On representing chemical environments

Cited by (1,001)

Items 601–700 of 1,001

1. Ceramic science of crystal defect cores
   Katsuyuki Matsunaga, Masato Yoshiya, Naoya Shibata, Hiromichi Ohta, Teruyasu Mizoguchi · 2022 · Journal of the Ceramic Society of Japan
2. Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
   Atsuto Seko, Atsushi Togo, Isao Tanaka · 2019 · Physical review. B./Physical review. B
3. Charge and Exciton Transfer Simulations Using Machine-Learned Hamiltonians
   Mila Krämer, Philipp M. Dohmen, Weiwei Xie, Daniel Holub, Anders Steen Christensen, Marcus Elstner · 2020 · Journal of Chemical Theory and Computation
4. Finding symmetry breaking order parameters with Euclidean neural networks
   Tess Smidt, Mario Geiger, B. Miller · 2021 · Physical Review Research
5. Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers
   Chee‐Kong Lee, Chengqiang Lu, Yue Yu, Qiming Sun, Chang‐Yu Hsieh, Shengyu Zhang, Qi Liu, Liang Shi · 2021 · The Journal of Chemical Physics
6. Machine learning accelerates quantum mechanics predictions of molecular crystals
   Yanqiang Han, Imran Ali, Zhilong Wang, Junfei Cai, Sicheng Wu, Jiequn Tang, Lin Zhang, Jiahao Ren, Rui Xiao, Qianqian Lu et al. · 2021 · Physics Reports
7. Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
   Paul L. Houston, Chen Qu, Apurba Nandi, Riccardo Conte, Qi Yu, Joel M. Bowman · 2022 · The Journal of Chemical Physics
8. Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation
   Wenbin Xu, Karsten Reuter, Mie Andersen · 2022 · Nature Computational Science
9. Machine Learning Assisted Simulations of Electrochemical Interfaces: Recent Progress and Challenges
   Yipeng Zhou, Yixin Ouyang, Yehui Zhang, Qiang Li, Jinlan Wang · 2023 · The Journal of Physical Chemistry Letters
10. Reactivity of Single-Atom Alloy Nanoparticles: Modeling the Dehydrogenation of Propane
    Rhys J. Bunting, Felix Wodaczek, Tina Torabi, Bingqing Cheng · 2023 · Journal of the American Chemical Society
11. Enhancing Structure–Property Relationships in Porous Materials through Transfer Learning and Cross-Material Few-Shot Learning
    Hyunsoo Park, Yeonghun Kang, Jihan Kim · 2023 · ACS Applied Materials & Interfaces
12. Machine-learning structural reconstructions for accelerated point defect calculations
    Irea Mosquera‐Lois, Seán R. Kavanagh, Alex M. Ganose, Aron Walsh · 2024 · npj Computational Materials
13. Roadmap on machine learning glassy dynamics
    Gerhard Jung, R. M. Alkemade, V. Bapst, D. Coslovich, L. Filion, François P. Landes, Andrea Liu, F. Pezzicoli, Hayato Shiba, G. Volpe et al. · 2023 · Nature Reviews Physics
14. Making the Coupled Cluster Correlation Energy Machine-Learnable
    Johannes T. Margraf, Karsten Reuter · 2018 · The Journal of Physical Chemistry A
15. Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments
    Michael S. Chen, Tim Joachim Zuehlsdorff, Tobias Morawietz, Christine M. Isborn, Thomas E. Markland · 2020 · The Journal of Physical Chemistry Letters
16. Ab Initio Calculations of Free Energy of Activation at Multiple Electronic Structure Levels Made Affordable: An Effective Combination of Perturbation Theory and Machine Learning
    Tomáš Bučko, Monika Gešvandtnerová, Dario Rocca · 2020 · Journal of Chemical Theory and Computation
17. Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide
    Ganesh Sivaraman, Leighanne C. Gallington, Anand Narayanan Krishnamoorthy, Marius Stan, Gábor Cśanyi, Álvaro Vázquez‐Mayagoitia, Chris J. Benmore · 2021 · Physical Review Letters
18. Deep-Learning Approach to First-Principles Transport Simulations
    Marius Bürkle, Umesha Perera, Florian Gimbert, Hisao Nakamura, Masaaki Kawata, Yoshihiro Asai · 2021 · Physical Review Letters
19. Anharmonic lattice dynamics in large thermodynamic ensembles with machine-learning force fields: CsPbBr3 , a phonon liquid with Cs rattlers
    Jonathan Lahnsteiner, Menno Bokdam · 2022 · Physical review. B./Physical review. B
20. A reactive neural network framework for water-loaded acidic zeolites
    Erlebach, Andreas, Šípka, Martin, Saha, Indranil, Nachtigall, Petr, Heard, Christopher J., Grajciar, Lukáš · 2024 · Nature Communications
21. Quantum dynamics using path integral coarse-graining
    Félix Musil, Iryna Zaporozhets, Frank Noé, Cecilia Clementi, Venkat Kapil · 2022 · The Journal of Chemical Physics
22. Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)
    Peitao Liu, Jian-Tao Wang, Noah Avargues, Carla Verdi, Andreas Singraber, Ferenc Karsai, Xing‐Qiu Chen, Georg Kresse · 2023 · Physical Review Letters
23. Spin-Phonon Relaxation in Magnetic Molecules: Theory, Predictions and Insights
    Alessandro Lunghi · 2023 · Challenges and advances in computational chemistry and physics
24. Silicon phase transitions in nanoindentation: Advanced molecular dynamics simulations with machine learning phase recognition
    Guojia Ge, Fabrizio Rovaris, Daniele Lanzoni, Luca Barbisan, Xiaobin Tang, Leo Miglio, Anna Marzegalli, Emilio Scalise, Francesco Montalenti · 2023 · Acta Materialia
25. Ab Initio Vibrational Spectroscopy ofcis- andtrans-Formic Acid from a Global Potential Energy Surface
    David P. Tew, Wataru Mizukami · 2016 · The Journal of Physical Chemistry A
26. Convolutional neural networks for atomistic systems
    Kevin Ryczko, Kyle Mills, Iryna Luchak, Christa M. Homenick, Isaac Tamblyn · 2018 · Computational Materials Science
27. Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation
    Ole Schütt, Joost VandeVondele · 2018 · Journal of Chemical Theory and Computation
28. Approximate high mode coupling potentials using Gaussian process regression and adaptive density guided sampling
    Gunnar Alexander Schmitz, Denis G. Artiukhin, Ove Christiansen A · 2019 · The Journal of Chemical Physics
29. A novel approach to describe chemical environments in high-dimensional neural network potentials
    Emir Kocer, Jeremy K. Mason, Hakan Ertürk · 2019 · The Journal of Chemical Physics
30. General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods
    Rohit Batra, Tran Doan Huan, Chiho Kim, James Chapman, Lihua Chen, Anand R. Chandrasekaran, Rampi Ramprasad · 2019 · The Journal of Physical Chemistry C
31. Molecular machine learning with conformer ensembles
    Simon Axelrod, Rafael Gómez‐Bombarelli · 2023 · Machine Learning Science and Technology
32. Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison
    David Rosenberger, Justin S. Smith, Angel E. Garcı́a · 2021 · The Journal of Physical Chemistry B
33. Predicting tensorial molecular properties with equivariant machine learning models
    Nguyen Vu Ha Anh, Alessandro Lunghi · 2022 · Physical review. B./Physical review. B
34. Electron-Volt Fluctuation of Defect Levels in Metal Halide Perovskites on a 100 ps Time Scale
    Bipeng Wang, Weibin Chu, Yifan Wu, David Casanova, Wissam Abdo Saidi, Oleg V. Prezhdo · 2022 · The Journal of Physical Chemistry Letters
35. Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration
    Sina Stocker, Hyunwook Jung, Gábor Cśanyi, Claude Franklin Goldsmith, Karsten Reuter, Johannes T. Margraf · 2023 · Journal of Chemical Theory and Computation
36. Chemiscope: interactive structure-property explorer for materials and molecules
    Guillaume Fraux, Rose K. Cersonsky, Michele Ceriotti · 2020 · The Journal of Open Source Software
37. Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects
    Théo Jaffrelot Inizan, Thomas Plé, Olivier Adjoua, Pengyu Y. Ren, Hatice Gökcan, Olexandr Isayev, Louis Lagardère, Jean‐Philip Piquemal · 2023 · Chemical Science
38. Lifelong Machine Learning Potentials
    Marco Eckhoff, Markus Reiher · 2023 · Journal of Chemical Theory and Computation
39. Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials
    Jiayan Xu, Xiaoming Cao, P. Hu · 2021 · Journal of Chemical Theory and Computation
40. Thermal Conductivity of Silicate Liquid Determined by Machine Learning Potentials
    Jie Deng, Lars P. Stixrude · 2021 · Geophysical Research Letters
41. Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?
    Laine, Elodie, Eismann, Stephan, Elofsson, Arne, Grudinin, Sergei · 2021 · Proteins: Structure, Function, and Bioinformatics
42. Combining phonon accuracy with high transferability in Gaussian approximation potential models
    George, Janine, Hautier, Geoffroy, Bartók, Albert P., Csányi, Gábor, Deringer, Volker L. · 2020 · The Journal of Chemical Physics
43. Prediction of the Electron Density of States for Crystalline Compounds with Atomistic Line Graph Neural Networks (ALIGNN)
    Prathik R. Kaundinya, Kamal Kumar Choudhary, Surya R. Kalidindi · 2022 · JOM
44. Melting of MgSi O 3 determined by machine learning potentials
    Jie Deng, Haiyang Niu, Junwei Hu, Mingyi Chen, Lars P. Stixrude · 2023 · Physical review. B./Physical review. B
45. Machine-learned interatomic potentials: Recent developments and prospective applications
    Volker Eyert, Jonathan L. Wormald, W.A. Curtin, E. Wimmer · 2023 · Journal of materials research/Pratt's guide to venture capital sources
46. An overview about neural networks potentials in molecular dynamics simulation
    Raidel Martin‐Barrios, Edisel Navas Conyedo, Xuyi Zhang, Yunwei Chen, Jorge Gulı́n González · 2024 · International Journal of Quantum Chemistry
47. Strain Engineering: Reduction of Microstrain at the Perovskite Surface via Alkali Metal Chloride Treatment Enhances Stability
    Do-Kyoung Lee, Kostas Fykouras, Tim Kodalle, Raphael F. Moral, Craig Philip Schwartz, Nobumichi Tamura, Keith V. Lawler, Linn Leppert, Carolin M. Sutter‐Fella · 2025 · ACS Energy Letters
48. Full and fragmented permutationally invariant polynomial potential energy surfaces for trans and cis N -methyl acetamide and isomerization saddle points
    Apurba Nandi, Chen Qu, Joel M. Bowman · 2019 · The Journal of Chemical Physics
49. Hierarchically Structured Allotropes of Phosphorus from Data‐Driven Exploration
    Volker L. Deringer, Chris J. Pickard, Davide Maria Proserpio · 2020 · Angewandte Chemie International Edition
50. SingleNN: Modified Behler–Parrinello Neural Network with Shared Weights for Atomistic Simulations with Transferability
    Mingjie Liu, John R. Kitchin · 2020 · The Journal of Physical Chemistry C
51. Machine Learning Estimates of Natural Product Conformational Energies
    Matthias Rupp, Matthias R. Bauer, Rainer Wilcken, Andreas Lange, Michael Reutlinger, Frank M. Boeckler, Gisbert Schneider · 2014 · PLoS Computational Biology
52. Attention-wise masked graph contrastive learning for predicting molecular property
    Hui Liu, Yibiao Huang, Xuejun Liu, Lei Deng · 2022 · Briefings in Bioinformatics
53. Materials property prediction with uncertainty quantification: A benchmark study
    Daniel Varivoda, Rongzhi Dong, Sadman Sadeed Omee, Jianjun Hu · 2023 · Applied Physics Reviews
54. Viewpoint: Can symmetric tilt grain boundaries represent polycrystals?
    Malik Wagih, Christopher Allan Schuh · 2023 · Scripta Materialia
55. Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential
    Lei Zhang, Gábor Cśanyi, Erik Van der Giessen, Francesco Maresca · 2023 · npj Computational Materials
56. Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses
    Marco Bertani, Thibault Charpentier, Francesco Faglioni, Alfonso Pedone · 2024 · Journal of Chemical Theory and Computation
57. Atomic-level mechanisms of short-circuit diffusion in materials
    Ian Chesser, Raj Kiran Koju, Yuri M. Mishin · 2024 · International Journal of Materials Research (formerly Zeitschrift fuer Metallkunde)
58. General reactive element-based machine learning potentials for heterogeneous catalysis
    Changxi Yang, Chenyu Wu, Wenbo Xie, Daiqian Xie, P. Hu · 2025 · Nature Catalysis
59. Data Science Based Mg Corrosion Engineering
    Tim Würger, Christian Feiler, Félix Musil, Gregor B. Vonbun‐Feldbauer, Daniel Höche, Sviatlana V. Lamaka, Mikhail L. Zheludkevich, Robert H. Meißner · 2019 · Frontiers in Materials
60. A new kind of atlas of zeolite building blocks
    Benjamin A. Helfrecht, Rocío Semino, Giovanni Pireddu, Scott M. Auerbach, Michele Ceriotti · 2019 · The Journal of Chemical Physics
61. Structure-property maps with Kernel principal covariates regression
    Benjamin A. Helfrecht, Rose K. Cersonsky, Guillaume Fraux, Michele Ceriotti · 2020 · Machine Learning Science and Technology
62. Efficient Generation of Permutationally Invariant Potential Energy Surfaces for Large Molecules
    Riccardo Conte, Chen Qu, Paul L. Houston, Joel M. Bowman · 2020 · Journal of Chemical Theory and Computation
63. Reaction prediction via atomistic simulation: from quantum mechanics to machine learning
    Pei‐Lin Kang, Zhi‐Pan Liu · 2020 · iScience
64. Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules
    Valentín Vassilev-Galindo, Grégory Fonseca, Igor Poltavsky, Alexandre Tkatchenko · 2021 · The Journal of Chemical Physics
65. Nonperturbative phonon scatterings and the two-channel thermal transport in Tl3VSe4
    Zezhu Zeng, Cunzhi Zhang, Yi Xia, Zheyong Fan, Chris M. Wolverton, Yue Chen · 2021 · Physical review. B./Physical review. B
66. AugLiChem: data augmentation library of chemical structures for machine learning
    Rishikesh Magar, Yuyang Wang, Cooper Lorsung, Liang Chen, Hariharan Ramasubramanian, Peiyuan Li, Amir Barati Farimani · 2022 · Machine Learning Science and Technology
67. Accelerating the structure search of catalysts with machine learning
    Eric Musa, Francis Doherty, Bryan R. Goldsmith · 2021 · Current Opinion in Chemical Engineering
68. Unified theory of atom-centered representations and message-passing machine-learning schemes
    Jigyasa Nigam, Sergey N. Pozdnyakov, Guillaume Fraux, Michele Ceriotti · 2022 · The Journal of Chemical Physics
69. Hierarchical Inter-Message Passing for Learning on Molecular Graphs
    Matthias Fey, Jan-Gin Yuen, Frank Weichert · 2020 · arXiv (Cornell University)
70. Machine learning for the design and discovery of zeolites and porous crystalline materials
    Akhilesh Gandhi, M. M. Faruque Hasan · 2021 · Current Opinion in Chemical Engineering
71. The role of machine learning in perovskite solar cell research
    Chen Chen, Ayman Maqsood, T. Jesper Jacobsson · 2023 · Journal of Alloys and Compounds
72. Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
    Adil Kabylda, Valentín Vassilev-Galindo, Stefan Chmiela, Igor Poltavsky, Alexandre Tkatchenko · 2023 · Nature Communications
73. Tensor-Reduced Atomic Density Representations
    James P. Darby, Dávid Péter Kovács, Ilyes Batatia, A. Miguel, Gus L. W. Hart, Christoph Ortner, Gábor Cśanyi · 2023 · Physical Review Letters
74. Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities
    Wojciech G. Stark, Julia Westermayr, Oscar A. Douglas‐Gallardo, James Gardner, Scott Habershon, Reinhard J. Maurer · 2023 · The Journal of Physical Chemistry C
75. Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon
    Tigany Zarrouk, Rina Ibragimova, Albert P. Bartók, Miguel A. Caro · 2024 · Journal of the American Chemical Society
76. Efficiency, accuracy, and transferability of machine learning potentials: Application to dislocations and cracks in iron
    Lei Zhang, Gábor Cśanyi, Erik Van der Giessen, Francesco Maresca · 2024 · Acta Materialia
77. Robustness of the cluster expansion: Assessing the roles of relaxation and numerical error
    Andrew Huy Nguyen, Conrad W. Rosenbrock, C. Shane Reese, Gus L. W. Hart · 2017 · Physical review. B./Physical review. B
78. Atomic energy mapping of neural network potential
    Dongsun Yoo, Kyuhyun Lee, Wonseok Jeong, Dongheon Lee, Satoshi Watanabe, Seungwu Han · 2019 · Physical Review Materials
79. NMR shifts in aluminosilicate glasses via machine learning
    Ziyad Chaker, Mathieu Salanne, Jean‐Marc Delaye, Thibault Charpentier · 2019 · Physical Chemistry Chemical Physics
80. Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters
    Marc O. J. Jäger, Yashasvi S. Ranawat, Filippo Federici Canova, Eiaki V. Morooka, Adam Stuart Foster · 2020 · ACS Combinatorial Science
81. Euclidean Symmetry and Equivariance in Machine Learning
    Tess Smidt · 2020 · Trends in Chemistry
82. CrySPY: a crystal structure prediction tool accelerated by machine learning
    Tomoki Yamashita, Shinichi Kanehira, Nobuya Sato, Hiori Kino, Kei Terayama, Hikaru Sawahata, Takumi Sato, Futoshi Utsuno, Koji Tsuda, Takashi Miyake et al. · 2021 · Science and Technology of Advanced Materials Methods
83. Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model
    Carsten G. Staacke, Simon Wengert, Christian Künkel, Gábor Cśanyi, Karsten Reuter, Johannes T. Margraf · 2022 · Machine Learning Science and Technology
84. High-pressure hydrogen by machine learning and quantum Monte Carlo
    Andrea Tirelli, Giacomo Tenti, Kousuke Nakano, Sandro Sorella · 2022 · Physical review. B./Physical review. B
85. Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
    Yuge Hu, Joseph Musielewicz, Zachary W. Ulissi, Andrew James Medford · 2022 · Machine Learning Science and Technology
86. Quantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases
    Yang Yang, Ka Un Lao, David M. Wilkins, Andrea Grisafi, Michele Ceriotti, Robert A. DiStasio · 2019 · Scientific Data
87. Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment
    Iovanac, Nicolae C., Savoie, Brett M. · 2019 · The Journal of Physical Chemistry A
88. Insights into the primary radiation damage of silicon by a machine learning interatomic potential
    A. Hamedani, Jesper Byggmästar, Flyura Djurabekova, Ghasem Alahyarizadeh, Reza Ghaderi, A. Minuchehr, K. Nordlund · 2020 · Materials Research Letters
89. Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers
    Eleonora Ricci, Niki Vergadou · 2023 · The Journal of Physical Chemistry B
90. Structural Dynamics Descriptors for Metal Halide Perovskites
    Xia Liang, Johan Klarbring, William J. Baldwin, Zhenzhu Li, Gábor Cśanyi, Aron Walsh · 2023 · The Journal of Physical Chemistry C
91. Capturing short-range order in high-entropy alloys with machine learning potentials
    Yifan Cao, Killian Sheriff, Rodrigo Moura Freitas · 2025 · npj Computational Materials
92. A multimodal large language model for materials science
    Yingheng Tang, Wenbin Xu, Jie Cao, Jianzhu Ma, Weilu Gao, Steven Farrell, N. Benjamin Erichson, Michael W. Mahoney, Andy Nonaka, Zhi Yao · 2026 · Nature Machine Intelligence
93. Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water
    Chungwen Liang, Gabriele Tocci, David M. Wilkins, Andrea Grisafi, Sylvie Roke, Michele Ceriotti · 2017 · Physical review. B./Physical review. B
94. Thermal conductivity modeling of hybrid organic-inorganic crystals and superlattices
    Xin Qian, Xiaokun Gu, Ronggui Yang · 2017 · Nano Energy
95. Quantum-Chemical Insights from Interpretable Atomistic Neural Networks
    Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus‐Robert Müller · 2019 · Lecture notes in computer science
96. On machine learning force fields for metallic nanoparticles
    Claudio Zeni, Kevin Rossi, Aldo Glielmo, Francesca Baletto · 2019 · Advances in Physics X
97. Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression
    Chiara Panosetti, Artur Engelmann, Lydia Nemec, Karsten Reuter, Johannes T. Margraf · 2020 · Journal of Chemical Theory and Computation
98. Adversarial reverse mapping of equilibrated condensed-phase molecular structures
    Marc Stieffenhofer, Michael Wand, Tristan Bereau · 2020 · Machine Learning Science and Technology
99. Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of MnxGey compounds
    Claudia Mangold, Shunda Chen, Giuseppe Barbalinardo, Jörg Behler, Pascal Pochet, Konstantinos Termentzidis, Yang Han, Laurent Chaput, David Lacroix, Davide Donadio · 2020 · Journal of Applied Physics
100. Improving sample and feature selection with principal covariates regression
     Rose K. Cersonsky, Benjamin A. Helfrecht, Edgar A. Engel, Sergei Kliavinek, Michele Ceriotti · 2021 · Machine Learning Science and Technology