Integrating crystallographic and computational approaches to carbon-capture materials for the mitigation of climate change DOI
Eric Cockayne, Austin McDannald, W. Wong‐Ng

et al.

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(38), P. 25678 - 25695

Published: Jan. 1, 2024

This article presents a perspective on the state of art in structure determination microporous carbon-capture materials and paths toward future progress this field, as discussed NIST workshop same title.

Language: Английский

Examining proton conductivity of metal–organic frameworks by means of machine learning DOI

Ivan V. Dudakov,

С. В. Савельев,

Iurii M. Nevolin

et al.

Physical Chemistry Chemical Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

The presented multimodal transformer networks quantitatively reproduce experimental proton conductivity and the underlying conduction mechanism provide predictive uncertainty estimates.

Language: Английский

Citations

0
Using experimental data in computationally guided rational design of inorganic materials with machine learning DOI Creative Commons
Heather J. Kulik

Journal of materials research/Pratt's guide to venture capital sources, Journal Year: 2025, Volume and Issue: unknown

Published: April 8, 2025

Language: Английский

Citations

0
Advancing electrocatalyst discovery through the lens of data science: State of the art and perspectives☆ DOI Creative Commons
Xue Jia, Tianyi Wang, Di Zhang

et al.

Journal of Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 116162 - 116162

Published: April 1, 2025

Language: Английский

Citations

0
Facilitating Screening of MOFs for Mixed Matrix Membranes Using Machine Learning and the Maxwell Model DOI Creative Commons
Xiaohan Yu,

Jia Yuan Chng,

David S. Sholl

et al.

The Journal of Physical Chemistry C, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

Citations

0
A Dataset for Investigations of Amine-Impregnated Solid Adsorbent for Direct Air Capture DOI Creative Commons
Eryu Wang, Liping Luo, Jiachuan Wang

et al.

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: May 1, 2025

Language: Английский

Citations

0
Catalytic Large Atomic Model (CLAM): A Machine-Learning-Based Interatomic Potential Universal Model DOI Creative Commons

Zhihong Wu,

Lei Zhou, Peng‐Fei Hou

et al.

Published: Aug. 22, 2024

Catalysis involves complex reactions with dynamic changes in catalyst morphology, challenging the capabilities of traditional Density Functional Theory (DFT) methods. To address this, we present Catalytic Large Atomic Model (CLAM), a machine-learning-based interatomic potential designed for heterogeneous catalysis. Trained on comprehensive dataset that includes metals, alloys, oxides, clusters, zeolites, 2D materials, and small molecules, CLAM ensures high accuracy across diverse catalytic systems. We also introduce "local fine-tuning" algorithm enhances model’s applicability by accelerating structural optimizations transition state searches while maintaining precision. Additionally, facilitates rapid reaction network construction efficient kinetic analysis. This work advances computational catalysis providing universal robust tool design mechanism exploration.

Language: Английский

Citations

2
Integrating direct air capture with algal biofuel production to reduce cost, energy, and GHG emissions DOI Creative Commons

Shavonn D'Souza,

John W. Johnston,

Valerie M. Thomas

et al.

Journal of CO2 Utilization, Journal Year: 2024, Volume and Issue: 86, P. 102911 - 102911

Published: Aug. 1, 2024

Language: Английский

Citations

2
Machine learning of metal-organic framework design for carbon dioxide capture and utilization DOI Creative Commons
Yang Jeong Park, Sungroh Yoon, Sung Eun Jerng

et al.

Journal of CO2 Utilization, Journal Year: 2024, Volume and Issue: 89, P. 102941 - 102941

Published: Oct. 21, 2024

Language: Английский

Citations

2
Application of graph neural network in computational heterogeneous catalysis DOI Creative Commons
Zihao Jiao, Ya Liu, Ziyun Wang

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(17)

Published: Nov. 1, 2024

Heterogeneous catalysis, as a key technology in modern chemical industries, plays vital role social progress and economic development. However, its complex reaction process poses challenges to theoretical research. Graph neural networks (GNNs) are gradually becoming tool this field they can intrinsically learn atomic representation consider connection relationship, making them naturally applicable molecular systems. This article introduces the basic principles, current network architectures, datasets of GNNs reviews application GNN heterogeneous catalysis from accelerating materials screening exploring potential energy surface. In end, we summarize main prospects future research endeavors.

Language: Английский

Citations

2
WS24: A diverse data set for predicting metal-organic framework stability in water and harsh environments with data-driven models DOI Creative Commons
Gianmarco Terrones, Shih-Peng Huang,

Matt Rivera

et al.

Published: April 23, 2024

Metal-organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity air environment. Consequently, it is useful to predict whether MOF water-stable before investing time resources into synthesis. Existing heuristics for designing MOFs generality artificially limit diversity explored chemistry due narrowly defined criteria. Machine learning (ML) models offer promise improve predictions require diverse experimental data be trained. In an improvement on previous efforts, we enlarge available training prediction by over 400%, adding 911 labels assigned through semi-automated manuscript analysis curate new set WS24. The additional shown ML model performance (test ROC-AUC > 0.8) both harsher acidic conditions. We illustrate how expanded can used previously developed activation carry out genetic algorithms quickly screen ~10,000 from space hundreds thousands candidates multivariate (i.e., activation, water, acid). Model algorithm results uncover metal- geometry-specific design rules robust MOFs. this work, which disseminate easy-to-use web interface, expected contribute toward accelerated discovery novel, such as direct capture treatment.

Language: Английский

Citations

1