Jonathan Fine, Prageeth R. Wijewardhane, Sheik Dawood Beer Mohideen, Katelyn Smith, Jameson R. Bothe, Yogita Krishnamachari, Alexandra Andrews, Yong Liu and Gaurav Chopra

Pharm Res, 2023, 40, 701
DOI: 10.1007/s11095-023-03475-3

Machine learning prediction of organic moieties from the IR spectra, enhanced by additionally using the derivative IR data

Maurycy Krzyżanowski and Grzegorz Matyszczak

Chem. Pap., 2024, 78, 3149
DOI: 10.1007/s11696-024-03301-z

A Bayesian Inference-Based Method for Uncertainty Analysis in Raman Spectroscopy

Hanxuan Zhou

IEEE Access, 2025, 13, 7746
DOI: 10.1109/ACCESS.2024.3510927

Deconvolution of 1D NMR spectra: A deep learning-based approach

N. Schmid, S. Bruderer, F. Paruzzo, G. Fischetti, G. Toscano, D. Graf, M. Fey, A. Henrici, V. Ziebart, B. Heitmann, H. Grabner, J.D. Wegner, R.K.O. Sigel and D. Wilhelm

Journal of Magnetic Resonance, 2023, 347, 107357
DOI: 10.1016/j.jmr.2022.107357

An In Silico Infrared Spectral Library of Molecular Ions for Metabolite Identification

Kas J. Houthuijs, Giel Berden, Udo F. H. Engelke, Vasuk Gautam, David S. Wishart, Ron A. Wevers, Jonathan Martens and Jos Oomens

Anal. Chem., 2023, 95, 8998
DOI: 10.1021/acs.analchem.3c01078

Impact of conformation and intramolecular interactions on vibrational circular dichroism spectra identified with machine learning

Tom Vermeyen, Ana Cunha, Patrick Bultinck and Wouter Herrebout

Commun Chem, 2023, 6
DOI: 10.1038/s42004-023-00944-z

Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification

Thomas Specht, Kerstin Münnemann, Hans Hasse and Fabian Jirasek

J. Chem. Inf. Model., 2021, 61, 143
DOI: 10.1021/acs.jcim.0c01186

Meso-carbazole substituted porphyrin complexes: Synthesis and spectral properties according to experiment, DFT calculations and the prediction by machine learning methods

N.G. Bichan, E.N. Ovchenkova, A.A. Ksenofontov, V.A. Mozgova, M.S. Gruzdev, U.V. Chervonova, I.V. Shelaev and T.N. Lomova

Dyes and Pigments, 2022, 204, 110470
DOI: 10.1016/j.dyepig.2022.110470

Analysis and control of Hall effect thruster using optical emission spectroscopy and artificial neural network

Tarek Ben Slimane, Alexandre Leduc, Loïc Schiesko, Anne Bourdon and Pascal Chabert

Journal of Applied Physics, 2024, 136
DOI: 10.1063/5.0214760

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Mohammed A. Jabed, Yeonjoon Kim, Clark Yarbrough, Anne E. Harman-Ware, Jessica Olstad, Reinhard Seiser, Cheyenne Paeper, Anne K. Starace and Seonah Kim

ACS Sustainable Chem. Eng., 2023, 11, 11912
DOI: 10.1021/acssuschemeng.3c01821

Identification of Reaction Network Hypotheses for Complex Feedstocks from Spectroscopic Measurements with Minimal Human Intervention

Karthik Srinivasan, Anjana Puliyanda and Vinay Prasad

J. Phys. Chem. A, 2024, 128, 4714
DOI: 10.1021/acs.jpca.4c01592

Kolmogorov–Arnold neural network for identification of functional groups from FTIR spectra

Tomasz Urbańczyk, Jakub Bożek, Szymon Mirczak, Jarosław Koperski and Marek Krośnicki

Chemometrics and Intelligent Laboratory Systems, 2025, 263, 105421
DOI: 10.1016/j.chemolab.2025.105421

Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks

Shiyun Liu and Jacqueline M. Cole

J. Chem. Inf. Model., 2025, 65, 8435
DOI: 10.1021/acs.jcim.5c00499

Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) in patients with major depressive disorder: rationale and design of a prospective multicentre cohort study

Yuzhen Zheng, Linna Zhang, Shen He, Zuoquan Xie, Jing Zhang, Changrong Ge, Guangqiang Sun, Jingjing Huang and Huafang Li

BMJ Open, 2022, 12, e067447
DOI: 10.1136/bmjopen-2022-067447

Machine Learning for Functional Group Identification in Vibrational Spectroscopy: A Pedagogical Lab for Undergraduate Chemistry Students

Elizabeth S. Thrall, Seung Eun Lee, Joshua Schrier and Yijun Zhao

J. Chem. Educ., 2021, 98, 3269
DOI: 10.1021/acs.jchemed.1c00693

Carbon Dot Blinking Fingerprint Uncovers Native Membrane Receptor Organizations via Deep Learning

Qian Wang, Qian Zhang, Hua He, Zhenzhen Feng, Jian Mao, Xiang Hu, Xiaoyun Wei, Simin Bi, Guangyong Qin, Xiaojuan Wang, Baosheng Ge, Daoyong Yu, Hao Ren and Fang Huang

Anal. Chem., 2022, 94, 3914
DOI: 10.1021/acs.analchem.1c04947

Identifying molecular functional groups of organic compounds by deep learning of NMR data

Chongcan Li, Yong Cong and Weihua Deng

Magnetic Reson in Chemistry, 2022, 60, 1061
DOI: 10.1002/mrc.5292

Application of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions

Yue Sun, Sandor Brockhauser, Péter Hegedűs, Christian Plückthun, Luca Gelisio and Danilo Enoque Ferreira de Lima

Sci Rep, 2023, 13
DOI: 10.1038/s41598-023-36456-y

Regional and global hotspots of arsenic contamination of topsoil identified by deep learning

Mengting Wu, Chongchong Qi, Sybil Derrible, Yosoon Choi, Andy Fourie and Yong Sik Ok

Commun Earth Environ, 2024, 5
DOI: 10.1038/s43247-023-01177-7

QMe14S: A Comprehensive and Efficient Spectral Data Set for Small Organic Molecules

Mingzhi Yuan, Zihan Zou, Yi Luo, Jun Jiang and Wei Hu

J. Phys. Chem. Lett., 2025, 16, 3972
DOI: 10.1021/acs.jpclett.5c00839

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information

Xin-Yu Lu, Chen-Yue Wang, Hui Tang, Yi-Fei Qin, Li Cui, Xiang Wang, Guo-Kun Liu and Bin Ren

Anal. Chem., 2024
DOI: 10.1021/acs.analchem.3c03889

Neural network algorithm enables mass calibration autocorrection for miniature mass spectrometry systems

Yanjun Wei, Bin Jiao, Haoyue Zhang, Donghui Zhang, Jiexun Bu, Xiaoyu Zhou and Zheng Ouyang

International Journal of Mass Spectrometry, 2023, 490, 117085
DOI: 10.1016/j.ijms.2023.117085

An ensemble machine learning method for microplastics identification with FTIR spectrum

Xinyu Yan, Zhi Cao, Alan Murphy and Yuansong Qiao

Journal of Environmental Chemical Engineering, 2022, 10, 108130
DOI: 10.1016/j.jece.2022.108130

Recent Developments in Mass Spectrometry to Support Next-Generation Synthesis and Screening

Nari N. Talaty, Robert W. Johnson, James Sawicki, Omprakash Nacham and Stevan W. Djuric

ACS Med. Chem. Lett., 2023, 14, 711
DOI: 10.1021/acsmedchemlett.3c00040

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants

Nitika Nitika, B. Keerthiveena, Garima Thakur and Anurag S. Rathore

Pharm Res, 2024, 41, 463
DOI: 10.1007/s11095-024-03663-9

A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra

Ruocheng Han, Rangsiman Ketkaew and Sandra Luber

J. Phys. Chem. A, 2022, 126, 801
DOI: 10.1021/acs.jpca.1c10417

A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning

Xiaqiong Fan, Yue Wang, Chuanxiu Yu, Yuanxia Lv, Hailiang Zhang, Qiong Yang, Ming Wen, Hongmei Lu and Zhimin Zhang

Anal. Chem., 2023, 95, 4863
DOI: 10.1021/acs.analchem.2c03853

Predicting Structural Motifs of Glycosaminoglycans using Cryogenic Infrared Spectroscopy and Random Forest

Jerome Riedel, Maike Lettow, Márkó Grabarics, Michael Götze, Rebecca L. Miller, Geert-Jan Boons, Gerard Meijer, Gert von Helden, Gergo Peter Szekeres and Kevin Pagel

J. Am. Chem. Soc., 2023, 145, 7859
DOI: 10.1021/jacs.2c12762

Unveiling Spectrum–Structure Correlation in Vibrational Spectroscopy: Task-Driven Deep Learning Classification Balancing Global Fusion and Local Extraction

Guoyang Shi, Haoyu Guo, Tianchu Gao, Haoping Wu, Si-Heng Luo, Sanlei Wang, Yong Lei, Yun Zhang, Yangfan Xie, Bin Ren, Zhong-Qun Tian, Gui-Fang Shao and Guo-Kun Liu

Anal. Chem., 2025, 97, 10988
DOI: 10.1021/acs.analchem.4c05842

Deep Q-Learning-Based Molecular Graph Generation for Chemical Structure Prediction From Infrared Spectra

Joshua Dean Ellis, Razib Iqbal and Keiichi Yoshimatsu

IEEE Trans. Artif. Intell., 2024, 5, 634
DOI: 10.1109/TAI.2023.3287947

Microfluidic Synthesis of Semiconductor Materials: Toward Accelerated Materials Development in Flow

Zachary S. Campbell, Fazel Bateni, Amanda A. Volk, Kameel Abdel‐Latif and Milad Abolhasani

Part & Part Syst Charact, 2020, 37
DOI: 10.1002/ppsc.202000256

Algorithmic graph theory for post-processing molecular dynamics trajectories

Sana Bougueroua, Ylène Aboulfath, Dominique Barth and Marie-Pierre Gaigeot

Molecular Physics, 2023, 121
DOI: 10.1080/00268976.2022.2162456

Fcg-Former: Identification of Functional Groups in FTIR Spectra Using Enhanced Transformer-Based Model

Vu Hoang Minh Doan, Cao Duong Ly, Sudip Mondal, Thi Thuy Truong, Tan Dung Nguyen, Jaeyeop Choi, Byeongil Lee and Junghwan Oh

Anal. Chem., 2024
DOI: 10.1021/acs.analchem.4c01622

Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks

Samuel Goldman, Janet Li and Connor W. Coley

Anal. Chem., 2024, 96, 3419
DOI: 10.1021/acs.analchem.3c04654

Deep metabolome: Applications of deep learning in metabolomics

Yotsawat Pomyen, Kwanjeera Wanichthanarak, Patcha Poungsombat, Johannes Fahrmann, Dmitry Grapov and Sakda Khoomrung

Computational and Structural Biotechnology Journal, 2020, 18, 2818
DOI: 10.1016/j.csbj.2020.09.033

Transformer-Based Models for Predicting Molecular Structures from Infrared Spectra Using Patch-Based Self-Attention

Wenjin Wu, Aleš Leonardis, Jianbo Jiao, Jun Jiang and Linjiang Chen

J. Phys. Chem. A, 2025, 129, 2077
DOI: 10.1021/acs.jpca.4c05665

Recent Developments in Machine Learning for Mass Spectrometry

Armen G. Beck, Matthew Muhoberac, Caitlin E. Randolph, Connor H. Beveridge, Prageeth R. Wijewardhane, Hilkka I. Kenttämaa and Gaurav Chopra

ACS Meas. Sci. Au, 2024, 4, 233
DOI: 10.1021/acsmeasuresciau.3c00060

Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry

Nicole M. North, Abigail A. Enders, Morgan L. Cable and Heather C. Allen

ACS Omega, 2023, 8, 24341
DOI: 10.1021/acsomega.3c01684

Leveraging infrared spectroscopy for automated structure elucidation

Marvin Alberts, Teodoro Laino and Alain C. Vaucher

Commun Chem, 2024, 7
DOI: 10.1038/s42004-024-01341-w

Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning

Yanlong Liu, Wenli Yao, Fenghui Qin, Lei Zhou and Yian Zheng

Environ. Sci. Technol., 2023, 57, 6656
DOI: 10.1021/acs.est.2c08952

Can One Hear the Shape of a Molecule (from its Coulomb Matrix Eigenvalues)?

Joshua Schrier

J. Chem. Inf. Model., 2020, 60, 3804
DOI: 10.1021/acs.jcim.0c00631

Infrared Spectral Analysis for Prediction of Functional Groups Based on Feature-Aggregated Deep Learning

Tianyi Wang, Ying Tan, Yu Zong Chen and Chunyan Tan

J. Chem. Inf. Model., 2023, 63, 4615
DOI: 10.1021/acs.jcim.3c00749

Machine Learning in Soft Matter: From Simulations to Experiments

Kaihua Zhang, Xiangrui Gong and Ying Jiang

Adv Funct Materials, 2024, 34
DOI: 10.1002/adfm.202315177

Rapid SERS Analysis: From Laboratory to Real Sample

Haoyu Guo, Shengfu Zhi, Zeyu Zhao, Tianchu Gao, Hao Ma, Si-Heng Luo, Wei Zhang, Ping Guo, Bin Ren, Zhong-Qun Tian and Guo-Kun Liu

ACS Appl. Mater. Interfaces, 2025, 17, 33318
DOI: 10.1021/acsami.5c05699

TransG-net: transformer and graph neural network based multi-modal data fusion network for molecular properties prediction

Taohong Zhang, Saian Chen, Aziguli Wulamu, Xuxu Guo, Qianqian Li and Han Zheng

Appl Intell, 2023, 53, 16077
DOI: 10.1007/s10489-022-04351-0

Machine-Learning Approach to Identify Organic Functional Groups from FT-IR and NMR Spectral Data

Gwanho Lee, Hyekyoung Shim, Juhyun Cho and Sang-Il Choi

ACS Omega, 2025, 10, 12717
DOI: 10.1021/acsomega.5c01903

Machine learning for the advancement of genome-scale metabolic modeling

Pritam Kundu, Satyajit Beura, Suman Mondal, Amit Kumar Das and Amit Ghosh

Biotechnology Advances, 2024, 74, 108400
DOI: 10.1016/j.biotechadv.2024.108400

Deep Learning-Assisted Spectrum–Structure Correlation: State-of-the-Art and Perspectives

Xin-Yu Lu, Hao-Ping Wu, Hao Ma, Hui Li, Jia Li, Yan-Ti Liu, Zheng-Yan Pan, Yi Xie, Lei Wang, Bin Ren and Guo-Kun Liu

Anal. Chem., 2024, 96, 7959
DOI: 10.1021/acs.analchem.4c01639

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Yunchao Xie, Kianoosh Sattari, Chi Zhang and Jian Lin

Progress in Materials Science, 2023, 132, 101043
DOI: 10.1016/j.pmatsci.2022.101043

Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR

Achini Herath, Rhowell Jr Tiozon, Tobias Kretzschmar, Nese Sreenivasulu, Peter Mahon and Vito Butardo

Food Chemistry, 2024, 460, 140728
DOI: 10.1016/j.foodchem.2024.140728

Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip

Haiting Cao, Huayi Shi, Jie Tang, Yanan Xu, Yufan Ling, Xing Lu, Yang Yang, Xiaojie Zhang and Houyu Wang

iScience, 2023, 26, 107821
DOI: 10.1016/j.isci.2023.107821

Infrared spectrum analysis of organic molecules with neural networks using standard reference data sets in combination with real-world data

Dev Punjabi, Yu-Chieh Huang, Laura Holzhauer, Pierre Tremouilhac, Pascal Friederich, Nicole Jung and Stefan Bräse

J Cheminform, 2025, 17
DOI: 10.1186/s13321-025-00960-2

From multi-omics to predictive biomarker: AI in tumor microenvironment

Luo Hai, Ziming Jiang, Haoxuan Zhang and Yingli Sun

Front. Immunol., 2024, 15
DOI: 10.3389/fimmu.2024.1514977

Machine Learning-based Classification for the Prioritization of Potentially Hazardous Chemicals with Structural Alerts in Nontarget Screening

Nienke Meekel, Anneli Kruve, Marja H. Lamoree and Frederic M. Been

Environ. Sci. Technol., 2025, 59, 5056
DOI: 10.1021/acs.est.4c10498

Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective

Xi Xue, Hanyu Sun, Minjian Yang, Xue Liu, Hai-Yu Hu, Yafeng Deng and Xiaojian Wang

Anal. Chem., 2023, 95, 13733
DOI: 10.1021/acs.analchem.3c02540

Functional groups prediction from infrared spectra based on computer-assist approaches

Zhimeng Wang, Xiaoyu Feng, Junhong Liu, Minchun Lu and Menglong Li

Microchemical Journal, 2020, 159, 105395
DOI: 10.1016/j.microc.2020.105395

Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data

Frithjof Herb, Mario Boley and Wye-Khay Fong

Journal of Hazardous Materials, 2025, 487, 136989
DOI: 10.1016/j.jhazmat.2024.136989

An Ensemble Machine Learning Method for Microplastics Identification with Ftir Spectrum

Xinyu Yan, Zhi Cao, Alan Murphy and Yuansong Qiao

SSRN Journal, 2022
DOI: 10.2139/ssrn.4059945

Algorithmic Graph Theory, Reinforcement Learning and Game Theory in MD Simulations: From 3D Structures to Topological 2D-Molecular Graphs (2D-MolGraphs) and Vice Versa

Sana Bougueroua, Marie Bricage, Ylène Aboulfath, Dominique Barth and Marie-Pierre Gaigeot

Molecules, 2023, 28, 2892
DOI: 10.3390/molecules28072892

Unsupervised Machine Learning-Based Image Recognition of Raw Infrared Spectra: Toward Chemist-like Chemical Structural Classification and Beyond Numerical Data

Kentarou Fuku and Takefumi Yoshida

J. Chem. Inf. Model., 2025, 65, 3176
DOI: 10.1021/acs.jcim.4c01644

Evaluation of optical constants in oxide thin films using machine learning

Kyosuke Saeki and Takayuki Makino

Jpn. J. Appl. Phys., 2023, 62, 081002
DOI: 10.35848/1347-4065/acea4b

Rapid estimation of soil Mn content by machine learning and soil spectra in large-scale

Min Zhou, Tao Hu, Mengting Wu, Chundi Ma and Chongchong Qi

Ecological Informatics, 2024, 81, 102615
DOI: 10.1016/j.ecoinf.2024.102615

Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum

Hee-Deok Jang, Seokjoon Kwon, Hyunwoo Nam and Dong Eui Chang

Sensors, 2024, 24, 3601
DOI: 10.3390/s24113601

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

Abigail A. Enders, Nicole M. North, Chase M. Fensore, Juan Velez-Alvarez and Heather C. Allen

Anal. Chem., 2021, 93, 9711
DOI: 10.1021/acs.analchem.1c00867

A glass matrices-assisted quantum dots-based biosensor for selective capturing and detection ofEscherichia coli

Anjali Pant, Taranvir Kaur, Teenu Sharma, Joga Singh, Ashish Suttee, Ravi Pratap Barnwal, Indu Pal Kaur, Gurpal Singh and Bhupinder Singh

Journal of Water and Health, 2022, 20, 1673
DOI: 10.2166/wh.2022.293

Pixel-based Raman hyperspectral identification of complex pharmaceutical formulations

Laureen Coic, Pierre-Yves Sacré, Amandine Dispas, Charlotte De Bleye, Marianne Fillet, Cyril Ruckebusch, Philippe Hubert and Eric Ziemons

Analytica Chimica Acta, 2021, 1155, 338361
DOI: 10.1016/j.aca.2021.338361

Deep learning-based screening approach for priority pollutants: a case study on retired power battery recycling

Xuchao Liu, Jing Zhao, Guohua Zhang, Sheng Wei, Xiaolong Song and Daqiang Yin

Environmental Pollution, 2025, 383, 126849
DOI: 10.1016/j.envpol.2025.126849