Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Cited by (1,039)

Items 501–600 of 1,039

1. An Unlikely Route to Low Lattice Thermal Conductivity: Small Atoms in a Simple Layered Structure
   Wanyue Peng, Guido Petretto, Gian‐Marco Rignanese · 2018 · Joule
2. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
   Rama Krishnan Vasudevan, Kamal Kumar Choudhary, Apurva Mehta, Ryan P. Smith, Aaron Gilad Kusne, Francesca Tavazza, Lukáš Vlček, Maxim A Ziatdinov, Sergei V. Kalinin, Jason R. Hattrick-Simpers · 2019 · MRS Communications
3. Machine Learning for Atomic Simulation and Activity Prediction in Heterogeneous Catalysis: Current Status and Future
   Sicong Ma, Zhi‐Pan Liu · 2020 · ACS Catalysis
4. Machine learning in combinatorial polymer chemistry
   A. Gormley, Michael A. Webb · 2021 · Nature Reviews Materials
5. Design and exploration of semiconductors from first principles: A review of recent advances
   Fumiyasu Oba, Yu Kumagai · 2018 · Applied Physics Express
6. A monoclinic polymorph of sodium birnessite for ultrafast and ultrastable sodium ion storage
   Hui Xia, Xiaohui Zhu, Jizi Liu et al. · 2018 · Nature Communications
7. Roadmap on Machine learning in electronic structure
   Heather J. Kulik, Thomas Hammerschmidt, Jonathan Schmidt, Silvana Botti, Miguel A. L. Marques, Mario Boley, Matthias Scheffler, Milica Todorović, Patrick Rinke, Corey Oses et al. · 2022 · Electronic Structure
8. Stability of Electrodeposition at Solid-Solid Interfaces and Implications for Metal Anodes
   Zeeshan Ahmad, Venkatasubramanian Viswanathan · 2017 · Physical Review Letters
9. Predictions of new AB O 3 perovskite compounds by combining machine learning and density functional theory
   Prasanna V. Balachandran, Antoine Emery, James E. Gubernatis, Turab Lookman, Chris M. Wolverton, Alex Zunger · 2018 · Physical Review Materials
10. Rational approach to guest confinement inside MOF cavities for low-temperature catalysis
    Tiesheng Wang, Lijun Gao, Jingwei Hou et al. · 2019 · Nature Communications
11. Novel core-shell CuMo-oxynitride@N-doped graphene nanohybrid as multifunctional catalysts for rechargeable zinc-air batteries and water splitting
    Jayaraman Balamurugan, Thanh Tuan Nguyen, Nam Hoon Kim, Do Hwan Kim, Joong Hee Lee · 2021 · Nano Energy
12. Review of progress in calculation and simulation of high-temperature oxidation
    Dongxin Gao, Zhao Shen, Kai Chen, Xiaofeng Zhou, Liu Hong, Jingya Wang, Yangxin Li, Zhixiao Liu, Huiqiu Deng, William Yi Wang et al. · 2024 · Progress in Materials Science
13. First-principles evaluation of multi-valent cation insertion into orthorhombic V 2 O 5
    Gopalakrishnan Sai Gautam, Pieremanuele Canepa, Rahul Malik · 2015 · Chemical Communications
14. Machine Learning for Predicting the Band Gaps of ABX 3 Perovskites from Elemental Properties
    Vladislav Gladkikh, Dong Yeon Kim, Amir Hajibabaei, Atanu Jana, Chang Woo Myung, Kwang S. Kim · 2020 · The Journal of Physical Chemistry C
15. Nanoparticle Ex-solution for Supported Catalysts: Materials Design, Mechanism and Future Perspectives
    Jun Hyuk Kim, Jun Kyu Kim, Jiapeng Liu · 2020 · ACS Nano
16. Elastic constants of polycrystals with generally anisotropic crystals
    Christopher M. Kube, Maarten de Jong · 2016 · Journal of Applied Physics
17. Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
    Kamal Kumar Choudhary, Brian DeCost, Francesca Tavazza · 2018 · Physical Review Materials
18. Opportunities and challenges of text mining in materials research
    Kononova, Olga, He, Tanjin, Huo, Haoyan, Trewartha, Amalie, Olivetti, Elsa A., Ceder, Gerbrand · 2021 · iScience
19. Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning
    Seoin Back, Kevin Tran, Zachary Ward Ulissi · 2019 · ACS Catalysis
20. Machine Learning for Accelerated Discovery of Solar Photocatalysts
    Hassan Masood, Cui Ying Toe, Wey Yang Teoh, Vidhyasaharan Sethu, Rose Amal · 2019 · ACS Catalysis
21. Database of Two-Dimensional Hybrid Perovskite Materials: Open-Access Collection of Crystal Structures, Band Gaps, and Atomic Partial Charges Predicted by Machine Learning
    Ekaterina I. Marchenko, Sergey A. Fateev, Andrey A. Petrov, Vadim Korolev, Artem A. Mitrofanov, Andrey V. Petrov, Eugene Alekseevich Goodilin, Alexey B. Tarasov · 2020 · Chemistry of Materials
22. Big data and machine learning for materials science
    Jose Fernando Rodrigues Junior, Larisa Florea, Maria Cristina Ferreira de Oliveira, Dermot Diamond, Osvaldo N. Oliveira · 2021 · Discover Materials
23. Unveiling the Unique Phase Transformation Behavior and Sodiation Kinetics of 1D van der Waals Sb2S3 Anodes for Sodium Ion Batteries
    Shanshan Yao, Jiang Cui, Ziheng Lu · 2016 · Advanced Energy Materials
24. A data ecosystem to support machine learning in materials science
    Ben J. Blaiszik, Logan T. Ward, Marcus Schwarting · 2019 · MRS Communications
25. Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models
    Rachel Cook, Jonathan Lapeyre, Hongyan Ma, Aditya Kumar · 2019 · Journal of Materials in Civil Engineering
26. High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery
    Katherine McCullough, Travis Williams, Kathleen Mingle, Pooyan Jamshidi, Jochen A. Lauterbach · 2020 · Physical Chemistry Chemical Physics
27. Alloying effect on phase stability, elastic and thermodynamic properties of Nb-Ti-V-Zr high entropy alloy
    Mingqing Liao, Yong Liu, Lujin Min, Zhonghong Lai, Tianyi Han, Danni Yang, Jingchuan Zhu · 2018 · Intermetallics
28. Efficient Pourbaix diagrams of many-element compounds
    Anjli M. Patel, Jens K. Nørskov, Kristin Aslaug Persson, Joseph H. Montoya · 2019 · Physical Chemistry Chemical Physics
29. Predicting aqueous stability of solid with computed Pourbaix diagram using SCAN functional
    Zhenbin Wang, Xingyu Guo, Joseph H. Montoya, Jens K. Nørskov · 2020 · npj Computational Materials
30. A chemical map of NaSICON electrode materials for sodium-ion batteries
    Baltej Singh, Ziliang Wang, Sunkyu Park, Gopalakrishnan Sai Gautam, Jean‐Noël Chotard, Laurence Croguennec, Dany Carlier, Anthony Kevin Cheetham, Christian Masquelier, Pieremanuele Canepa · 2020 · Journal of Materials Chemistry A
31. Construction of Pd-Zn dual sites to enhance the performance for ethanol electro-oxidation reaction
    Yajun Qiu, Jian Zhang, Jing Jin, Jia-Qiang Sun, Hao-Lin Tang, Qingqing Chen, Ze-Dong Zhang, Wenming Sun, G. Meng, Qi Xu et al. · 2021 · Nature Communications
32. Data Generation for Machine Learning Interatomic Potentials and Beyond
    Maksim Kulichenko, Benjamin T. Nebgen, Nicholas Lubbers, Justin S. Smith, Kipton M. Barros, Alice E. A. Allen, Adela Habib, Emily Shinkle, Nikita S. Fedik, Ying Wai Li et al. · 2024 · Chemical Reviews
33. A framework for quantifying uncertainty in DFT energy corrections
    A. Wang, Ryan Kingsbury, Matthew J. McDermott, Matthew K. Horton, Anubhav Jain, Shyue Ping Ong, Shyam Dwaraknath, Kristin Aslaug Persson · 2021 · Scientific Reports
34. Machine learning in scanning transmission electron microscopy
    Sergei V. Kalinin, Colin Ophus, Paul M. Voyles, Rolf Erni, Demie M. Kepaptsoglou, Vincenzo Grillo, Andrew R. Lupini, Mark P. Oxley, Eric Schwenker, Maria K. Y. Chan et al. · 2022 · Nature Reviews Methods Primers
35. Artificial Intelligence for Materials Discovery, Development, and Optimization
    Benediktus Madika, Aditi Saha, Chaeyul Kang, Batzorig Buyantogtokh, Joshua Agar, Chris M. Wolverton, Peter W. Voorhees, P. B. Littlewood, Sergei V. Kalinin, Seungbum Hong · 2025 · ACS Nano
36. Investigation of the Redox Chemistry of Anthraquinone Derivatives Using Density Functional Theory
    Jonathan E. Bachman, Larry A. Curtiss, Rajeev S. Assary · 2014 · The Journal of Physical Chemistry A
37. Machine-learned multi-system surrogate models for materials prediction
    Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker, Alexander V. Shapeev, Tim Mueller, Conrad W. Rosenbrock, Gábor Cśanyi, David Wingate, Gus L. W. Hart · 2019 · npj Computational Materials
38. Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning
    Shiru Lin, Haoxiang Xu, Yekun Wang, Xiao Cheng Zeng, Zhongfang Chen · 2020 · Journal of Materials Chemistry A
39. The rise of high-entropy battery materials
    Bin Ouyang, Yan Zeng · 2024 · Nature Communications
40. Unsupervised machine learning in atomistic simulations, between predictions and understanding
    Ceriotti, Michele · 2019 · The Journal of Chemical Physics
41. Multi-objective Optimization for Materials Discovery via Adaptive Design
    Abhijith Gopakumar, Prasanna V. Balachandran, Dezhen Xue, James E. Gubernatis, Turab Lookman · 2018 · Scientific Reports
42. Chiral twisted van der Waals nanowires
    Peter W. Sutter, Shawn Wimer, Peter W. Sutter · 2019 · Nature
43. Linking Solid Electrolyte Degradation to Charge Carrier Transport in the Thiophosphate‐Based Composite Cathode toward Solid‐State Lithium‐Sulfur Batteries
    Saneyuki Ohno, Carolin Rosenbach, Georg F. Dewald, Jürgen Janek, Wolfgang G. Zeier · 2021 · Advanced Functional Materials
44. Metal-based cathodes for hydrogen production by alkaline water electrolysis: Review of materials, degradation mechanism, and durability tests
    Naeemeh Esfandiari, Mahmood Aliofkhazraei, Alejandro N. Colli, F.C. Walsh, Serhiy Cherevko, Ludwig A. Kibler, Mohamed M. Elnagar, Peter D. Lund, Dongke Zhang, Sasha Omanovic et al. · 2024 · Progress in Materials Science
45. Wallpaper fermions and the nonsymmorphic Dirac insulator
    Benjamin J. Wieder, Barry Bradlyn, Zhijun Wang, Jennifer Cano, Youngkuk Kim, Hyeong-Seok D. Kim, Andrew M. Rappe, C. L. Kane, Bogdan Andrei Bernevig · 2018 · Science
46. The role of artificial intelligence in the mass adoption of electric vehicles
    Moin Ahmed, Yun Hong Zheng, Anna Amine, Hamed Fathiannasab, Zhongwei Chen · 2021 · Joule
47. Thermodynamic Routes to Novel Metastable Nitrogen-Rich Nitrides
    Wenhao Q. Sun, Aaron M. Holder, Bernardo Orvañanos, Elisabetta Arca, Andriy Zakutayev, Stephan Lany, Gerbrand Ceder · 2017 · Chemistry of Materials
48. “Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors
    Janis Timoshenko, Anatoly I. Frenkel · 2019 · ACS Catalysis
49. Digital Reticular Chemistry
    Hao Lyu, Zhe Ji, Stefan Wuttke, Omar M. Yaghi · 2020 · Chem
50. Interfacial-engineering-enabled practical low-temperature sodium metal battery
    Tao Deng, Xiao Yuan Ji, Lianfeng Zou et al. · 2021 · Nature Nanotechnology
51. Future high-energy density anode materials from an automotive application perspective
    Dave André, Holger Hain, Peter Lamp · 2017 · Journal of Materials Chemistry A
52. Applications and limitations of two step metal oxide thermochemical redox cycles; a review
    Brendan Bulfin, Josua Vieten, Christos Agrafiotis · 2017 · Journal of Materials Chemistry A
53. Materials design of perovskite solid solutions for thermochemical applications
    Josua Vieten, Brendan Bulfin, Patrick Huck · 2019 · Energy & Environmental Science
54. Reactivity-Guided Interface Design in Na Metal Solid-State Batteries
    Yaosen Tian, Yingzhi Sun, Daniel C. Hannah · 2019 · Joule
55. A Disquisition on the Active Sites of Heterogeneous Catalysts for Electrochemical Reduction of CO2 to Value‐Added Chemicals and Fuel
    Rahman Daiyan, Wibawa Hendra Saputera, Hassan Masood, Josh Leverett, Xunyu Lu, Rose Amal · 2020 · Advanced Energy Materials
56. Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships
    Steven B. Torrisi, Matthew R. Carbone, Brian A. Rohr, Joseph H. Montoya, Yang Ha, Junko Yano, Santosh K. Suram, Linda Hung · 2020 · npj Computational Materials
57. Extensive Benchmarking of DFT+U Calculations for Predicting Band Gaps
    Nicole E. Kirchner-Hall, Wayne Zhao, Yihuang Xiong, Iurii Timrov, Ismaïla Dabo · 2021 · Applied Sciences
58. Materials and structure engineering by magnetron sputtering for advanced lithium batteries
    Yitian Ma, Li Li, Ji Qian · 2021 · Energy storage materials
59. Diffusion mechanisms of fast lithium-ion conductors
    KyuJung Jun, Yu Chen, Grace Wei · 2024 · Nature Reviews Materials
60. Understanding Doping of Quantum Materials
    Alex Zunger, Oleksandr I. Malyi · 2021 · Chemical Reviews
61. Understanding, discovery, and synthesis of 2D materials enabled by machine learning
    Byunghoon Ryu, Luqing Wang, Haihui Pu, Maria K. Y. Chan, Junhong Chen · 2022 · Chemical Society Reviews
62. Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
    Liu, Yiming, Tan, Xinyu, Liang, Jie, Han, Hongwei, Xiang, Peng, Yan, Wensheng · 2023 · Advanced Functional Materials
63. Materials 4.0: Materials big data enabled materials discovery
    Rajan Jose, Seeram Ramakrishna · 2018 · Applied Materials Today
64. Active learning for accelerated design of layered materials
    Lindsay Bassman Oftelie, Pankaj Rajak, Rajiv K. Kalia, Aiichiro Nakano, Fei Sha, Jifeng Sun, David Joseph Singh, Muratahan Aykol, Patrick Huck, Kristin Aslaug Persson et al. · 2018 · npj Computational Materials
65. Nonaqueous rechargeable aluminum batteries
    Kok Long Ng, Brohath Amrithraj, Gisele Azimi · 2022 · Joule
66. Review of Zinc Oxide Piezoelectric Nanogenerators: Piezoelectric Properties, Composite Structures and Power Output
    Neelesh Bhadwal, Ridha Ben Mrad, Kamran Behdinan · 2023 · Sensors
67. Structural evolution and catalytic mechanisms of perovskite oxides in electrocatalysis
    J. W. Zhao, Yunxiang Li, Deyan Luan, Xiong Wen Lou · 2024 · Science Advances
68. Accelerated materials property predictions and design using motif-based fingerprints
    Tran Doan Huan, Arun Mannodi‐Kanakkithodi, Rampi Ramprasad · 2015 · Physical Review B
69. Crystal Structural Framework of Lithium Super‐Ionic Conductors
    Xingfeng He, Qiang Bai, Yunsheng Liu, Adelaide M. Nolan, Chen Ling, Yifei Mo · 2019 · Advanced Energy Materials
70. Machine-learning and high-throughput studies for high-entropy materials
    E‐Wen Huang, Wen‐Jay Lee, Sudhanshu Shekhar Singh, Poresh Kumar, Chih‐Yu Lee, Tu‐Ngoc Lam, Hsu-Hsuan Chin, Bi‐Hsuan Lin, Peter Kaehuei Liaw · 2022 · Materials Science and Engineering R Reports
71. Exploring dopant effects in stannic oxide nanoparticles for CO2 electro-reduction to formate
    Young‐Jin Ko, Jun-Yong Kim, Woong Hee Lee, Min Gyu Kim, Tae‐Yeon Seong, Jongkil Park, YeonJoo Jeong, Byoung Koun Min, Wook‐Seong Lee, Dong Ki Lee et al. · 2022 · Nature Communications
72. NOMAD: A distributed web-based platform for managing materials science research data
    Markus Scheidgen, Lauri Himanen, Alvin Noe Ladines et al. · 2023 · The Journal of Open Source Software
73. Simultaneous Etching and Doping of TiO2 Nanowire Arrays for Enhanced Photoelectrochemical Performance
    Yongcheng Wang, Yueyu Zhang, Jing Tang · 2013 · ACS Nano
74. Discovering and understanding materials through computation
    Steven G. Louie, Yang‐Hao Chan, Felipe H. da Jornada, Zhenglu Li, Diana Yuan Qiu · 2021 · Nature Materials
75. Achieving zT > 1 in Inexpensive Zintl Phase Ca 9 Zn 4+ x Sb 9 by Phase Boundary Mapping
    Saneyuki Ohno, Umut Aydemir, Maximilian Amsler · 2017 · Advanced Functional Materials
76. Phase Diagrams and Stability of Lead-Free Halide Double Perovskites Cs2BB′X6: B = Sb and Bi, B′ = Cu, Ag, and Au, and X = Cl, Br, and I
    Marina Rucsandra Filip, Xinlei Liu, Anna Miglio, Geoffroy Hautier, Feliciano Giustino · 2017 · The Journal of Physical Chemistry C
77. Toward Design of Novel Materials for Organic Electronics
    Pascal Friederich, Artem Fediai, Simon Kaiser, Manuel Konrad, Nicole Jung, Wolfgang Wenzel · 2019 · Advanced Materials
78. Thermal conductance across β-Ga2O3-diamond van der Waals heterogeneous interfaces
    Zhe Cheng, Luke Yates, Jingjing Shi, Marko Jak Tadjer, Karl D. Hobart, Samuel Lindsay Graham · 2019 · APL Materials
79. Metal Borohydrides as Electrolytes for Solid-State Li, Na, Mg, and Ca Batteries: A First-Principles Study
    Ziheng Lu, Francesco Ciucci A · 2017 · Chemistry of Materials
80. Machine learning models for the lattice thermal conductivity prediction of inorganic materials
    Lihua Chen, Tran Doan Huan, Rohit Batra, Chiho Kim, Rampi Ramprasad · 2019 · Computational Materials Science
81. Towards realizing nano-enabled precision delivery in plants
    Gregory Victor Lowry, Juan Pablo Giraldo, Nicole F. Steinmetz, Astrid Avellan, Gözde S. Demirer, Kurt D. Ristroph, Gerald Wang, Christine Ogilvie Hendren, Christopher A. Alabi, Adam A. Caparco et al. · 2024 · Nature Nanotechnology
82. Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
    Nandy, Aditya, Duan, Chenru, Janet, Jon Paul, Gugler, Stefan, Kulik, Heather J. · 2018 · Industrial & Engineering Chemistry Research
83. Recent developments in complex metal oxide photoelectrodes
    Fatwa F. Abdi, Sean P. Berglund · 2017 · Journal of Physics D Applied Physics
84. Accurate force field for molybdenum by machine learning large materials data
    Chi Chen, Zhi Deng, Richard Tran, Hanmei Tang, Iek‐Heng Chu, Shyue Ping Ong · 2017 · Physical Review Materials
85. High voltage electrolytes for lithium-ion batteries with micro-sized silicon anodes
    Ai-Min Li, Ze-Yi Wang, T. Pollard, Wei-Ran Zhang, Sha Tan, Tianyu Li, Chamithri Jayawardana, S. Liou, Jian-Cun Rao, Brett L. Lucht et al. · 2024 · Nature Communications
86. Machine-learned and codified synthesis parameters of oxide materials
    Edward Kim, Kejun Huang, Alex Tomala, Sara Matthews, Emma Strubell, Adam M. Saunders, Andrew McCallum, Elsa Olivetti · 2017 · Scientific Data
87. Evaluation of sulfur spinel compounds for multivalent battery cathode applications
    Miao Liu, Anubhav Jain, Ziqin Rong · 2016 · Energy & Environmental Science
88. Heat capacity of Mg3Sb2, Mg3Bi2, and their alloys at high temperature
    Matthias T. Agne, Kazuki Imasato, Shashwat Anand · 2018 · Materials Today Physics
89. Tin Intercalated Ultrathin MoO 3 Nanoribbons for Advanced Lithium–Sulfur Batteries
    Weiwei Yang, Jiewen Xiao, Yang Ma et al. · 2018 · Advanced Energy Materials
90. Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing
    Nathan C. Frey, Deji Akinwande, Deep Jariwala, Vivek B. Shenoy · 2020 · ACS Nano
91. ElTools: A tool for analyzing anisotropic elastic properties of the 2D and 3D materials
    Shahram Yalameha, Zahra Nourbakhsh, Daryoosh Vashaee · 2021 · Computer Physics Communications
92. Truly chiral phonons in α-HgS
    Kyosuke Ishito, Huiling Mao, Yusuke Kousaka, Yoshihiko Togawa, Satoshi Iwasaki, Tiantian Zhang, Shuichi Murakami, Jun‐ichiro Kishine, Takuya Satoh · 2022 · Nature Physics
93. Crystal structure generation with autoregressive large language modeling
    Luis M. Antunes, Keith T. Butler, Ricardo Grau‐Crespo · 2024 · Nature Communications
94. Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries
    M. Attarian Shandiz, Raynald Gauvin · 2016 · Computational Materials Science
95. Evolving embodied intelligence from materials to machines
    David Howard, Agoston Endre Eiben, Danielle Frances Kennedy, Jean-Baptiste Mouret, Philip Valencia, David Alan Winkler · 2019 · Nature Machine Intelligence
96. Air‐Stable 2D Cr 5 Te 8 Nanosheets with Thickness‐Tunable Ferromagnetism
    Chao Chen, Xiaodie Chen, Changwei Wu et al. · 2021 · Advanced Materials
97. Non‐Electrode Components for Rechargeable Aqueous Zinc Batteries: Electrolytes, Solid‐Electrolyte‐Interphase, Current Collectors, Binders, and Separators
    Qiao Ni, Byung‐Hoon Kim, Chuan Kun Wu, Kisuk Kang · 2021 · Advanced Materials
98. A database of battery materials auto-generated using ChemDataExtractor
    Shu Huang, Jacqueline M. Cole · 2020 · Scientific Data
99. Atomically Thin 2D Transition Metal Oxides: Structural Reconstruction, Interaction with Substrates, and Potential Applications
    Tong Yang, Ting Ting Song, Martin Callsen, Jun Zhou, Jian Chai, Yuan Ping Feng, Shijie Wang, Ming Yang · 2018 · Advanced Materials Interfaces
100. Crystalline nickel, cobalt, and manganese antimonates as electrocatalysts for the chlorine evolution reaction
     Ivan A. Moreno‐Hernandez, Bruce S. Brunschwig, Nathan S. Lewis · 2019 · Energy & Environmental Science