Infrared spectroscopy-assisted prediction of impurities in chemicals using machine learning: towards smart self-driving laboratories

Anastasiia M. Kutskaia and Konstantin S. Rodygin

Phys. Chem. Chem. Phys., 2026, 28, 3939
DOI: 10.1039/D5CP04214C

Deductive machine learning models for product identification

Tianfan Jin, Qiyuan Zhao, Andrew B. Schofield and Brett M. Savoie

Chem. Sci., 2024, 15, 11995
DOI: 10.1039/D3SC04909D

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

Austin H. Cheng, Cher Tian Ser, Marta Skreta, Andrés Guzmán-Cordero, Luca Thiede, Andreas Burger, Abdulrahman Aldossary, Shi Xuan Leong, Sergio Pablo-García, Felix Strieth-Kalthoff and Alán Aspuru-Guzik

Faraday Discuss., 2025, 256, 10
DOI: 10.1039/D4FD00153B

Intelligent understanding of spectra: from structural elucidation to property design

Shuo Feng, Meng Huang, Yanbo Li, Aoran Cai, Xiaoyu Yue, Song Wang, Linjiang Chen, Jun Jiang and Yi Luo

Chem. Soc. Rev., 2025, 54, 8243
DOI: 10.1039/D4CS01293C

Setting new benchmarks in AI-driven infrared structure elucidation

Marvin Alberts, Federico Zipoli and Teodoro Laino

Digital Discovery, 2025, 4, 1936
DOI: 10.1039/D5DD00131E

Enhancement of the texture and microstructure of faba bean-based meat analogues with brewers' spent grain through enzymatic treatments

Yue Fan, Shiyu Zheng, Pratheep K. Annamalai, Bhesh Bhandari and Sangeeta Prakash

Sustainable Food Technol., 2024, 2, 826
DOI: 10.1039/D4FB00045E

Multi-modal contrastive learning for chemical structure elucidation with VibraCLIP

Pau Rocabert-Oriols, Camilla Lo Conte, Núria López and Javier Heras-Domingo

Digital Discovery, 2025, 4, 3818
DOI: 10.1039/D5DD00269A

Machine learning-guided substrate temperature optimization for anisotropic Fe(Se, Te) superconducting thin films

Jie Zhang, Yi Luo, Yalin Zhang, Shengnan Zhang, Jixing Liu, Yu Feng, Fanggong Cai, ChuanYu Zhang, Guo Yan and Yong Zhao

J. Mater. Chem. C, 2025, 13, 12533
DOI: 10.1039/D5TC00059A

Spectra to structure: contrastive learning framework for library ranking and generating molecular structures for infrared spectra

Ganesh Chandan Kanakala, Bhuvanesh Sridharan and U. Deva Priyakumar

Digital Discovery, 2024, 3, 2417
DOI: 10.1039/D4DD00135D

LEC-former: enhancing functional group identification in FTIR spectra by improving weak peak perception

ShiDi Xie, LiJuan Peng, JunNa Zhang, YiLong Kang, HongCheng Zhou and ZongJun Ren

Anal. Methods, 2025, 17, 9898
DOI: 10.1039/D5AY01519G

Impact of noise on inverse design: the case of NMR spectra matching

Dominik Lemm, Guido Falk von Rudorff and O. Anatole von Lilienfeld

Digital Discovery, 2024, 3, 136
DOI: 10.1039/D3DD00132F

Structural determination of natural products at speeds and scale of high-throughput screening

Christopher C. Thornburg, Rohitesh Kumar, Dongdong Wang, Barry R. O'Keefe and Tanja Grkovic

Nat. Prod. Rep., 2026
DOI: 10.1039/D6NP00016A

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

Integrated Machine Learning/FT-IR Framework for Efficient Solvent Composition Analysis in Carbon Capture

Ahmad Syauqi, Atabay Allamyradov, Cristóbal Quintana, Vijay Mohan Nagulapati, Boris Brigljević and Hankwon Lim

Ind. Eng. Chem. Res., 2024, 63, 16847
DOI: 10.1021/acs.iecr.4c01643

Predictive Modeling of High-Entropy Alloys and Amorphous Metallic Alloys Using Machine Learning

Son Gyo Jung, Guwon Jung and Jacqueline M. Cole

J. Chem. Inf. Model., 2024, 64, 7313
DOI: 10.1021/acs.jcim.4c00873

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

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

The Analysis of Vibrational Spectra: Past, Present and Future

Stewart F. Parker

ChemPlusChem, 2025, 90
DOI: 10.1002/cplu.202400461

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

Infrared Spectral Descriptors for Reaction Yield Prediction: Toward Redefining Experimental Spaces

Yuya Endo and Hiromasa Kaneko

Molecular Informatics, 2026, 45
DOI: 10.1002/minf.70019

A transformer based generative chemical language AI model for structural elucidation of organic compounds

Xiaofeng Tan

J Cheminform, 2025, 17
DOI: 10.1186/s13321-025-01016-1

Convolutional neural networks for accurate and robust noninvasive pressure measurements in sealed systems

D. Pereira, M. Prisbrey, E.S. Davis, P. Vakhlamov, A. Saini, C. Chavez, C. Pantea and J. Greenhall

Engineering Applications of Artificial Intelligence, 2025, 160, 111729
DOI: 10.1016/j.engappai.2025.111729

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

Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials

Son Gyo Jung, Guwon Jung and Jacqueline M. Cole

J. Chem. Inf. Model., 2024, 64, 6388
DOI: 10.1021/acs.jcim.4c00947

Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow

Son Gyo Jung, Guwon Jung and Jacqueline M. Cole

J. Chem. Inf. Model., 2025, 65, 133
DOI: 10.1021/acs.jcim.4c01862

Biomedical Fragmented Attention Spectral Transformer Network for Infrared Spectra Analysis

Durgesh Ameta, Hardik Sharma, Praful Hambarde and Laxmidhar Behera

IEEE Trans. Instrum. Meas., 2026, 75, 1
DOI: 10.1109/TIM.2026.3679079

Quantitative analysis of MoS2 thin film micrographs with machine learning

Isaiah A. Moses and Wesley F. Reinhart

Materials Characterization, 2024, 209, 113701
DOI: 10.1016/j.matchar.2024.113701

Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors

Son Gyo Jung, Guwon Jung and Jacqueline M. Cole

J. Chem. Inf. Model., 2024, 64, 7349
DOI: 10.1021/acs.jcim.4c01137

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

DeepMIR: A Hybrid Convolutional Neural Network-Transformer Framework for Accurate Identification of Target Components from Mid-Infrared Spectra of Mixtures

Lin Tan, Yue Wang, Hailiang Zhang, Jinyu Sun, Qiong Yang, Xiao Yang, Zhimin Zhang and Hongmei Lu

Anal. Chem., 2025, 97, 27706
DOI: 10.1021/acs.analchem.5c04545

Bacterial identification in SERS-integrated microfluidics using CNN-driven 2D classification of 1D spectra

Mehdi Feizpour, Halewijn Van den Bosche, Lilit Melikyan, Thomas Demuyser, Piet Cools, Hugo Thienpont, Tatevik Sarukhanyan and Heidi Ottevaere

Talanta, 2025, 295, 128325
DOI: 10.1016/j.talanta.2025.128325

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

Substance Science Insight Based on Spectroscopic Large Model

Yi Luo, Zhongqun Tian, Jinghong Li, Jun Jiang, Bin Jiang, Linjiang Chen, Song Wang, Shuo Feng, Meng Huang and Xiangjian Shen

BNSFC, 2025, 39, 542
DOI: 10.3724/BNSFC-2025-0001

A new staining method using different targeted fluorescent carbon dots for tissue sections analysis and diagnosis

Qianqian Duan, Qingxia Guo, Yanfeng Xi, Hong Su, Juanjuan Xue, Qi Zhang, Xing Guo, Haixuan Han, Yangming Zhou, Shengbo Sang and Xiaojuan Wang

Diamond and Related Materials, 2025, 152, 111949
DOI: 10.1016/j.diamond.2025.111949

AI‐Assisted Design of Advanced Polymeric Materials: Challenges and Solutions

Liang Gao, Siqin Song, Jiaping Lin, Yinyi Xu, Liquan Wang and Lei Du

Advanced Materials, 2026, 38
DOI: 10.1002/adma.202516857

Pyrolysis of biochar impregnated with glucose: Influence of evolution of functionalities on capabilities for adsorption

Xiao Cheng, Yuchen Jiang, Mengjiao Fan, Wenjian Liu, Shu Zhang and Xun Hu

Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2025, 725, 137718
DOI: 10.1016/j.colsurfa.2025.137718

Toward Complete Molecular Structure Prediction from Infrared Spectroscopy Using Deep Learning

Colin Zhang and Yang Ha

J. Chem. Inf. Model., 2026, 66, 100
DOI: 10.1021/acs.jcim.5c01834

Dual microenvironment sensitive carbon dots with large stokes shift for high-contrast cell and tissue section imaging

Qingxia Guo, Juanjuan Xue, Qi Zhang, Haixuan Han, Yangming Zhou, Xing Guo, Xiaojuan Wang, Yanfeng Xi, Qianqian Duan and Shengbo Sang

Dyes and Pigments, 2026, 245, 113164
DOI: 10.1016/j.dyepig.2025.113164

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

Gradient boosted and statistical feature selection workflow for materials property predictions

Son Gyo Jung, Guwon Jung and Jacqueline M. Cole

The Journal of Chemical Physics, 2023, 159
DOI: 10.1063/5.0171540

Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow

Son Gyo Jung, Guwon Jung and Jacqueline M. Cole

J. Chem. Inf. Model., 2024, 64, 1187
DOI: 10.1021/acs.jcim.3c01897

Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks

Son Gyo Jung, Guwon Jung and Jacqueline M. Cole

J. Chem. Inf. Model., 2024, 64, 1486
DOI: 10.1021/acs.jcim.3c01792

Machine learning for CO2 capture and conversion: A review

Sung Eun Jerng, Yang Jeong Park and Ju Li

Energy and AI, 2024, 16, 100361
DOI: 10.1016/j.egyai.2024.100361

Decoding the infrared spectrum of size-fractionated graphene oxide with machine learning

Dmitrii M. Filatov, Ivan V. Mikheev and Mikhail A. Proskurnin

Diamond and Related Materials, 2026, 162, 113196
DOI: 10.1016/j.diamond.2025.113196

Paired Neural Network for Matching Experimental and Predicted Infrared Spectra

Sean M. Colby, Jessica L. Bade, Amy M. Jystad, Peter S. Rice, Yehia M. Ibrahim, Simone Raugei and Christopher P. Harrilal

Anal. Chem., 2025, 97, 20049
DOI: 10.1021/acs.analchem.5c01607

Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra

Philip Jacobson, Suhas Kumar, Raga Krishnakumar and Jerilyn A. Timlin

ACS Omega, 2026, 11, 27991
DOI: 10.1021/acsomega.5c11985

Machine Learning for Stability Enhancement in Perovskite Solar Cells: A Pathway to Commercial Viability

Shen Wang, Weiren Zhao, Minjia Zhou, Tanghao Liu, Yi'an Wang, Run Shi, Yunfan Wang and Zhuoqiong Zhang

Progress in Photovoltaics, 2025
DOI: 10.1002/pip.70060

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

A joint sparse representation-based approach for anomaly rectification in extended-duration SHM systems with sensor networks

Xiaolei Xu, Fucai Li, Yue Hu and Yanping Zhu

Mechanical Systems and Signal Processing, 2025, 231, 112666
DOI: 10.1016/j.ymssp.2025.112666

Machine learning for analyses and automation of structural characterization of polymer materials

Shizhao Lu and Arthi Jayaraman

Progress in Polymer Science, 2024, 153, 101828
DOI: 10.1016/j.progpolymsci.2024.101828

Silicon-based long-wave infrared detectors for flammable gas sensing

Wei-Shin Liu, Jun-Yi Li, Mun-Wei Phan, Yu-Sheng Lai, Szu-Hung Chen and Hsuen-Li Chen

Journal of Hazardous Materials, 2026, 507, 141629
DOI: 10.1016/j.jhazmat.2026.141629

CIRSNet: A Lightweight Chemometric Infrared Spectral Network for Spectral Analysis

Durgesh Ameta, Hardik Sharma, Praful Hambarde and Laxmidhar Behera

IEEE Trans. Instrum. Meas., 2025, 74, 1
DOI: 10.1109/TIM.2025.3614890

Robust Identification of Gas Mixtures from FTIR Spectra using Attention Mechanism to Mitigate Instrument Line Shape Variations

Tairan Xu, Liang Xu, Yongfeng Sun, Wenqing Liu and Jianguo Liu

Anal. Chem., 2025, 97, 23312
DOI: 10.1021/acs.analchem.5c03975

Reconocimiento asistido por redes neuronales de grupos funcionales en espectros FTIR de compuestos obtenidos por pirólisis

Alcides Gustavo López Paredes, Patricia Esther Ochoa Pesantes and Karla Alexandra Ávila Hidalgo

RIG, 2026, 5, 143
DOI: 10.62943/rig.v5n1.2026.487

ZIF-8 Vibrational Spectra: Peak Assignments and Defect Signals

Mueed Ahmad, Roshan Patel, Dennis T. Lee, Peter Corkery, Andrea Kraetz, Prerna, Samuel A. Tenney, Dmytro Nykypanchuk, Xiao Tong, J. Ilja Siepmann, Michael Tsapatsis and J. Anibal Boscoboinik

ACS Appl. Mater. Interfaces, 2024, 16, 27887
DOI: 10.1021/acsami.4c02396

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

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

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

Characterizing functional group composition of needle coke feedstocks via infrared spectroscopy: Experimental and simulation techniques

Denis O. Potapov, Gintaras Gliaudelis, Ilia V. Kopanichuk, Dmitrii A. Sbytov, Nikita A. Krotkov, Timur A. Aliev, Anton A. Muravev, Nikolay D. Kondratyuk and Ekaterina V. Skorb

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2026, 361, 128054
DOI: 10.1016/j.saa.2026.128054

Functional Group Identification from Infrared Spectra via a Dual-Head Convolutional Network

Yitian Kong

ACS Omega, 2026, 11, 22369
DOI: 10.1021/acsomega.6c01193