v1v2v3 (latest)

Topological Descriptors Help Predict Guest Adsorption in Nanoporous Materials

Journal of Physical Chemistry C (JPCC), 2020

16 January 2020

Aditi S. Krishnapriyan

Papers citing "Topological Descriptors Help Predict Guest Adsorption in Nanoporous Materials"

8 / 8 papers shown

MOFFlow: Flow Matching for Structure Prediction of Metal-Organic FrameworksInternational Conference on Learning Representations (ICLR), 2024

529

07 Oct 2024

Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites

Marko Petković

J. M. Vicent‐Luna

Vlado Menkovski

Sofía Calero

130

19 Mar 2024

MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework DesignInternational Conference on Learning Representations (ICLR), 2023

278

16 Oct 2023

$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

241

22 Aug 2022

Topology-Driven Generative Completion of Lacunae in Molecular Data

Dmitry Zubarev

Petar Ristoski

141

29 Jul 2022

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

Nicolas Swenson

Aditi S. Krishnapriyan

A. Buluç

Dmitriy Morozov

Katherine Yelick

165

30 Oct 2020

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

Aditi S. Krishnapriyan

159

01 Oct 2020

Big-Data Science in Porous Materials: Materials Genomics and Machine LearningChemical Reviews (Chem. Rev.), 2020

279

414

18 Jan 2020