Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO 2 Reduction
Ulissi, Zachary W., Tang, Michael T., Xiao, Jianping, Liu, Xinyan, Torelli, Daniel A., Karamad, Mohammadreza, Cummins, Kyle, Hahn, Christopher +4 more·2017·ACS Catalysis
Cited by (386)
Items 101–200 of 386
- Atomic‐Level Design of Active Site on Two‐Dimensional MoS 2 toward Efficient Hydrogen Evolution: Experiment, Theory, and Artificial Intelligence ModellingChun Sun, Longlu Wang, Weiwei Zhao, Lingbin Xie, Jin Wang, Jianmin Li, Bingxiang Li, Shujuan Liu, Zechao C. Zhuang, Qiang Zhao · 2022 · Advanced Functional Materials
- Emerging Atomistic Modeling Methods for Heterogeneous ElectrocatalysisZachary H. Levell, Jiabo Le, Saerom Yu, Ruoyu Wang, Sudheesh Kumar Ethirajan, Rachita Rana, Ambarish R. Kulkarni, Joaquin Resasco, Deyu Lu, Jun Jie Cheng et al. · 2024 · Chemical Reviews
- A brief review of the computational modeling of CO2 electroreduction on Cu electrodesAlejandra Rendón-Calle, Santiago Builes, Federico Calle‐Vallejo · 2018 · Current Opinion in Electrochemistry
- Recent advances in nanostructured intermetallic electrocatalysts for renewable energy conversion reactionsHo Young Kim, Sang Hoon Joo · 2020 · Journal of Materials Chemistry A
- Shift of the Optimum Binding Energy at Higher Rates of CatalysisHideshi Ooka, Ryuhei Nakamura · 2019 · The Journal of Physical Chemistry Letters
- Dynamics of Heterogeneous Catalytic Processes at Operando ConditionsXiangcheng Shi, Xiaoyun Lin, Ran Luo, Shican Wu, Lulu Li, Zhi‐Jian Zhao, Jinlong Gong · 2021 · JACS Au
- Electronic structure factors and the importance of adsorbate effects in chemisorption on surface alloysShikha Saini, Joakim Halldin Stenlid, Frank Abild‐Pedersen · 2022 · npj Computational Materials
- Molecular representations for machine learning applications in chemistryShampa Raghunathan, U. Deva Priyakumar · 2021 · International Journal of Quantum Chemistry
- Machine learning accelerated calculation and design of electrocatalysts for CO2 reductionZhehao Sun, Hang Yin, Kaili Liu, Shuwen Cheng, Gang Kevin Li, Sibudjing Kawi, Haitao Zhao, Guohua Jia, Zongyou Yin · 2022 · SmartMat
- Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published DataKeisuke Suzuki, Takashi Toyao, Zen Maeno, Satoru Takakusagi, Ken‐ichi Shimizu, Ichigaku Takigawa · 2019 · ChemCatChem
- Engineering Electrochemical Surface for Efficient Carbon Dioxide UpgradeGuobin Wen, Bohua Ren, Yun Hong Zheng, Matthew Li, Catherine J. Silva, Shuqin Song, Zhen Zhang, Haozhen Dou, Lei Zhao, Dan Luo et al. · 2021 · Advanced Energy Materials
- Encoding the atomic structure for machine learning in materials scienceShunning Li, Yuanji Liu, Dong Chen, Yi Jiang, Zhiwei Nie, Feng Pan · 2021 · Wiley Interdisciplinary Reviews Computational Molecular Science
- A Career in Catalysis: Jens Kehlet NørskovA. Medford, P. G. Moses, K. Jacobsen, A. Peterson · 2022 · ACS Catalysis
- Advanced electrocatalytic technologies for conversion of carbon dioxide into methanol by electrochemical reduction: Recent progress and future perspectivesIjaz Hussain, Hassan Saeed Alasiri, Wasim Ullah Khan, Khalid Rashed Alhooshani · 2023 · Coordination Chemistry Reviews
- High-Throughput Identification of Exfoliable Two-Dimensional Materials with Active Basal Planes for Hydrogen EvolutionTong Yang, Jun Zhou, T. Song, Lei Shen, Yuan P. Feng, Ming Yang · 2020 · ACS Energy Letters
- Perspectives on Advancing Sustainable CO 2 Conversion Processes: Trinomial Technology, Environment, and EconomyLourdes F. Vega, Daniel Bahamón, Ismail I.I. Alkhatib · 2024 · ACS Sustainable Chemistry & Engineering
- A Bayesian framework for adsorption energy prediction on bimetallic alloy catalystsOsman Mamun, Kirsten T. Winther, Jacob R. Boes, Thomas Bligaard · 2020 · npj Computational Materials
- Electrocatalytic Oxygen Reduction to Produce Hydrogen Peroxide: Rational Design from Single-Atom Catalysts to DevicesYueyu Tong, Liqun Wang, Feng Hou, Shi Xue Dou, Ji Liang · 2022 · Electrochemical Energy Reviews
- Investigating the Nature of the Active Sites for the CO 2 Reduction Reaction on Carbon-Based ElectrocatalystsTristan Asset, Samuel T. Garcia, Sergio Herrera, Nalin I. Andersen, Yechuan Chen, Eric J. Peterson, Ivana Matanović, Kateryna Artyushkova, Jack K. Lee, Shelley D. Minteer et al. · 2019 · ACS Catalysis
- Machine learning activation energies of chemical reactionsToby Lewis‐Atwell, Piers A. Townsend, Matthew N. Grayson · 2021 · Wiley Interdisciplinary Reviews Computational Molecular Science
- Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A ReviewKaiwei Wan, Jianxin He, Xinghua Shi · 2023 · Advanced Materials
- Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerizationMaley, Steven M., Kwon, Doo-Hyun, Rollins, Nick, Stanley, Johnathan C., Sydora, Orson L., Bischof, Steven M., Ess, Daniel H. · 2020 · Chemical Science
- Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature DataKEISUKE G. TAKAHASHI, Itsuki Miyazato, Shun Nishimura, Junya Ohyama · 2018 · ChemCatChem
- Recent developments and current trends on catalytic dry reforming of Methane: Hydrogen Production, thermodynamics analysis, techno feasibility, and machine learningMohammed Mosaad Awad, Esraa Kotob, Omer Ahmed Taialla, Ijaz Hussain, Saheed Adewale Ganiyu, Khalid Rashed Alhooshani · 2024 · Energy Conversion and Management
- Machine Learning for Accurate Force Calculations in Molecular Dynamics SimulationsPunyaslok Pattnaik, Shampa Raghunathan, Tarun Kalluri, Prabhakar Bhimalapuram, C. V. Jawahar, U. Deva Priyakumar · 2020 · The Journal of Physical Chemistry A
- Progress in Modeling of Biomass Fast Pyrolysis: A ReviewPavlo Kostetskyy, Linda J. Broadbelt · 2020 · Energy & Fuels
- Searching for an Optimal Multi‐Metallic Alloy Catalyst by Active Learning Combined with ExperimentsMinki Kim, Min Young Ha, Woo‐Bin Jung, Jeesoo Yoon, Euichul Shin, Il‐Doo Kim, Won Bo Lee, YongJoo Kim, Hee‐Tae Jung · 2022 · Advanced Materials
- AI in drug development: a multidisciplinary perspectiveVíctor Gallego, Roi Naveiro, Carlos Roca, David Rı́os Insua, Nuria Eugenia Campillo · 2021 · Molecular Diversity
- Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysisJiayan Xu, Xiaoming Cao, P. Hu · 2021 · Physical Chemistry Chemical Physics
- In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modellingShivam Saxena, Tuhin Suvra Khan, Fatima Jalid, Manojkumar Ramteke, Mohammad Ali Haider · 2019 · Journal of Materials Chemistry A
- Simulation and design of energy materials accelerated by machine learningHongshuai Wang, Yujin Ji, Youyong Li · 2019 · Wiley Interdisciplinary Reviews Computational Molecular Science
- Mechanism in pH effects of electrochemical reactions: a mini-reviewSibei Liu, Zhuowen Wang, Shan Qiu, Fengxia Deng · 2024 · Carbon letters
- Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule ActivationGeun Ho Gu, Changhyeok Choi, Yeunhee Lee, Andres Bethavan Situmorang, Juhwan Noh, Yong‐Hyun Kim, Yousung Jung · 2020 · Advanced Materials
- Discovery of Acid-Stable Oxygen Evolution Catalysts: High-Throughput Computational Screening of Equimolar Bimetallic OxidesSeoin Back, Kevin Tran, Zachary W. Ulissi · 2020 · ACS Applied Materials & Interfaces
- From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal ElectrocatalystsXinyu Fan, Letian Chen, Dulin Huang, Yun Tian, Xu Zhang, Menggai Jiao, Zhen Ping Zhou · 2024 · Advanced Functional Materials
- Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural NetworksAini Palizhati, Wen Zhong, Kevin Tran, Seoin Back, Zachary W. Ulissi · 2019 · Journal of Chemical Information and Modeling
- Theoretical Investigations of the Electrochemical Reduction of CO on Single Metal Atoms Embedded in GrapheneCharlotte Kirk, Leanne D. Chen, Samira Siahrostami, Mohammadreza Karamad, Michal Bajdich, Johannes Voss, Jens K. Nørskov, Karen Chan · 2017 · ACS Central Science
- Machine-learning accelerated geometry optimization in molecular simulationYilin Yang, Omar A. Jiménez-Negrón, John R. Kitchin · 2021 · The Journal of Chemical Physics
- Machine learning for the modeling of interfaces in energy storage and conversion materialsNongnuch Artrith · 2019 · Journal of Physics Energy
- Mesoporous trimetallic PtPdAu alloy films toward enhanced electrocatalytic activity in methanol oxidation: unexpected chemical compositions discovered by Bayesian optimizationAsep Sugih Nugraha, G. Lambard, Jongbeom Na, Md. Shahriar A. Hossain, Toru Asahi, Watcharop Chaikittisilp, Yusuke Yamauchi · 2020 · Journal of Materials Chemistry A
- Rapid estimation of activation energy in heterogeneous catalytic reactions via machine learningKEISUKE G. TAKAHASHI, Itsuki Miyazato · 2018 · Journal of Computational Chemistry
- Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution ReactionJiawei Zhang, P. Hu, Haifeng Wang · 2020 · The Journal of Physical Chemistry C
- Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous CatalysisMiguel Steiner, Markus Reiher · 2022 · Topics in Catalysis
- The role of machine learning in carbon neutrality: Catalyst property prediction, design, and synthesis for carbon dioxide reductionZhuo Wang, Zhehao Sun, Hang Yin, Honghe Wei, Zicong Peng, Yoong Xin Pang, Guohua Jia, Haitao Zhao, Cheng Heng Pang, Zongyou Yin · 2023 · eScience
- Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy ApplicationsHao Wang, Yuquan Li, Xiaoyang Xuan, Kai Wang, Ye‐Feng Yao, Likun Pan · 2025 · Environmental Science & Technology
- Electrochemical reduction of carbon dioxide into valuable chemicals: a reviewSidra Anis Farooqi, Ahmad Salam Farooqi, Shamaila Sajjad, Chenglin Yan, Ayodele Bamidele Victor · 2023 · Environmental Chemistry Letters
- Efficiency of Generalized Regular k -point gridsWiley S. Morgan, Jeremy J. Jorgensen, Bret C. Hess, Gus L. W. Hart · 2018 · Computational Materials Science
- First-Principles Based Microkinetic Modeling of CO2 Reduction at the Ni/SDC Cathode of a Solid Oxide Electrolysis CellBohua Ren, Jingde Li, Guobin Wen, Luis Ricardez‐Sandoval, Eric Croiset · 2018 · The Journal of Physical Chemistry C
- Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistryYongqiang Kang, Lejing Li, Baohua Li · 2020 · Journal of Energy Chemistry
- Mechanistic understanding and design of non-noble metal-based single-atom catalysts supported on two-dimensional materials for CO 2 electroreductionYa Huang, Faisal Rehman, Mohsen Tamtaji, Xuning Li, Yanqiang Huang, Tao Zhang, Zhengtang Luo · 2021 · Journal of Materials Chemistry A
- Machine learning for CO 2 capture and conversion: A reviewSung Eun Jerng, Yang Jeong Park, Ju Li · 2024 · Energy and AI
- Chemical Diversity of Metal Sulfide Minerals and Its Implications for the Origin of LifeYamei Li, Norio Kitadai, Ryuhei Nakamura · 2018 · Life
- Computational discovery of energy materials in the era of big data and machine learning: A critical reviewZiheng Lu · 2021 · Materials Reports Energy
- Machine learning applications in catalytic hydrogenation of carbon dioxide to methanol: A comprehensive reviewErmias Girma Aklilu, Tijani Bounahmidi · 2024 · International Journal of Hydrogen Energy
- Machine Learning Prediction of H Adsorption Energies on Ag AlloysRobert A. Hoyt, M. M. Montemore, Ioanna Fampiou, Wei Chen, Georgios A. Tritsaris, Efthimios Kaxiras · 2019 · Journal of Chemical Information and Modeling
- Deep learning metal complex properties with natural quantum graphsKneiding, Hannes, Lukin, Ruslan, Lang, Lucas, Reine, Simen, Pedersen, Thomas Bondo, De Bin, Riccardo, Balcells, David · 2023 · Digital Discovery
- Synthesis bifunctional catalysts with amorphous NiFe-LDH/crystalline CoMo bimetallic phosphide heterojunction by electrodeposition for efficient water splittingGuo, Desheng, Xia, Houbing, Guo, Xu, Wen, Lingling, Wang, Tiantian, Li, Xin, Sun, Zhiqiang · 2024 · International Journal of Hydrogen Energy
- Reducing the onset potential of CO2 electroreduction on CuRu bimetallic particlesJoshua T. Billy, Anne C. Co · 2018 · Applied Catalysis B: Environmental
- Electric-Field-Assisted Modulation of Surface ThermochemistryManish Shetty, M. Alexander Ardagh, Yutong Pang, Omar Ali Abdelrahman, Paul Jakob Dauenhauer · 2020 · ACS Catalysis
- MAISE: Construction of neural network interatomic models and evolutionary structure optimizationSamad Hajinazar, Aidan Thorn, Ernesto D. Sandoval, Saba Kharabadze, Aleksey N. Kolmogorov · 2020 · Computer Physics Communications
- A perspective on the electrocatalytic conversion of carbon dioxide to methanol with metallomacrocyclic catalystsXinyan Liu, Bo‐Quan Li, Bing Ni, Lei Wang, Hong‐Jie Peng · 2021 · Journal of Energy Chemistry
- Building machine learning force fields for nanoclustersClaudio Zeni, Kevin Rossi, Aldo Glielmo, Ádám Fekete, Nicola Gaston, Francesca Baletto, Alessandro De Vita · 2018 · The Journal of Chemical Physics
- Nanoparticle Size Effects on Phase Stability for Molybdenum and Tungsten CarbidesAnukriti Shrestha, Xutao Gao, Jason C. Hicks, Christopher Paolucci · 2021 · Chemistry of Materials
- Metal–Nitrogen–Carbon Electrocatalysts for CO 2 Reduction towards Syngas GenerationLaurent Delafontaine, Tristan Asset, Plamen B. Atanassov · 2020 · ChemSusChem
- Machine Learning Assisted Understanding and Discovery of CO2 Reduction Reaction ElectrocatalystErhai Hu, Chuntai Liu, Wei Zhang, Qingyu Yan · 2023 · The Journal of Physical Chemistry C
- Moving Frontiers in Transition Metal Catalysis: Synthesis, Characterization and ModelingDmitry I. Sharapa, Dmitry E. Doronkin, Felix Studt, Jan‐Dierk Grunwaldt, Silke S. Behrens · 2019 · Advanced Materials
- Single-atom iron catalysts for biomedical applicationsJing Tang, Yifan Wu, Xun Li, Lihong Bu, Baisong Chang · 2022 · Progress in Materials Science
- Revolutionizing electrochemical CO 2 reduction to deeply reduced products on non-Cu-based electrocatalystsHaoming Yu, Hsiwen Wu, Yuen Leong Chow, Jun Wang, Jie Zhang · 2024 · Energy & Environmental Science
- Interpretable Machine Learning of Chemical Bonding at Solid SurfacesNoushin Omidvar, Hemanth Somarajan Pillai, Shih‐Han Wang, Tianyou Mou, Siwen Wang, Andy Athawale, Luke E. K. Achenie, Hongliang Xin · 2021 · The Journal of Physical Chemistry Letters
- First-principles database driven computational neural network approach to the discovery of active ternary nanocatalysts for oxygen reduction reactionJoonhee Kang, Seung Hyo Noh, Jeemin Hwang, Hoje Chun, Hansung Kim, Byungchan Han · 2018 · Physical Chemistry Chemical Physics
- Tailoring Copper Foam with Silver Dendrite Catalysts for Highly Selective Carbon Dioxide Conversion into Carbon MonoxideFélix Urbain, Pengyi Tang, Nina M. Carretero, Teresa Andreu, Jordi Arbiol, Joan Ramon Morante · 2018 · ACS Applied Materials & Interfaces
- Machine learning accelerates quantum mechanics predictions of molecular crystalsYanqiang Han, Imran Ali, Zhilong Wang, Junfei Cai, Sicheng Wu, Jiequn Tang, Lin Zhang, Jiahao Ren, Rui Xiao, Qianqian Lu et al. · 2021 · Physics Reports
- Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advancesEmmanuel Anuoluwa Bamidele, Ahmed Olanrewaju Ijaola, Michael Oluwatosin Bodunrin, Oluwaniyi Ajiteru, Afure Martha Oyibo, Mamookho Elizabeth Makhatha, Eylem Asmatulu · 2022 · Advanced Engineering Informatics
- Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopyNicholas Marcella, Yang Liu, Janis Timoshenko, Erjia Guan, Mathilde Luneau, Tanya Shirman, Anna M. Płonka, Jessi E. S. van der Hoeven, Joanna Aizenberg, C. M. Friend et al. · 2020 · Physical Chemistry Chemical Physics
- Molecular Dynamics and Machine Learning in CatalystsWenxiang Liu, Yang Zhu, Yongqiang Wu, Cen Chen, Yang Hong, Yanan Yue, Jingchao Zhang, Bo Hou · 2021 · Catalysts
- Machine Learning Applications for Chemical ReactionsSanggil Park, Herim Han, Hyungjun Kim, Sunghwan Choi · 2022 · Chemistry - An Asian Journal
- Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspectiveAnnette Trunschke · 2022 · Catalysis Science & Technology
- Data-driven design of electrocatalysts: principle, progress, and perspectiveShan Zhu, Kezhu Jiang, Biao Chen, Shijian Zheng · 2023 · Journal of Materials Chemistry A
- Machine Learning Across Metal and Carbon Support for the Screening of Efficient Atomic Catalysts Toward CO 2 ReductionMingzi Sun, Bolong Huang · 2023 · Advanced Energy Materials
- The role of material defects in the photocatalytic CO2 reduction: Interfacial properties, thermodynamics, kinetics and mechanismDileep Maarisetty, Reeba Mary Mammen, Da‐Ren Hang, Priyabrat Mohapatra, Saroj Sundar Baral · 2022 · Journal of CO2 Utilization
- A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteriesChao Han, Yuchen Gao, Xiang Chen, Xinyan Liu, Nan Yao, Legeng Yu, Long Kong, Qiang Zhang · 2024 · InfoMat
- Research Status, Challenges, and Future Prospects of Carbon Dioxide Reduction TechnologyHongtao Dang, Bin Guan, Junyan Chen, Zeren Ma, Yujun Chen, Jinhe Zhang, Zelong Guo, Lei Chen, Jingqiu Hu, Chao Yi et al. · 2024 · Energy & Fuels
- Capture and electroreduction of CO 2 using highly efficient bimetallic Pd–Ag aerogels paired with carbon nanotubesMaryam Abdinejad, Celia Ferrag, M. Nur Hossain, Meissam Noroozifar, Kağan Kerman, Heinz‐Bernhard Kraatz · 2021 · Journal of Materials Chemistry A
- Tunable Product Selectivity in Electrochemical CO 2 Reduction on Well-Mixed Ni–Cu AlloysHakhyeon Song, Ying Chuan Tan, Beomil Kim, Stefan Ringe, Jihun Oh · 2021 · ACS Applied Materials & Interfaces
- Tuning Cu/Cu2O Interfaces for the Reduction of Carbon Dioxide to Methanol in Aqueous SolutionsXiaoxia Chang, Tuo Wang, Zhi‐Jian Zhao, Piaoping Yang, Jeffrey Greeley, Rentao Mu, Gong Zhang, Zhongmiao Gong, Zhibin Luo, Jun Chen et al. · 2018 · Angewandte Chemie
- Rational Design of Stimuli‐Responsive Inorganic 2D Materials via Molecular Engineering: Toward Molecule‐Programmable NanoelectronicsJosé Muñoz · 2023 · Advanced Materials
- On machine learning force fields for metallic nanoparticlesClaudio Zeni, Kevin Rossi, Aldo Glielmo, Francesca Baletto · 2019 · Advances in Physics X
- Accelerated identification of high-performance catalysts for low-temperature NH 3 -SCR by machine learningYi Dong, Yu Zhang, Mingchu Ran, Xiao Zhang, Shaojun Liu, Yang Yang, Wenshuo Hu, Chenghang Zheng, Xiang Gao · 2021 · Journal of Materials Chemistry A
- Machine Learning-Assisted Screening of Stepped Alloy Surfaces for C1 CatalysisXinyan Liu, Cheng Cai, Wanghui Zhao, Hong‐Jie Peng, Tao Wang · 2022 · ACS Catalysis
- Recent advances in metal-based electrocatalysts: from fundamentals and structural regulations to applications in anion-exchange membrane fuel cellsAli Han, Gang Liu · 2023 · Materials Chemistry Frontiers
- Multiscale Modeling of CO 2 Electrochemical Reduction on Copper Electrocatalysts: A Review of Advancements, Challenges, and Future DirectionsReza Gholizadeh, Matic Pavlin, Matej Huš, Blaž Likozar · 2024 · ChemSusChem
- Structure–Property-Performance Relationship of Ultrathin Pd–Au Alloy Catalyst Layers for Low-Temperature Ethanol Oxidation in Alkaline MediaJoshua Patrick McClure, Jonathan A. Boltersdorf, David R. Baker, Thomas G. Farinha, Nicholas Dzuricky, Cesar E. P. Villegas, Alexandre Reily Rocha, Marina S. Leite · 2019 · ACS Applied Materials & Interfaces
- Data driven reaction mechanism estimation via transient kinetics and machine learningM. Ross Kunz, Adam Yonge, Zongtang Fang, Rakesh Batchu, Andrew James Medford, Denis Constales, Gregory S. Yablonsky, Rebecca Fushimi · 2021 · Chemical Engineering Journal
- Strategic tailored design of electrocatalysts for environmental remediation based on density functional theory (DFT) and microkinetic modelingGabriel Antonio Cerrón-Calle, Thomas Patrick Senftle, Sergi Garcia‐Segura · 2022 · Current Opinion in Electrochemistry
- Electrochemical reactions catalyzed by carbon dots from computational investigations: functional groups, dopants, and defectsMin Yang, Bin Qin, Chaowei Si, Xiaoying Sun, Bo Li · 2023 · Journal of Materials Chemistry A
- Foundational techniques for catalyst design in the upgrading of biomass-derived multifunctional moleculesBrian M. Murphy, Bingjun Xu · 2018 · Progress in Energy and Combustion Science
- Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and UseClara Patricia Marshall, Julia Schumann, Annette Trunschke · 2023 · Angewandte Chemie International Edition
- Toward Reliable and Transferable Machine Learning Potentials: Uniform Training by Overcoming Sampling BiasWonseok Jeong, Kyuhyun Lee, Dongsun Yoo, Dongheon Lee, Seungwu Han · 2018 · The Journal of Physical Chemistry C
- Design of an Accurate Machine Learning Algorithm to Predict the Binding Energies of Several Adsorbates on Multiple Sites of Metal SurfacesC. S. Praveen, Aleix Comas‐Vives · 2020 · ChemCatChem
- Catalyst Design for Selective Hydrogenation of Benzene to Cyclohexene through Density Functional Theory and Microkinetic ModelingHaoran He, Randall J. Meyer, Robert M. Rioux, Michael John Janik · 2021 · ACS Catalysis