archive-url=https://web.archive.org/web/20231016190918/https://ieeexplore.ieee.org/document/7333916|url-status=live}}</ref>)
* Sensor data analysis<ref>{{cite journal|last=Gessler|first=Josef|title=Sensor for food analysis applying impedance spectroscopy and artificial neural networks|journal=RiuNet UPV|date=August 2021|issue=1|pages=8–12|url=https://riunet.upv.es/handle/10251/174498|access-date=21 October 2021|archive-date=21 October 2021|archive-url=https://web.archive.org/web/20211021115443/https://riunet.upv.es/handle/10251/174498|url-status=live}}</ref> (including [[image analysis]])
* [[Robotics]] (including directing manipulators and [[prosthesis|prostheses]])
* [[Data mining]] (including [[knowledge discovery in databases]])
* Finance<ref>{{cite journal|last1=French |first1=Jordan |title=The time traveller's CAPM|journal=Investment Analysts Journal|volume=46|issue=2|pages=81–96 |doi=10.1080/10293523.2016.1255469|year=2016|s2cid=157962452}}</ref> (such as [[ex-ante]] models for specific financial long-run forecasts and [[artificial financial market]]s)
* [[Quantum chemistry]]<ref name="Balabin_2009">{{Cite journal|journal=[[J. Chem. Phys.]] |volume=131 |issue=7 |page=074104 |doi=10.1063/1.3206326 |title=Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies |year=2009 |author1=Roman M. Balabin |author2=Ekaterina I. Lomakina |pmid=19708729|bibcode=2009JChPh.131g4104B}}</ref>
* [[General game playing]]<ref>{{cite journal |last1=Silver |first1=David |display-authors=etal |year=2016 |title=Mastering the game of Go with deep neural networks and tree search |url=http://web.iitd.ac.in/~sumeet/Silver16.pdf |journal=Nature |volume=529 |issue=7587 |pages=484–489 |doi=10.1038/nature16961 |pmid=26819042 |bibcode=2016Natur.529..484S |s2cid=515925 |access-date=31 January 2019 |archive-date=23 November 2018 |archive-url=https://web.archive.org/web/20181123112812/http://web.iitd.ac.in/~sumeet/Silver16.pdf |url-status=live }}</ref>
* [[Generative AI]]<ref>{{Cite news |last=Pasick |first=Adam |date=27 March 2023 |title=Artificial Intelligence Glossary: Neural Networks and Other Terms Explained |language=en-US |work=The New York Times |url=https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html |access-date=22 April 2023 |issn=0362-4331 |archive-date=1 September 2023 |archive-url=https://web.archive.org/web/20230901183440/https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html |url-status=live }}</ref>
* [[Data visualization]]
* [[Machine translation]]
* Social network filtering<ref>{{Cite news|url=https://www.wsj.com/articles/facebook-boosts-a-i-to-block-terrorist-propaganda-1497546000|title=Facebook Boosts A.I. to Block Terrorist Propaganda|last=Schechner|first=Sam|date=15 June 2017|work=[[The Wall Street Journal]]|access-date=16 June 2017|issn=0099-9660|archive-date=19 May 2024|archive-url=https://web.archive.org/web/20240519082135/https://www.wsj.com/articles/facebook-boosts-a-i-to-block-terrorist-propaganda-1497546000|url-status=live}}</ref>
* [[E-mail spam]] filtering
* [[Medical diagnosis]]<ref name=Ciaramella>{{cite book|last1=Ciaramella|first1=Alberto|author-link=Alberto Ciaramella|last2=Ciaramella|first2=Marco|title=Introduction to Artificial Intelligence: from data analysis to generative AI|date=2024|publisher=Intellisemantic Editions|isbn=978-8-8947-8760-3}}</ref>

ANNs have been used to diagnose several types of cancers<ref>{{cite journal|last=Ganesan|first=N |title=Application of Neural Networks in Diagnosing Cancer Disease Using Demographic Data |journal=International Journal of Computer Applications|volume=1|issue=26|pages=81–97 |bibcode=2010IJCA....1z..81G|year=2010|doi=10.5120/476-783|doi-access=free}}</ref><ref>{{cite journal |url=http://www.lcc.uma.es/~jja/recidiva/042.pdf|title=Artificial Neural Networks Applied to Outcome Prediction for Colorectal Cancer Patients in Separate Institutions|journal=Lancet|volume=350|issue=9076 |pages=469–72|last=Bottaci|first=Leonardo|publisher=The Lancet|pmid=9274582|year=1997|doi=10.1016/S0140-6736(96)11196-X|s2cid=18182063|access-date=2 May 2012|archive-date=23 November 2018|archive-url=https://web.archive.org/web/20181123170444/http://www.lcc.uma.es/~jja/recidiva/042.pdf}}</ref> and to distinguish highly invasive cancer cell lines from less invasive lines using only cell shape information.<ref>{{cite journal|last1=Alizadeh|first1=Elaheh|last2=Lyons|first2=Samanthe M|last3=Castle|first3=Jordan M|last4=Prasad|first4=Ashok|date=2016|title=Measuring systematic changes in invasive cancer cell shape using Zernike moments|url=http://pubs.rsc.org/en/Content/ArticleLanding/2016/IB/C6IB00100A|journal=Integrative Biology|volume=8|issue=11|pages=1183–1193|doi=10.1039/C6IB00100A|pmid=27735002|access-date=28 March 2017|archive-date=19 May 2024|archive-url=https://web.archive.org/web/20240519082133/https://pubs.rsc.org/en/Content/ArticleLanding/2016/IB/C6IB00100A|url-status=live}}</ref><ref>{{cite journal |last1=Lyons|first1=Samanthe|date=2016|title=Changes in cell shape are correlated with metastatic potential in murine|journal=Biology Open|volume=5|issue=3|pages=289–299|doi=10.1242/bio.013409|pmid=26873952 |pmc=4810736}}</ref>

ANNs have been used to accelerate reliability analysis of infrastructures subject to natural disasters<ref>{{Cite journal|last1=Nabian|first1=Mohammad Amin|last2=Meidani|first2=Hadi|date=28 August 2017|title=Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks|journal=Computer-Aided Civil and Infrastructure Engineering|volume=33|issue=6|pages=443–458|arxiv=1708.08551|doi=10.1111/mice.12359 |bibcode=2017arXiv170808551N |s2cid=36661983}}</ref><ref>{{Cite journal|last1=Nabian|first1=Mohammad Amin|last2=Meidani|first2=Hadi|date=2018|title=Accelerating Stochastic Assessment of Post-Earthquake Transportation Network Connectivity via Machine-Learning-Based Surrogates|url=https://trid.trb.org/view/1496617|journal=Transportation Research Board 97th Annual Meeting|access-date=14 March 2018|archive-date=9 March 2018|archive-url=https://web.archive.org/web/20180309120108/https://trid.trb.org/view/1496617|url-status=live}}</ref> and to predict foundation settlements.<ref>{{Cite journal|last1=Díaz|first1=E.|last2=Brotons|first2=V. |last3=Tomás|first3=R.|date=September 2018|title=Use of artificial neural networks to predict 3-D elastic settlement of foundations on soils with inclined bedrock|journal=Soils and Foundations|volume=58|issue=6 |pages=1414–1422 |doi=10.1016/j.sandf.2018.08.001|bibcode=2018SoFou..58.1414D |issn=0038-0806|hdl=10045/81208|doi-access=free|hdl-access=free}}</ref> It can also be useful to mitigate flood by the use of ANNs for modelling rainfall-runoff.<ref>{{Cite journal |last1=Tayebiyan |first1=A. |last2=Mohammad |first2=T. A. |last3=Ghazali |first3=A. H. |last4=Mashohor |first4=S. |title=Artificial Neural Network for Modelling Rainfall-Runoff |url=http://www.pertanika.upm.edu.my/pjtas/browse/regular-issue?article=JST-0566-2015 |journal=Pertanika Journal of Science & Technology |volume=24 |issue=2 |pages=319–330 |access-date=17 May 2023 |archive-date=17 May 2023 |archive-url=https://web.archive.org/web/20230517014047/http://www.pertanika.upm.edu.my/pjtas/browse/regular-issue?article=JST-0566-2015 |url-status=live }}</ref> ANNs have also been used for building black-box models in [[geoscience]]: [[hydrology]],<ref>{{Cite journal |first=Rao S.|last=Govindaraju |date=1 April 2000|title=Artificial Neural Networks in Hydrology. I: Preliminary Concepts|journal=Journal of Hydrologic Engineering|volume=5|issue=2|pages=115–123|doi=10.1061/(ASCE)1084-0699(2000)5:2(115)|citeseerx=<!--10.1.1.127.3861-->}}</ref><ref>{{Cite journal|first=Rao S.|last=Govindaraju|date=1 April 2000|title=Artificial Neural Networks in Hydrology. II: Hydrologic Applications|journal=Journal of Hydrologic Engineering|volume=5|issue=2 |pages=124–137 |doi=10.1061/(ASCE)1084-0699(2000)5:2(124)}}</ref> ocean modelling and [[coastal engineering]],<ref>{{Cite journal|last1=Peres|first1=D. J.|last2=Iuppa|first2=C.|last3=Cavallaro|first3=L.|last4=Cancelliere |first4=A. |last5=Foti|first5=E.|date=1 October 2015|title=Significant wave height record extension by neural networks and reanalysis wind data|journal=Ocean Modelling|volume=94|pages=128–140 |doi=10.1016/j.ocemod.2015.08.002 |bibcode=2015OcMod..94..128P}}</ref><ref>{{Cite journal|last1=Dwarakish|first1=G. S.|last2=Rakshith|first2=Shetty|last3=Natesan|first3=Usha|date=2013|title=Review on Applications of Neural Network in Coastal Engineering|journal=Artificial Intelligent Systems and Machine Learning|url=http://www.ciitresearch.org/dl/index.php/aiml/article/view/AIML072013007|volume=5|issue=7|pages=324–331|access-date=5 July 2017|archive-date=15 August 2017|archive-url=https://web.archive.org/web/20170815185634/http://www.ciitresearch.org/dl/index.php/aiml/article/view/AIML072013007|url-status=live}}</ref> and [[geomorphology]].<ref>{{Cite journal |last1=Ermini|first1=Leonardo|last2=Catani |first2=Filippo|last3=Casagli|first3=Nicola|date=1 March 2005|title=Artificial Neural Networks applied to landslide susceptibility assessment|journal=Geomorphology|series=Geomorphological hazard and human impact in mountain environments|volume=66|issue=1|pages=327–343|doi=10.1016/j.geomorph.2004.09.025 |bibcode=2005Geomo..66..327E}}</ref> ANNs have been employed in [[Computer security|cybersecurity]], with the objective to discriminate between legitimate activities and malicious ones. For example, machine learning has been used for classifying Android malware,<ref>{{Cite book|last1=Nix|first1=R.|last2=Zhang |first2=J.|title=2017 International Joint Conference on Neural Networks (IJCNN) |chapter=Classification of Android apps and malware using deep neural networks |date=May 2017 |pages=1871–1878|s2cid=8838479 |doi=10.1109/IJCNN.2017.7966078|isbn=978-1-5090-6182-2}}</ref> for identifying domains belonging to threat actors and for detecting URLs posing a security risk.<ref>{{Cite web|title=Detecting Malicious URLs |website=The systems and networking group at UCSD |url=http://www.sysnet.ucsd.edu/projects/url/|access-date=15 February 2019|archive-date=14 July 2019|archive-url=https://web.archive.org/web/20190714201955/http://www.sysnet.ucsd.edu/projects/url/}}</ref> Research is underway on ANN systems designed for penetration testing, for detecting botnets,<ref>{{Citation |last1=Homayoun|first1=Sajad|title=BoTShark: A Deep Learning Approach for Botnet Traffic Detection |date=2018|work=Cyber Threat Intelligence|pages=137–153|editor-last=Dehghantanha|editor-first=Ali |series=Advances in Information Security|publisher=Springer International Publishing|doi=10.1007/978-3-319-73951-9_7|isbn=978-3-319-73951-9|last2=Ahmadzadeh |first2=Marzieh|last3=Hashemi|first3=Sattar |last4=Dehghantanha|first4=Ali|last5=Khayami|first5=Raouf|volume=70 |editor2-last=Conti|editor2-first=Mauro|editor3-last=Dargahi|editor3-first=Tooska}}</ref> credit cards frauds<ref>{{Cite book |last1=Ghosh|last2=Reilly |title=Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences HICSS-94 |chapter=Credit card fraud detection with a neural-network |s2cid=13260377 |date=January 1994|volume=3|pages=621–630|doi=10.1109/HICSS.1994.323314|isbn=978-0-8186-5090-1}}</ref> and network intrusions.

ANNs have been proposed as a tool to solve [[partial differential equation]]s in physics<ref>{{cite web|last=Ananthaswamy|first=Anil|date=19 April 2021|title=Latest Neural Nets Solve World's Hardest Equations Faster Than Ever Before|url=https://www.quantamagazine.org/new-neural-networks-solve-hardest-equations-faster-than-ever-20210419/|access-date=12 May 2021|website=Quanta Magazine|archive-date=19 May 2024|archive-url=https://web.archive.org/web/20240519082138/https://www.quantamagazine.org/new-neural-networks-solve-hardest-equations-faster-than-ever-20210419/|url-status=live}}</ref><ref>{{cite web|title=AI has cracked a key mathematical puzzle for understanding our world|url=https://www.technologyreview.com/2020/10/30/1011435/ai-fourier-neural-network-cracks-navier-stokes-and-partial-differential-equations/|access-date=19 November 2020|website=MIT Technology Review|archive-date=19 May 2024|archive-url=https://web.archive.org/web/20240519082138/https://www.technologyreview.com/2020/10/30/1011435/ai-fourier-neural-network-cracks-navier-stokes-and-partial-differential-equations/|url-status=live}}</ref><ref>{{cite web|title=Caltech Open-Sources AI for Solving Partial Differential Equations|url=https://www.infoq.com/news/2020/12/caltech-ai-pde/|access-date=20 January 2021|website=InfoQ|archive-date=25 January 2021|archive-url=https://web.archive.org/web/20210125233952/https://www.infoq.com/news/2020/12/caltech-ai-pde/|url-status=live}}</ref> and simulate the properties of many-body [[open quantum system]]s.<ref>{{cite journal |last1=Nagy |first1=Alexandra |title=Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems |journal=[[Physical Review Letters]] |volume=122 |issue=25 |page=250501 |date=28 June 2019 |doi=10.1103/PhysRevLett.122.250501 |pmid=31347886 |bibcode=2019PhRvL.122y0501N |arxiv=1902.09483 |s2cid=119074378 }}</ref><ref>{{Cite journal|last1=Yoshioka|first1=Nobuyuki|last2=Hamazaki|first2=Ryusuke|date=28 June 2019|title=Constructing neural stationary states for open quantum many-body systems|journal=Physical Review B|volume=99|issue=21 |page=214306|doi=10.1103/PhysRevB.99.214306|bibcode=2019PhRvB..99u4306Y|arxiv=1902.07006|s2cid=119470636}}</ref><ref>{{Cite journal|last1=Hartmann|first1=Michael J.|last2=Carleo|first2=Giuseppe |date=28 June 2019|title=Neural-Network Approach to Dissipative Quantum Many-Body Dynamics|journal=Physical Review Letters|volume=122|issue=25|page=250502|doi=10.1103/PhysRevLett.122.250502|pmid=31347862 |bibcode=2019PhRvL.122y0502H|arxiv=1902.05131|s2cid=119357494}}</ref><ref>{{Cite journal|last1=Vicentini |first1=Filippo|last2=Biella|first2=Alberto|last3=Regnault|first3=Nicolas|last4=Ciuti|first4=Cristiano|date=28 June 2019 |title=Variational Neural-Network Ansatz for Steady States in Open Quantum Systems |journal=Physical Review Letters|volume=122|issue=25|page=250503|doi=10.1103/PhysRevLett.122.250503 |pmid=31347877 |bibcode=2019PhRvL.122y0503V |arxiv=1902.10104|s2cid=119504484}}</ref> In brain research ANNs have studied short-term behavior of [[biological neuron models|individual neurons]],<ref>{{cite journal |author=Forrest MD |title=Simulation of alcohol action upon a detailed Purkinje neuron model and a simpler surrogate model that runs >400 times faster |journal=BMC Neuroscience |volume=16 |issue=27 |page=27 |date=April 2015 |doi=10.1186/s12868-015-0162-6 |pmid=25928094 |pmc=4417229 |doi-access=free }}</ref> the dynamics of neural circuitry arise from interactions between individual neurons and how behavior can arise from abstract neural modules that represent complete subsystems. Studies considered long-and short-term plasticity of neural systems and their relation to learning and memory from the individual neuron to the system level.

It is possible to create a profile of a user's interests from pictures, using artificial neural networks trained for object recognition.<ref>{{cite journal | url=https://www.researchgate.net/publication/328964756 | doi=10.3233/978-1-61499-894-5-179 | last1=Wieczorek | first1=Szymon | last2=Filipiak | first2=Dominik | last3=Filipowska | first3=Agata | title=Semantic Image-Based Profiling of Users' Interests with Neural Networks | journal=Studies on the Semantic Web | volume=36 | issue=Emerging Topics in Semantic Technologies | year=2018 | access-date=20 January 2024 | archive-date=19 May 2024 | archive-url=https://web.archive.org/web/20240519082144/https://www.researchgate.net/publication/328964756_Semantic_Image-Based_Profiling_of_Users%27_Interests_with_Neural_Networks | url-status=live }}</ref>

Beyond their traditional applications, artificial neural networks are increasingly being utilized in interdisciplinary research, such as materials science. For instance, graph neural networks (GNNs) have demonstrated their capability in scaling deep learning for the discovery of new stable materials by efficiently predicting the total energy of crystals. This application underscores the adaptability and potential of ANNs in tackling complex problems beyond the realms of predictive modeling and artificial intelligence, opening new pathways for scientific discovery and innovation.<ref>{{Cite journal |last1=Merchant |first1=Amil |last2=Batzner |first2=Simon |last3=Schoenholz |first3=Samuel S. |last4=Aykol |first4=Muratahan |last5=Cheon |first5=Gowoon |last6=Cubuk |first6=Ekin Dogus |date=December 2023 |title=Scaling deep learning for materials discovery |journal=Nature |language=en |volume=624 |issue=7990 |pages=80–85 |doi=10.1038/s41586-023-06735-9 |issn=1476-4687 |pmc=10700131 |pmid=38030720|bibcode=2023Natur.624...80M }}</ref>