Papers citing Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks
Metal-solvent interaction contribution on voltage for metal ion battery: An interpretable machine learning approach
Electrochimica Acta · 2023
Souvik Manna, Surya Sekhar Manna, Sandeep Das, Biswarup Pathak
Read original paper ↗ ·Database System Based on 3Dmax Big Data Mining Technology
ICST Transactions on Scalable Information Systems · 2023
Xiaoyu Chen, Junkai Zhang, Pengshan Ren, Xian Hua, Yanfeng Ni
Read original paper ↗ ·A New Era of Modeling MOF‐Based Membranes: Cooperation of Theory and Data Science
Macromolecular Materials and Engineering · 2023
Hakan Demir, Seda Keskın
Read original paper ↗ ·On the shoulders of high-throughput computational screening and machine learning: Design and discovery of MOFs for H2 storage and purification
Materials Today Energy · 2023
Çiğdem Altıntaş, Seda Keskın
Read original paper ↗ ·A GPT‐4 Reticular Chemist for Guiding MOF Discovery**
Angewandte Chemie International Edition · 2023
Zhiling Zheng, Zichao Rong, Nakul Rampal, Christian Borgs, Jennifer Chayes, Omar M. Yaghi
Read original paper ↗ ·A GPT‐4 Reticular Chemist for Guiding MOF Discovery**
Angewandte Chemie · 2023
Zhiling Zheng, Zichao Rong, Nakul Rampal, Christian Borgs, Jennifer Chayes, Omar M. Yaghi
Read original paper ↗ ·Comment on ‘Physics-based representations for machine learning properties of chemical reactions’
Machine Learning Science and Technology · 2023
Kevin Spiekermann, Thijs Stuyver, Lagnajit Pattanaik, William H. Green
Read original paper ↗ ·Organic pollutants removal from aqueous solutions using metal-organic frameworks (MOFs) as adsorbents: A review
Journal of environmental chemical engineering · 2023
Lixin Li, Jiazhen Han, Xiaohui Huang, Shan Qiu, Xihan Liu, Lilai Liu, Mengjie Zhao, Jiwei Qu, Jinlong Zou, Jun Zhang
Read original paper ↗ ·Machine learning-based evaluation of functional characteristics of Li-rich layered oxide cathode materials using the data of XPS and XRD spectra
Computational Materials Science · 2023
Natalia V. Kireeva, Vladislav S. Pervov, Aslan Yu. Tsivadze
Read original paper ↗ ·Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective
Langmuir · 2023
Hongjian Tang, Lunbo Duan, Jianwen Jiang
Read original paper ↗ ·Enhancing Structure–Property Relationships in Porous Materials through Transfer Learning and Cross-Material Few-Shot Learning
ACS Applied Materials & Interfaces · 2023
Hyunsoo Park, Yeonghun Kang, Jihan Kim
Read original paper ↗ ·Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis
Nature Communications · 2023
Manu Suvarna, Alain C. Vaucher, Sharon Mitchell, Teodoro Laino, Javier Pérez‐Ramírez
Read original paper ↗ ·Optimal Surrogate Models for Predicting the Elastic Moduli of Metal–Organic Frameworks via Multiscale Features
Chemistry of Materials · 2023
Jaejun Lee, Inhyo Lee, Jaejung Park, Heekyu Kim, Minseon Kim, Kyoungmin Min, Seung‐Chul Lee
Read original paper ↗ ·A rule-free workflow for the automated generation of databases from scientific literature
npj Computational Materials · 2023
Luke P. J. Gilligan, Matteo Cobelli, Valentin Taufour, Stefano Sanvito
Read original paper ↗ ·Discovery Precision: An effective metric for evaluating performance of machine learning model for explorative materials discovery
Computational Materials Science · 2023
Zhengheng Lian, Yingying Ma, Minjie Li, Wencong Lu, Wenyan Zhou
Read original paper ↗ ·Drug delivery system tailoring via metal-organic framework property prediction using machine learning: A disregarded approach
Materials Today Communications · 2023
Niki Pouyanfar, Mahnaz Ahmadi, Seyed Mohammad Ayyoubzadeh, Fatemeh Ghorbani-Bidkorpeh
Read original paper ↗ ·MatGD: Materials Graph Digitizer
ACS Applied Materials & Interfaces · 2023
Jaewoong Lee, Wonseok Lee, Jihan Kim
Read original paper ↗ ·Accelerating metal–organic framework discovery via synthesisability prediction: the MFD evaluation method for one-class classification models
Digital Discovery · 2024
Chi Zhang, Dmytro Antypov, Matthew J. Rosseinsky, Matthew Stephen Dyer
Read original paper ↗ ·Active learning of alchemical adsorption simulations; towards a universal adsorption model
Chemical Science · 2024
Etinosa Osaro, Fernando Fajardo-Rojas, Gregory M. Cooper, Diego A. Gómez‐Gualdrón, Yamil J. Colón
Read original paper ↗ ·Agent-based learning of materials datasets from the scientific literature
Digital Discovery · 2024
Mehrad Ansari, Seyed Mohamad Moosavi
Read original paper ↗ ·Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties
Chemical Science · 2024
Tsung-Wei Liu, Quan Nguyen, Adji Bousso Dieng, Diego A. Gómez‐Gualdrón
Read original paper ↗ ·Image and data mining in reticular chemistry powered by GPT-4V
Digital Discovery · 2024
Zhiling Zheng, Zhiguo He, Omar Khattab, Nakul Rampal, Matei Zaharia, Christian Borgs, Jennifer Chayes, Omar M. Yaghi
Read original paper ↗ ·Integrating crystallographic and computational approaches to carbon-capture materials for the mitigation of climate change
Journal of Materials Chemistry A · 2024
Eric Cockayne, Austin McDannald, Winnie Wong-Ng, Yu‐Sheng Chen, Jason B. Benedict, Felipe Gándara, Christopher H. Hendon, David A. Keen, Ute Kolb, Lan Li, Shengqian Ma, William Morris, Aditya Nandy, Tomče Runčevski, Mustapha Soukri, Anuroop Sriram, Janice A. Steckel, John E. Findley, Christopher E. Wilmer, Taner Yildirim, Wei Zhou, Levin Igor, Craig M. Brown
Read original paper ↗ ·Machine learning-based screening of Mn-PNP catalysts for the CO 2 reduction reaction using a region-wise ligand-encoded feature matrix
Energy Advances · 2024
Amitabha Das, Diptendu Roy, Shyama Charan Mandal, Biswarup Pathak
Read original paper ↗ ·Metadata for Scientific Experiment Reporting: A Case Study in Metal-Organic Frameworks
Communications in computer and information science · 2024
Xintong Zhao, Kyle Langlois, Jacob David Furst, Scott McClellan, Xiaohua Tony Hu, Yuan An, Diego A. Gómez‐Gualdrón, Fernando J. Uribe‐Romo, Jane Greenberg
Read original paper ↗ ·Progress toward the computational discovery of new metal–organic framework adsorbents for energy applications
Nature Energy · 2024
Peyman Z. Moghadam, Yongchul G. Chung, Randall Q. Snurr
Read original paper ↗ ·Building robust metal-organic frameworks with premade ligands
Coordination Chemistry Reviews · 2024
Yunlong Hou, Caoyu Yang, Zhongjie Yang, Huaqun Zhou, Leiming Guo, Jun Ming Guo, Jun Ming Guo, Xiaofei Zhang
Read original paper ↗ ·Predicting the decomposition temperatures of metal-organic frameworks based on the pair distribution functions using a deep learning model
Microporous and Mesoporous Materials · 2024
Nhut T. Huynh, Riley Wagner, Xiang Ming He, Kim‐Doang Nguyen
Read original paper ↗ ·Informative Training Data for Efficient Property Prediction in Metal–Organic Frameworks by Active Learning
Journal of the American Chemical Society · 2024
Ashna Jose, Émilie Devijver, N. Jakse, Roberta Poloni
Read original paper ↗ ·Machine Learning-Driven Ionic Liquids as Electrolytes for the Advancement of High-Voltage Dual-Ion Battery
Chemistry of Materials · 2024
Surya Sekhar Manna, Biswarup Pathak
Read original paper ↗ ·Evaluation of Open-Source Large Language Models for Metal–Organic Frameworks Research
Journal of Chemical Information and Modeling · 2024
Xuefeng Bai, Ya-Bo Xie, Xin Zhang, Honggui Han, Jian‐Rong Li
Read original paper ↗ ·Oxide ceramics of A2M3O12 family with negative and close-to-zero thermal expansion coefficients: Machine learning-based modeling of functional characteristics
Journal of Alloys and Compounds · 2024
Natalia V. Kireeva, Aslan Yu. Tsivadze
Read original paper ↗ ·From Synthesis Conditions to UiO-66 Properties: Machine Learning Approach
Chemistry of Materials · 2024
Kirill P. Larionov, Vasilii Yu. Evtushok
Read original paper ↗ ·Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties: application to NH3 adsorption
ChemRxiv · 2024
Tsung-Wei Liu, Quan Nguyen, Adji Bousso Dieng, Diego A. Gómez‐Gualdrón
Read original paper ↗ ·WS24: A diverse data set for predicting metal-organic framework stability in water and harsh environments with data-driven models
ChemRxiv · 2024
Gianmarco Terrones, S. Huang, Matthew P. Rivera, Shuwen Yue, Alondra Hernandez, Heather J. Kulik
Read original paper ↗ ·WS24: A diverse data set for predicting metal-organic framework stability in water and harsh environments with data-driven models
ChemRxiv · 2024
Gianmarco Terrones, S. Huang, Matthew P. Rivera, Shuwen Yue, Alondra Hernandez, Heather J. Kulik
Read original paper ↗ ·In Silico Generation of Chromium-Based MOFs with Abundant Active Sites for N2/CH4 Separation
The Journal of Physical Chemistry C · 2024
Wonseok Lee, Sangwon Lee, Jihan Kim
Read original paper ↗ ·The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
ACS Central Science · 2024
Anuroop Sriram, Sihoon Choi, Xiaohan Yu, Logan M. Brabson, Abhishek Das, Zachary W. Ulissi, Matt Uyttendaele, Andrew James Medford, David S. Sholl
Read original paper ↗ ·Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo
npj Computational Materials · 2024
Stephan Thaler, Felix Mayr, Siby Thomas, Alessio Gagliardi, Julija Zavadlav
Read original paper ↗ ·Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes
ChemRxiv · 2024
Ilia Kevlishvili, Roland G. St. Michel, Aaron G. Garrison, Jacob Toney, Husain Adamji, Haojun Jia, Heather J. Kulik
Read original paper ↗ ·Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery
Coordination Chemistry Reviews · 2024
Chunhua Li, Luqian Bao, Yixin Ji, Zhehang Tian, Mengyao Cui, Yubo Shi, Zhilei Zhao, Xianyou Wang
Read original paper ↗ ·Machine learning insights into predicting biogas separation in metal-organic frameworks
Communications Chemistry · 2024
Isabel Cooley, Samuel Boobier, Jonathan D. Hirst, Elena Besley
Read original paper ↗ ·Prediction of adsorption performance of ZIF-67 for malachite green based on artificial neural network using L-BFGS algorithm
Journal of Hazardous Materials · 2024
Xiaoqing Wang, Shangkun Liu, Shaolei Chen, Xubin He, Wenjing Duan, Shuojie Wang, Junzi Zhao, Liangquan Zhang, Qing Chen, Chunhua Xiong
Read original paper ↗ ·Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning
Venue not supplied by source · 2024
Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·Machine Learning-Driven Discovery and Structure–Activity Relationship Analysis of Conductive Metal–Organic Frameworks
Chemistry of Materials · 2024
Jinglong Lin, Huibao Zhang, Mojgan Asadi, Kai Zhao, Luming Yang, Yunlong Fan, Jintao Zhu, Qianyi Liu, Lei Sun, Wen Jun Xie, Chenru Duan, Fanyang Mo, Jin‐Hu Dou
Read original paper ↗ ·Computational Screening of Putative Catalyst Transition Metal Complexes as Guests in a Ga4L6–12 Nanocage
ChemRxiv · 2024
Clorice R. Reinhardt, Melissa T. Manetsch, Wan‐Lu Li, Teresa L. Head-Gordon, Heather J. Kulik
Read original paper ↗ ·Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes
Faraday Discussions · 2024
Ilia Kevlishvili, Roland G. St. Michel, Aaron G. Garrison, Jacob Toney, Husain Adamji, Haojun Jia, Yuriy Román‐Leshkov, Heather J. Kulik
Read original paper ↗ ·Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set
Journal of the American Chemical Society · 2024
Gianmarco Terrones, S. Huang, Matthew P. Rivera, Shuwen Yue, Alondra Hernandez, Heather J. Kulik
Read original paper ↗ ·Design and optimization of spherical agglomeration process based on machine learning strategy
AIChE Journal · 2024
Chenyang Zhao, Yanbo Liu, Shilin Guo, Shanshan Feng, Yiming Ma, Songgu Wu, Junbo Gong
Read original paper ↗ ·Isoreticular Curves: A Theory of Capillary Condensation To Model Water Sorption within Microporous Sorbents
Journal of the American Chemical Society · 2024
Julius J. Oppenheim, Mircea Dincă
Read original paper ↗ ·