Fabio Novelli, Chun Yu Ma, Nidhi Adhlakha, Ellen M. Adams, Thorsten Ockelmann, Debasish Das Mahanta, Paola Di Pietro, Andrea Perucchi and Martina Havenith

Applied Sciences, 2020, 10, 5290
DOI: 10.3390/app10155290

Deep neural network for the dielectric response of insulators

Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E and Roberto Car

Phys. Rev. B, 2020, 102
DOI: 10.1103/PhysRevB.102.041121

Polarizability models for simulations of finite temperature Raman spectra from machine learning molecular dynamics

Ethan Berger and Hannu-Pekka Komsa

Phys. Rev. Materials, 2024, 8
DOI: 10.1103/PhysRevMaterials.8.043802

Accuracy, Performance, and Transferability of Interparticle Potentials for Al–Cu Alloys: Comparison of Embedded Atom and Deep Machine Learning Models

E. O. Khazieva, N. M. Shchelkatchev, A. O. Tipeev and R. E. Ryltsev

J. Exp. Theor. Phys., 2023, 137, 864
DOI: 10.1134/S1063776123120208

Neural Network-Based Sum-Frequency Generation Spectra of Pure and Acidified Water Interfaces with Air

Miguel de la Puente, Axel Gomez and Damien Laage

J. Phys. Chem. Lett., 2024, 15, 3096
DOI: 10.1021/acs.jpclett.4c00113

Permutationally invariant polynomial representation of polarizability tensor surfaces for linear regression analysis

Oluwaseun Omodemi, Martina Kaledin and Alexey L. Kaledin

J Comput Chem, 2022, 43, 1495
DOI: 10.1002/jcc.26952

Random Sampling Versus Active Learning Algorithms for Machine Learning Potentials of Quantum Liquid Water

Nore Stolte, János Daru, Harald Forbert, Dominik Marx and Jörg Behler

J. Chem. Theory Comput., 2025, 21, 886
DOI: 10.1021/acs.jctc.4c01382

Exploring new generation of characterization approaches for energy electrochemistry—from <italic>operando</italic> to artificial intelligence

Yu Qiao, Ren Hu, Yu Gu, Fu-Jie Tang, Si-Heng Luo, Hai-Tang Zhang, Jing-Hua Tian, Jun Cheng and Zhong-Qun Tian

Sci. Sin.-Chim, 2024, 54, 338
DOI: 10.1360/SSC-2023-0222

Accuracy, Performance, and Transferability of Interparticle Potentials for Al–Cu Alloys: Comparison of Embedded Atom and Deep Machine Learning Models

E. O. Khazieva, N. M. Shchelkachev, A. O. Tipeev and R. E. Ryl'tsev

Žurnal èksperimentalʹnoj i teoretičeskoj fiziki, 2023, 164, 980
DOI: 10.31857/S004445102312012X

Study of self-interaction errors in density functional predictions of dipole polarizabilities and ionization energies of water clusters using Perdew–Zunger and locally scaled self-interaction corrected methods

Sharmin Akter, Yoh Yamamoto, Carlos M. Diaz, Koblar A. Jackson, Rajendra R. Zope and Tunna Baruah

The Journal of Chemical Physics, 2020, 153
DOI: 10.1063/5.0025601

Evidence of ferroelectric features in low-density supercooled water from ab initio deep neural-network simulations

Cesare Malosso, Natalia Manko, Maria Grazia Izzo, Stefano Baroni and Ali Hassanali

Proc. Natl. Acad. Sci. U.S.A., 2024, 121
DOI: 10.1073/pnas.2407295121

Aqueous solution chemistryin silicoand the role of data-driven approaches

Debarshi Banerjee, Khatereh Azizi, Colin K. Egan, Edward Danquah Donkor, Cesare Malosso, Solana Di Pino, Gonzalo Díaz Mirón, Martina Stella, Giulia Sormani, Germaine Neza Hozana, Marta Monti, Uriel N. Morzan, Alex Rodriguez, Giuseppe Cassone, Asja Jelic, Damian Scherlis and Ali Hassanali

Chemical Physics Reviews, 2024, 5
DOI: 10.1063/5.0207567

Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra

Nan Xu, Petter Rosander, Christian Schäfer, Eric Lindgren, Nicklas Österbacka, Mandi Fang, Wei Chen, Yi He, Zheyong Fan and Paul Erhart

J. Chem. Theory Comput., 2024, 20, 3273
DOI: 10.1021/acs.jctc.3c01343

Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials

Amir Omranpour, Pablo Montero De Hijes, Jörg Behler and Christoph Dellago

The Journal of Chemical Physics, 2024, 160
DOI: 10.1063/5.0201241

Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments

Michael S. Chen, Tim J. Zuehlsdorff, Tobias Morawietz, Christine M. Isborn and Thomas E. Markland

J. Phys. Chem. Lett., 2020, 11, 7559
DOI: 10.1021/acs.jpclett.0c02168

Accurate and Efficient Prediction of Post-Hartree–Fock Polarizabilities of Condensed-Phase Systems

Dongbo Zhao, Yilin Zhao, Xin He, Yunzhi Li, Paul W. Ayers and Shubin Liu

J. Chem. Theory Comput., 2023, 19, 6461
DOI: 10.1021/acs.jctc.3c00646

Machine learning approaches for modelling of molecular polarizability in gold nanoclusters

Abhishek Ojha, Satya S. Bulusu and Arup Banerjee

Artificial Intelligence Chemistry, 2024, 2, 100080
DOI: 10.1016/j.aichem.2024.100080

Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach

Gong Chen, Théo Jaffrelot Inizan, Thomas Plé, Louis Lagardère, Jean-Philip Piquemal and Yvon Maday

J. Chem. Theory Comput., 2024, 20, 5558
DOI: 10.1021/acs.jctc.3c01421

Accelerating Molecular Vibrational Spectra Simulations with a Physically Informed Deep Learning Model

Yuzhuo Chen, Sebastian V. Pios, Maxim F. Gelin and Lipeng Chen

J. Chem. Theory Comput., 2024, 20, 4703
DOI: 10.1021/acs.jctc.4c00173

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

Denghui Lu, Han Wang, Mohan Chen, Lin Lin, Roberto Car, Weinan E, Weile Jia and Linfeng Zhang

Computer Physics Communications, 2021, 259, 107624
DOI: 10.1016/j.cpc.2020.107624

Algorithm-Assisted Structure Identification of Individual Nucleobases in Single DNA Chains through Tip-Enhanced Raman Spectromicroscopy

Lu-Yao Zhu, Yu Han, Yu-Fan Zhang, Hang Li, Li Dong, Chao Zhang, Yang Zhang, Yao Zhang and Zhen-Chao Dong

JACS Au, 2025, 5, 4835
DOI: 10.1021/jacsau.5c00791

Alkyne-tagged SERS nanoprobe for understanding Cu+ and Cu2+ conversion in cuproptosis processes

Sihan Zhang, Yuxiao Mei, Jiaqi Liu, Zhichao Liu and Yang Tian

Nat Commun, 2024, 15
DOI: 10.1038/s41467-024-47549-1

Antibodies Processed Using High Dilution Technology Distantly Change Structural Properties of IFNγ Aqueous Solution

Nikita Penkov

Pharmaceutics, 2021, 13, 1864
DOI: 10.3390/pharmaceutics13111864

Machine learning the electric field response of condensed phase systems using perturbed neural network potentials

Kit Joll, Philipp Schienbein, Kevin M. Rosso and Jochen Blumberger

Nat Commun, 2024, 15
DOI: 10.1038/s41467-024-52491-3

Dielectric Constant of Liquid Water Determined with Neural Network Quantum Molecular Dynamics

Aravind Krishnamoorthy, Ken-ichi Nomura, Nitish Baradwaj, Kohei Shimamura, Pankaj Rajak, Ankit Mishra, Shogo Fukushima, Fuyuki Shimojo, Rajiv Kalia, Aiichiro Nakano and Priya Vashishta

Phys. Rev. Lett., 2021, 126
DOI: 10.1103/PhysRevLett.126.216403

Predicting tensorial molecular properties with equivariant machine learning models

Vu Ha Anh Nguyen and Alessandro Lunghi

Phys. Rev. B, 2022, 105
DOI: 10.1103/PhysRevB.105.165131

Derivative learning of tensorial quantities—Predicting finite temperature infrared spectra from first principles

Bernhard Schmiedmayer and Georg Kresse

The Journal of Chemical Physics, 2024, 161
DOI: 10.1063/5.0217243

Accuracy and limitations of the bond polarizability model in modeling of Raman scattering from molecular dynamics simulations

Atanu Paul, Maya Rubenstein, Anthony Ruffino, Stefan Masiuk, Jonathan E. Spanier and Ilya Grinberg

The Journal of Chemical Physics, 2024, 161
DOI: 10.1063/5.0217227

Ab Initio Vibrational Spectroscopy of Water and Aqueous Solutions in a Wide Pressure–Temperature Range

Tao Li, Jiajia Huang, Chu Li, Cui Zhang and Ding Pan

WIREs Comput Mol Sci, 2025, 15
DOI: 10.1002/wcms.70017

Machine-learned interatomic potentials for transition metal dichalcogenide Mo1−xWxS2−2ySe2y alloys

Anas Siddiqui and Nicholas D. M. Hine

npj Comput Mater, 2024, 10
DOI: 10.1038/s41524-024-01357-9

Nuclear Quantum Effects on the Electronic Structure of Water and Ice

Margaret L. Berrens, Arpan Kundu, Marcos F. Calegari Andrade, Tuan Anh Pham, Giulia Galli and Davide Donadio

J. Phys. Chem. Lett., 2024, 15, 6818
DOI: 10.1021/acs.jpclett.4c01315

Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings

Lucien Dupuy and Neepa T. Maitra

The Journal of Chemical Physics, 2024, 161
DOI: 10.1063/5.0227523

First-Principles Molecular Dynamics Simulations of Infrared and Raman Vibrational Spectra of H5O2+, D5O2+, DH4O2+, and D4HO2+ from 50 to 300 K

Oluwaseun Omodemi and Martina Kaledin

J. Phys. Chem. A, 2025, 129, 9921
DOI: 10.1021/acs.jpca.5c05184

Study of the Effect of Europium Acetate on the Intermolecular Properties of Water

O. V. Slatinskaya, Yu. N. Pyrkov, S. A. Filatova, D. A. Guryev and N. V. Penkov

Front. Phys., 2021, 9
DOI: 10.3389/fphy.2021.641110

Thermal disorder and phonon softening in the ferroelectric phase transition of lead titanate

Pinchen Xie, Yixiao Chen, Weinan E and Roberto Car

Phys. Rev. B, 2025, 111
DOI: 10.1103/PhysRevB.111.094113

Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization

Yaolong Zhang, Qidong Lin and Bin Jiang

WIREs Comput Mol Sci, 2023, 13
DOI: 10.1002/wcms.1645

Molecular Dynamics Simulations of Molten Magnesium Chloride Using Machine‐Learning‐Based Deep Potential

Wenshuo Liang, Guimin Lu and Jianguo Yu

Advcd Theory and Sims, 2020, 3
DOI: 10.1002/adts.202000180

Dynamics & Spectroscopy with Neutrons—Recent Developments & Emerging Opportunities

Kacper Drużbicki, Mattia Gaboardi and Felix Fernandez-Alonso

Polymers, 2021, 13, 1440
DOI: 10.3390/polym13091440

Machine learning accelerates Raman computations from molecular dynamics for materials science

David A. Egger, Manuel Grumet and Tomáš Bučko

The Journal of Chemical Physics, 2025, 163
DOI: 10.1063/5.0287358

Transferability of Machine Learning Models for Predicting Raman Spectra

Mandi Fang, Shi Tang, Zheyong Fan, Yao Shi, Nan Xu and Yi He

J. Phys. Chem. A, 2024, 128, 2286
DOI: 10.1021/acs.jpca.3c07109

Transfer learning for accurate description of atomic transport in Al–Cu melts

E. O. Khazieva, N. M. Chtchelkatchev and R. E. Ryltsev

The Journal of Chemical Physics, 2024, 161
DOI: 10.1063/5.0222355

Phase Diagram of a Deep Potential Water Model

Linfeng Zhang, Han Wang, Roberto Car and Weinan E

Phys. Rev. Lett., 2021, 126
DOI: 10.1103/PhysRevLett.126.236001

Universal machine learning for the response of atomistic systems to external fields

Yaolong Zhang and Bin Jiang

Nat Commun, 2023, 14
DOI: 10.1038/s41467-023-42148-y

Investigations of cysteine isomers using Terahertz and Raman spectroscopies

Xun Zhang, Yujing Bian, Zhenqi Zhu, Ruonan Zeng, Wenlong Zhou and Bin Yang

Infrared Physics & Technology, 2022, 125, 104299
DOI: 10.1016/j.infrared.2022.104299

Theoretical prediction on the local structure and transport properties of molten alkali chlorides by deep potentials

Wenshuo Liang, Guimin Lu and Jianguo Yu

Journal of Materials Science & Technology, 2021, 75, 78
DOI: 10.1016/j.jmst.2020.09.040

Condensed Phase Water Molecular Multipole Moments from Deep Neural Network Models Trained on Ab Initio Simulation Data

Yu Shi, Carrie C. Doyle and Thomas L. Beck

J. Phys. Chem. Lett., 2021, 12, 10310
DOI: 10.1021/acs.jpclett.1c02328

Machine learning phase space quantum dynamics approaches

Xinzijian Liu, Linfeng Zhang and Jian Liu

The Journal of Chemical Physics, 2021, 154
DOI: 10.1063/5.0046689

How can machine learning facilitate computational electrochemistry

Jia-Xin Zhu and Jun Cheng

APL Computational Physics, 2026, 2
DOI: 10.1063/5.0320488

Deep machine learning potentials for multicomponent metallic melts: Development, predictability and compositional transferability

R.E. Ryltsev and N.M. Chtchelkatchev

Journal of Molecular Liquids, 2022, 349, 118181
DOI: 10.1016/j.molliq.2021.118181

Machine Learning Driven Advances in Molecular Dynamics of Bulk and Interfacial Aqueous Systems

Ruiyu Wang, Vanessa J. Meraz and Pratyush Tiwary

Chem. Rev., 2026, 126, 3730
DOI: 10.1021/acs.chemrev.5c00708

Investigating water structure and dynamics at metal/water interfaces from classical, ab initio to machine learning molecular dynamics

Fei-Teng Wang and Jun Cheng

Current Opinion in Electrochemistry, 2025, 49, 101605
DOI: 10.1016/j.coelec.2024.101605

Raman Spectra of Amino Acids and Peptides from Machine Learning Polarizabilities

Ethan Berger, Juha Niemelä, Outi Lampela, André H. Juffer and Hannu-Pekka Komsa

J. Chem. Inf. Model., 2024, 64, 4601
DOI: 10.1021/acs.jcim.4c00077

Viscosity in water from first-principles and deep-neural-network simulations

Cesare Malosso, Linfeng Zhang, Roberto Car, Stefano Baroni and Davide Tisi

npj Comput Mater, 2022, 8
DOI: 10.1038/s41524-022-00830-7

Finite-field coupling via learning the charge response kernel

Yunqi Shao, Linnéa Andersson, Lisanne Knijff and Chao Zhang

Electron. Struct., 2022, 4, 014012
DOI: 10.1088/2516-1075/ac59ca

Gaussian Process Regression for Materials and Molecules

Volker L. Deringer, Albert P. Bartók, Noam Bernstein, David M. Wilkins, Michele Ceriotti and Gábor Csányi

Chem. Rev., 2021, 121, 10073
DOI: 10.1021/acs.chemrev.1c00022

DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models

Denghui Lu, Wanrun Jiang, Yixiao Chen, Linfeng Zhang, Weile Jia, Han Wang and Mohan Chen

J. Chem. Theory Comput., 2022, 18, 5559
DOI: 10.1021/acs.jctc.2c00102

Hydrogen bond network modes in liquid water

David P. Shelton

Phys. Rev. B, 2023, 108
DOI: 10.1103/PhysRevB.108.174203

DeePMD-kit v2: A software package for deep potential models

Jinzhe Zeng, Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen, Marián Rynik, Li’ang Huang, Ziyao Li, Shaochen Shi, Yingze Wang, Haotian Ye, Ping Tuo, Jiabin Yang, Ye Ding, Yifan Li, Davide Tisi, Qiyu Zeng, Han Bao, Yu Xia, Jiameng Huang, Koki Muraoka, Yibo Wang, Junhan Chang, Fengbo Yuan, Sigbjørn Løland Bore, Chun Cai, Yinnian Lin, Bo Wang, Jiayan Xu, Jia-Xin Zhu, Chenxing Luo, Yuzhi Zhang, Rhys E. A. Goodall, Wenshuo Liang, Anurag Kumar Singh, Sikai Yao, Jingchao Zhang, Renata Wentzcovitch, Jiequn Han, Jie Liu, Weile Jia, Darrin M. York, Weinan E, Roberto Car, Linfeng Zhang and Han Wang

The Journal of Chemical Physics, 2023, 159
DOI: 10.1063/5.0155600

Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors

Willis O’Leary, Manuel Grumet, Waldemar Kaiser, Tomáš Bučko, Jennifer L. M. Rupp and David A. Egger

J. Am. Chem. Soc., 2024, 146, 26863
DOI: 10.1021/jacs.4c07812

General Framework for Geometric Deep Learning on Tensorial Properties of Molecules and Crystals

Wenjie Yan, Xinming Lai, Yicheng Chen, Wenhao Zhang, Jianming Wu and Xin Xu

J. Am. Chem. Soc., 2025, 147, 47044
DOI: 10.1021/jacs.5c12428

Machine Learning Accelerates Molten Salt Simulations: Thermal Conductivity of MgCl2‐NaCl Eutectic

Wenshuo Liang, Guimin Lu and Jianguo Yu

Advcd Theory and Sims, 2022, 5
DOI: 10.1002/adts.202200206

Raman Spectra of Electrified Si–Water Interfaces: First-Principles Simulations

Zifan Ye, Francois Gygi and Giulia Galli

J. Phys. Chem. Lett., 2024, 15, 51
DOI: 10.1021/acs.jpclett.3c03122

Atomistic simulations of oxide–water interfaces using machine learning potentials

Jan Elsner, K. Nikolas Lausch and Jörg Behler

Chemical Physics Reviews, 2025, 6
DOI: 10.1063/5.0268292

Transferability of the chemical-bond-based machine learning model for dipole moment: The GHz to THz dielectric properties of liquid propylene glycol and polypropylene glycol

Tomohito Amano, Tamio Yamazaki, Naoki Matsumura, Yuta Yoshimoto and Shinji Tsuneyuki

Phys. Rev. B, 2025, 111
DOI: 10.1103/PhysRevB.111.165149

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction

Nitik Bhatia, Patrick Rinke and Ondřej Krejčí

npj Comput Mater, 2025, 11
DOI: 10.1038/s41524-025-01827-8

An equivariant neural operator for developing nonlocal tensorial constitutive models

Jiequn Han, Xu-Hui Zhou and Heng Xiao

Journal of Computational Physics, 2023, 488, 112243
DOI: 10.1016/j.jcp.2023.112243

Revealing the molecular structures of α-Al2O3(0001)–water interface by machine learning based computational vibrational spectroscopy

Xianglong Du, Weizhi Shao, Chenglong Bao, Linfeng Zhang, Jun Cheng and Fujie Tang

The Journal of Chemical Physics, 2024, 161
DOI: 10.1063/5.0230101

A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?

Yaoguang Zhai, Alessandro Caruso, Sigbjørn Løland Bore, Zhishang Luo and Francesco Paesani

The Journal of Chemical Physics, 2023, 158
DOI: 10.1063/5.0142843

Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra

Manuel Grumet, Clara von Scarpatetti, Tomáš Bučko and David A. Egger

J. Phys. Chem. C, 2024, 128, 6464
DOI: 10.1021/acs.jpcc.4c00886

Machine-Learning-Driven Simulations on Microstructure and Thermophysical Properties of MgCl2–KCl Eutectic

Wenshuo Liang, Guimin Lu and Jianguo Yu

ACS Appl. Mater. Interfaces, 2021, 13, 4034
DOI: 10.1021/acsami.0c20665

Anharmonic Raman spectra simulation of crystals from deep neural networks

Honghui Shang and Haidi Wang

AIP Advances, 2021, 11
DOI: 10.1063/5.0040190

Completely Computational Model Setup for Spectroscopic Techniques: The Ab Initio Molecular Dynamics Indirect Hard Modeling Approach

Justus Wöhl, Wassja A. Kopp, Iryna Yevlakhovych, Leo Bahr, Hans-Jürgen Koß and Kai Leonhard

J. Phys. Chem. A, 2022, 126, 2845
DOI: 10.1021/acs.jpca.2c01061

Confinement Effects on Proton Transfer in TiO2 Nanopores from Machine Learning Potential Molecular Dynamics Simulations

Hyuna Kwon, Marcos F. Calegari Andrade, Shane Ardo, Daniel V. Esposito, Tuan Anh Pham and Tadashi Ogitsu

ACS Appl. Mater. Interfaces, 2024, 16, 31687
DOI: 10.1021/acsami.4c02339

Progress in machine learning-accelerated computational spectroscopy

Xianglong Du, Qi You, Jiezhen Xia, Fujie Tang, Jun Cheng and Zhongqun Tian

Sci. Sin.-Chim, 2025, 55, 1715
DOI: 10.1360/SSC-2025-0078

E(n)-Equivariant cartesian tensor message passing interatomic potential

Junjie Wang, Yong Wang, Haoting Zhang, Ziyang Yang, Zhixin Liang, Jiuyang Shi, Hui-Tian Wang, Dingyu Xing and Jian Sun

Nat Commun, 2024, 15
DOI: 10.1038/s41467-024-51886-6

Simulation of Raman-Spectra of water using machine learning potentials

Jan Eckwert, Raja Armughan Ahmed, Wassja Alexander Kopp and Kai Leonhard

Chemical Physics, 2025, 595, 112698
DOI: 10.1016/j.chemphys.2025.112698

Impact of interfacial curvature on molecular properties of aqueous interfaces

M. de la Puente and D. Laage

The Journal of Chemical Physics, 2024, 160
DOI: 10.1063/5.0210884

Advancements in Cancer Research: 3D Models, Single‐Cell, and Live‐Cell Techniques for Better Insights

Federica Carnevali, Stefania Forciniti, Valentina Onesto, Anna Chiara Siciliano, Helena Iuele, Giuliana Grasso, Anderson Fraga da Cruz, Ilaria Serra, Norma Depalma, Stefano D'Ugo, Prisco Piscitelli, Marcello G. Spampinato, Giuseppe Gigli, Carolina Camargo de Oliveira and Loretta L. del Mercato

Advanced Therapeutics, 2024, 7
DOI: 10.1002/adtp.202400351

Generalized bond polarizability model for more accurate atomistic modeling of Raman spectra

Atanu Paul, Nagaprasad Reddy Samala and Ilya Grinberg

The Journal of Chemical Physics, 2025, 162
DOI: 10.1063/5.0246427

Atomistic Approach for Modeling of Polarizability and Raman Scattering of Water Clusters and Liquid Water

Atanu Paul and Ilya Grinberg

J. Chem. Theory Comput., 2025, 21, 7017
DOI: 10.1021/acs.jctc.5c00452

Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors

Hao Ren, Qian Zhang, Zhengjie Wang, Guozhen Zhang, Hongzhang Liu, Wenyue Guo, Shaul Mukamel and Jun Jiang

Proc. Natl. Acad. Sci. U.S.A., 2022, 119
DOI: 10.1073/pnas.2202713119

Polarizability relaxation in protic ionic liquids: The case of alkylammonium nitrates

Ary de O. Cavalcante

Journal of Molecular Liquids, 2025, 439, 128815
DOI: 10.1016/j.molliq.2025.128815

Enhancement of classical force field predictions of hydrogen bonding in water, methanol and their crossed dimers using machine learning

Emil Obeid, Ahmet E. Topcu, Lina Khader, Nimer Murshid and Mahmoud Abu Samha

Molecular Simulation, 2025, 51, 478
DOI: 10.1080/08927022.2025.2501382

Molecular Fingerprints of Ice Surfaces in Sum Frequency Generation Spectra: A First-Principles Machine Learning Study

Margaret L. Berrens, Marcos F. Calegari Andrade, John T. Fourkas, Tuan Anh Pham and Davide Donadio

JACS Au, 2025, 5, 1173
DOI: 10.1021/jacsau.4c00957

Δ-Machine Learning of Polarizability Tensors Using a Dipole Interaction Model

Imran Chaudhry, Mark J. Bronson and Lasse Jensen

J. Chem. Theory Comput., 2025, 21, 7040
DOI: 10.1021/acs.jctc.5c00752

Exploring Spectrum‐based Molecular Descriptors for Reaction Performance Prediction

Miao‐Jiong Tang, Li‐Cheng Xu, Shuo‐Qing Zhang and Xin Hong

Chemistry An Asian Journal, 2023, 18
DOI: 10.1002/asia.202300011

Computer-Aided Design of Hand-Drawn art Food Packaging Design Based on Deep Neural Network Model

Xiaobing Liu

Applied Mathematics and Nonlinear Sciences, 2023, 8, 2043
DOI: 10.2478/amns.2023.1.00308

Chemical bond based machine learning model for dipole moment: Application to dielectric properties of liquid methanol and ethanol

Tomohito Amano, Tamio Yamazaki and Shinji Tsuneyuki

Phys. Rev. B, 2024, 110
DOI: 10.1103/PhysRevB.110.165159

A deep potential model with long-range electrostatic interactions

Linfeng Zhang, Han Wang, Maria Carolina Muniz, Athanassios Z. Panagiotopoulos, Roberto Car and Weinan E

The Journal of Chemical Physics, 2022, 156
DOI: 10.1063/5.0083669

Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations

Pablo M. Piaggi and Roberto Car

Molecular Physics, 2021, 119
DOI: 10.1080/00268976.2021.1916634

Fully First-Principles Surface Spectroscopy with Machine Learning

Yair Litman, Jinggang Lan, Yuki Nagata and David M. Wilkins

J. Phys. Chem. Lett., 2023, 14, 8175
DOI: 10.1021/acs.jpclett.3c01989

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

Zheyu Zhang, Yaoxiang Li and Ying Li

Front. Plant Sci., 2022, 13
DOI: 10.3389/fpls.2022.1006292

Beyond potentials: Integrated machine learning models for materials

Michele Ceriotti

MRS Bulletin, 2022, 47, 1045
DOI: 10.1557/s43577-022-00440-0

Probing Confinement Effects on the Infrared Spectra of Water with Deep Potential Molecular Dynamics Simulations

Marcos F. Calegari Andrade and Tuan Anh Pham

J. Phys. Chem. Lett., 2023, 14, 5560
DOI: 10.1021/acs.jpclett.3c01054

Intelligent Computing: The Latest Advances, Challenges, and Future

Shiqiang Zhu, Ting Yu, Tao Xu, Hongyang Chen, Schahram Dustdar, Sylvain Gigan, Deniz Gunduz, Ekram Hossain, Yaochu Jin, Feng Lin, Bo Liu, Zhiguo Wan, Ji Zhang, Zhifeng Zhao, Wentao Zhu, Zuoning Chen, Tariq S. Durrani, Huaimin Wang, Jiangxing Wu, Tongyi Zhang and Yunhe Pan

Intell Comput, 2023, 2
DOI: 10.34133/icomputing.0006

A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra

Jakub K. Sowa and Peter J. Rossky

J. Chem. Theory Comput., 2024, 20, 10071
DOI: 10.1021/acs.jctc.4c01086

Yong Wang, Yifan Li, Linhan Du, Chunyi Zhang, Lorenzo Agosta, Marcos Calegari Andrade, Annabella Selloni and Roberto Car

Phys. Rev. Lett., 2026, 136
DOI: 10.1103/w27h-sclq

Homogeneous ice nucleation in an ab initio machine-learning model of water

Pablo M. Piaggi, Jack Weis, Athanassios Z. Panagiotopoulos, Pablo G. Debenedetti and Roberto Car

Proc. Natl. Acad. Sci. U.S.A., 2022, 119
DOI: 10.1073/pnas.2207294119

Machine Learning Potentials for Heterogeneous Catalysis

Amir Omranpour, Jan Elsner, K. Nikolas Lausch and Jörg Behler

ACS Catal., 2025, 15, 1616
DOI: 10.1021/acscatal.4c06717

Enhancing transferability of machine learning-based polarizability models in condensed-phase systems via atomic polarizability constraint

Mandi Fang, Yinqiao Zhang, Zheyong Fan, Daquan Tan, Xiaoyong Cao, Chunlei Wei, Nan Xu and Yi He

npj Comput Mater, 2025, 11
DOI: 10.1038/s41524-025-01705-3

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DOI: 10.1063/5.0119251

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Chaoqiang Feng, Jin Xi, Yaolong Zhang, Bin Jiang and Yong Zhou

J. Chem. Theory Comput., 2023, 19, 1207
DOI: 10.1021/acs.jctc.2c01094

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Sci. Sin.-Chim, 2024, 54, 93
DOI: 10.1360/SSC-2023-0205

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Kazuki Inoue, Yair Litman, David M. Wilkins, Yuki Nagata and Masanari Okuno

J. Phys. Chem. Lett., 2023, 14, 3063
DOI: 10.1021/acs.jpclett.3c00398

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Stefano Falletta, Andrea Cepellotti, Anders Johansson, Chuin Wei Tan, Marc L. Descoteaux, Albert Musaelian, Cameron J. Owen and Boris Kozinsky

Nat Commun, 2025, 16
DOI: 10.1038/s41467-025-59304-1

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Int J of Quantum Chemistry, 2023, 123
DOI: 10.1002/qua.27102

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Philipp Pracht, Yuthika Pillai, Venkat Kapil, Gábor Csányi, Nils Gönnheimer, Martin Vondrák, Johannes T. Margraf and David J. Wales

J. Chem. Theory Comput., 2024, 20, 10986
DOI: 10.1021/acs.jctc.4c01157

Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids

Xu-Hui Zhou, Jiequn Han and Heng Xiao

Computer Methods in Applied Mechanics and Engineering, 2022, 388, 114211
DOI: 10.1016/j.cma.2021.114211

First-principles calculations of the viscosity in multicomponent metallic melts: Al-Cu-Ni as a test case

Nikolay Kondratyuk, Roman Ryltsev, Vladimir Ankudinov and Nikolay Chtchelkatchev

Journal of Molecular Liquids, 2023, 380, 121751
DOI: 10.1016/j.molliq.2023.121751

Two-Component Dynamics and the Liquidlike to Gaslike Crossover in Supercritical Water

Peihao Sun, J. B. Hastings, Daisuke Ishikawa, Alfred Q. R. Baron and Giulio Monaco

Phys. Rev. Lett., 2020, 125
DOI: 10.1103/PhysRevLett.125.256001

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Luyuan Zhao, Jinxiao Zhang, Yaolong Zhang, Sheng Ye, Guozhen Zhang, Xin Chen, Bin Jiang and Jun Jiang

JACS Au, 2021, 1, 2377
DOI: 10.1021/jacsau.1c00449

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Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko and Klaus-Robert Müller

Chem. Rev., 2021, 121, 10142
DOI: 10.1021/acs.chemrev.0c01111