A computational workflow to discover novel liquid organic hydrogen carriers and their dehydrogenation routes

Paragian, Kristin; Li, Bowen; Massino, Morgan; Rangarajan, Srinivas

doi:10.1039/d0me00105h

Cited by 17 publications

(11 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the few exceptions are theoretical studies in the academic and patent literature. 12,13 Moreover, the LOHC system acetone/isopropanol has been recently described for direct electrochemical hydrogen utilization using direct organic fuel cells. [14][15][16][17] Herein, we show that oxygen-containing LOHC systems offer indeed exceptionally high hydrogen storage capacities, very attractive low-temperature hydrogen release performance and very attractive features for potential technical applications if oxygen-functionalities and cycloalkyl/aromatic moieties are combined in their molecular structure.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Better through oxygen functionality? The benzophenone/dicyclohexylmethanol LOHC-system

Zakgeym

Hofmann

Maurer

et al. 2023

Sustainable Energy Fuels

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Better through oxygen functionality? The benzophenone/dicyclohexylmethanol LOHC-system

Zakgeym

Hofmann

Maurer

et al. 2023

Sustainable Energy Fuels

View full text Add to dashboard Cite

show abstract

“…Other promising molecules exist (1,2-dihydro-1,2-azaborine, phenazine) and can be found in the cited reviews [44]. Note that a recent paper also lists possible new H 2 -rich/H 2 -lean pairs based on a computational study [45].…”

Section: Hydrogen-rich/hydrogen-lean Pairsmentioning

confidence: 99%

Liquid Organic Hydrogen Carriers or Organic Liquid Hydrides: 40 Years of History

Meille

Pitault

2021

Reactions

View full text Add to dashboard Cite

The term LOHC stands for Liquid Organic Hydrogen Carriers. The term has been so well accepted by the scientific community that the studies published before the existence of this name are not very visible. In this mini-review, we have tried to rehabilitate various studies that deserve to be put back in the spotlight in the present context. Studies indeed began in the early 1980s and many publications have compared the use of various organic carriers, various catalysts and reactors. Recent reviews also include the economic aspects of this concept.

show abstract

“…Datasets. We conduct experiments on four tabular datasets [33], [34] and two graph datasets (molecules [35] and brain networks [30]). We split the instances into training (70%) and test (30%) sets, and Section IV-H provides details about pairs generation.…”

Section: A Experimental Settingsmentioning

confidence: 99%

“…The algorithm did identify fragments, such as "CC-C1COCN", that appear only in the negative class, but belong to motifs that might be considered favorable. We posit that the reason for this is that the junction tree cannot accurately specify how two cliques are connected (i.e., to which atom of the ring "C1COCN1" is the fragment "CC" connected to), and that the dataset used to train the GNN may not be sufficiently large and diverse due to its construction [35].…”

Section: F Sensitivity Analysismentioning

confidence: 99%

Self-learn to Explain Siamese Networks Robustly

Chen¹,

Shen²,

Ma³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data are scarce and imbalanced. As these applications make high-stake decisions and involve societal values like fairness and transparency, it is critical to explain the learned models. We aim to study post-hoc explanations of Siamese networks (SN) widely used in learning to compare. We characterize the instability of gradientbased explanations due to the additional compared object in SN, in contrast to architectures with a single input instance. We propose an optimization framework that derives global invariance from unlabeled data using self-learning to promote the stability of local explanations tailored for specific query-reference pairs. The optimization problems can be solved using gradient descent-ascent (GDA) for constrained optimization, or SGD for KL-divergence regularized unconstrained optimization, with convergence proofs, especially when the objective functions are nonconvex due to the Siamese architecture. Quantitative results and case studies on tabular and graph data from neuroscience and chemical engineering show that the framework respects the self-learned invariance while robustly optimizing the faithfulness and simplicity of the explanation. We further demonstrate the convergence of GDA experimentally.

show abstract

A computational workflow to discover novel liquid organic hydrogen carriers and their dehydrogenation routes

Abstract: Two-way liquid organic hydrogen carriers (LOHC) – organic molecules that store hydrogen reversible chemical bonds – is an emerging concept for on-demand storage and transportation of hydrogen (and thereby energy)....

Cited by 17 publications

References 52 publications

Better through oxygen functionality? The benzophenone/dicyclohexylmethanol LOHC-system

Better through oxygen functionality? The benzophenone/dicyclohexylmethanol LOHC-system

Liquid Organic Hydrogen Carriers or Organic Liquid Hydrides: 40 Years of History

Self-learn to Explain Siamese Networks Robustly

Contact Info

Product

Resources

About