v1v2v3 (latest)

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Chemical Reviews (Chem. Rev.), 2020

18 January 2020

Papers citing "Big-Data Science in Porous Materials: Materials Genomics and Machine Learning"

18 / 18 papers shown

Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores

Benhour Amirian

Ashley S. Dale

Sergei Kalinin

Jason Hattrick-Simpers

141

30 Nov 2025

Interaction Topological Transformer for Multiscale Learning in Porous Materials

123

23 Sep 2025

MOFGPT: Generative Design of Metal-Organic Frameworks using Language ModelsJournal of Chemical Information and Modeling (JCIM), 2025

Srivathsan Badrinarayanan

262

30 May 2025

Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted ZeolitesJournal of Materials Chemistry A (J. Mater. Chem. A), 2025

M. Petković

José-Manuel Vicent Luna

278

26 Mar 2025

Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and MOFs using Machine LearningSeparation and Purification Technology (Sep. Purif. Technol.), 2024

Subhadeep Dasgupta

Amal R S

P. Maiti

AI4CE

19 Jun 2024

MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling

Hengrui Zhang

Jie Chen

J. Rondinelli

Wei Chen

177

27 Dec 2023

Harnessing Data Augmentation to Quantify Uncertainty in the Early Estimation of Single-Photon Source Quality

D. Kedziora

A. Musiał

Wojciech Rudno-Rudziński

Bogdan Gabrys

164

22 Jun 2023

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model HackathonDigital Discovery (DD), 2023

Kevin Maik Jablonka

Qianxiang Ai

Alexander H Al-Feghali

S. Badhwar

Joshua D. Bocarsly Andres M Bran

...

658

170

09 Jun 2023

Equivariant Parameter Sharing for Porous Crystalline MaterialsInternational Symposium on Intelligent Data Analysis (IDA), 2023

Marko Petković

Pablo Romero-Marimon

Vlado Menkovski

Sofía Calero

208

04 Apr 2023

Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocksnpj Computational Materials (npj Comput Mater), 2023

138

17 Feb 2023

A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning ModelsSocial Science Research Network (SSRN), 2022

Gianmarco G. Terrones

Yongchul-Grego Chung

Heather J. Kulik

AI4CE

138

25 Oct 2022

$Prediction of $\textrm{CO}_2$ Adsorption in Nano-Pores with Graph Neural Networks$

Prediction of

\textrm{CO}_2

Adsorption in Nano-Pores with Graph Neural Networks

Guojing Cong

Anshul Gupta

R. Neumann

Maira Gatti de Bayser

Mathias Steiner

B. O. Conchúir

GNN

351

22 Aug 2022

SELFIES and the future of molecular string representationsPatterns (Patterns), 2022

...

381

234

31 Mar 2022

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discoveryCurrent Opinion in Chemical Engineering (Curr Opin Chem Eng), 2021

283

02 Nov 2021

Predicting the Efficiency of CO

_2

Sequestering by Metal Organic Frameworks Through Machine Learning Analysis of Structural and Electronic Properties

M. Manda

12 Oct 2021

Crystal structure prediction of materials with high symmetry using differential evolution

Wenhui Yang

Edirisuriya M Dilanga Siriwardane

Rongzhi Dong

Yuxin Li

Jianjun Hu

544

20 Apr 2021

Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks

207

02 Dec 2020

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Aditi S. Krishnapriyan

264

01 Oct 2020