Machine learning potential era of zeolite simulation

Ma, Sicong; Liu, Zhi-Pan

doi:10.1039/d2sc01225a

Cited by 19 publications

(19 citation statements)

References 104 publications

(143 reference statements)

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“…The calculation finely reproduced previous results on thermostability of PAs. Consistent with recent investigations, Δ G f of t2 is slightly positive and nearly thermal neutral, suggesting that t2 would be slightly less plausible with respect to OA (Figure ) and its concentration would be lower than OA at steady state. − ,− However, the energy barriers for t2 formation and hydrolysis into t1(OA) are similar and higher than ∼60 kJ/mol, showing that a large portion of t2 may exist for further evolution. − The calculated Δ G f of t3 and c3-3-1 are −13.5 and −16.9 kJ/mol, respectively, at 298.15 K (Table ) and are in reasonable agreement with the reported Δ G when the formed H 2 O was treated explicitly. ,, The slight difference can be attributed to the theoretical implementation and computational setups.…”

Section: Resultssupporting

confidence: 84%

Superior Thermostability of Poly-Silicic Acid Analogues of Zeolite Composite/Secondary Building Units: A Theoretical Investigation

Liu

Meng

2023

J. Phys. Chem. C

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Secondary building units and composite building units (S/CBUs) are recognized in zeolite frameworks and built up by tetrahedron TO4 units. They were not only identified in the precursor gel for zeolite synthesis but also proposed to be of great significance in interzeolite conversion. Poly-silicic acids of S/CBU structures (S/CBU PAs) are precursors and structural analogues of S/CBUs in zeolites. We performed an exhaustive density functional theory (DFT)-based investigation on thermostability and reaction thermodynamics along potential pathways for the formation and evolution of PAs containing 1–7 T atoms. We showed that most intermolecular condensations (IECs) are exergonic and IECs are among the major mechanisms for the growth and evolution of PAs. The exergonicity of an intramolecular condensation (IAC) depends strongly on the degree of condensation of the reactant PA and it can be even endergonic if the TO4 tetrahedrons are distorted by the T–O–T connection formed. Although S/CBU PAs can only be formed on specific pathways, it is general that the exergonicity for their formation is always superior over that for their non-S/CBU counterparts originated from the same PA. Evolution of S/CBU PAs to non-S/CBU PAs would be less exergonic or even endergonic as compared with the competing formation of S/CBU PA. These data suggest the superior thermostability of S/CBU PAs over non-S/CBU PAs formed by competing IECs and IACs. We expect these findings would help to understand the hydrothermal, solvothermal, and interzeolite conversion processes for the synthesis of zeolites.

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Section: Resultssupporting

confidence: 84%

Superior Thermostability of Poly-Silicic Acid Analogues of Zeolite Composite/Secondary Building Units: A Theoretical Investigation

Liu

Meng

2023

J. Phys. Chem. C

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“…Atomic simulations were also used to investigate zeolite structure and stability [10][11][12]. To computationally predict structure of zeolites, as well as to study their absorption mechanisms and ion exchange, Monte Carlo methods were applied [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…The application of machine learning techniques has also expanded substantially in recent years [12]. For example, they were used for design and discovery of new zeolites [25], or for prediction the formation of energetically stable hypothetical zeolites [26].…”

Section: Introductionmentioning

confidence: 99%

Use of Multiscale Data-Driven Surrogate Models for Flowsheet Simulation of an Industrial Zeolite Production Process

et al. 2022

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The production of catalysts such as zeolites is a complex multiscale and multi-step process. Various material properties, such as particle size or moisture content, as well as operating parameters—e.g., temperature or amount and composition of input material flows—significantly affect the outcome of each process step, and hence determine the properties of the final product. Therefore, the design and optimization of such processes is a complex task, which can be greatly facilitated with the help of numerical simulations. This contribution presents a modeling framework for the dynamic flowsheet simulation of a zeolite production sequence consisting of four stages: precipitation in a batch reactor; concentration and washing in a block of centrifuges; formation of droplets and drying in a spray dryer; and burning organic residues in a chain of rotary kilns. Various techniques and methods were used to develop the applied models. For the synthesis in the reactor, a multistage strategy was used, comprising discrete element method simulations, data-driven surrogate modeling, and population balance modeling. The concentration and washing stage consisted of several multicompartment decanter centrifuges alternating with water mixers. The drying is described by a co–current spray dryer model developed by applying a two-dimensional population balance approach. For the rotary kilns, a multi-compartment model was used, which describes the gas–solid reaction in the counter–current solids and gas flows.

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“…These ML atomic simulations bypass the heavy QM calculations and utilize ML models to link the atomic coordinates with the total energies by learning the PES data from QM calculations 64 . These new techniques are bound to reshaping profoundly the research in heterogeneous catalysis 61,62,65,66 .…”

mentioning

confidence: 99%

“…In our group, we proposed a set of power-type structural descriptors (PTSDs) as the structural descriptors for fitting the global PES data, where the spherical functions and the four-body terms are introduced 73 . The complex PTSD descriptors allow the ML atomic simulation in complex material systems, such as boron cluster 73 , zeolite 66,74,75 , phase interface 76 , and heterogeneous reactions [77][78][79] .…”

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confidence: 99%

Machine-learning atomic simulation for heterogeneous catalysis

2023

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Heterogeneous catalysis is at the heart of chemistry. New theoretical methods based on machine learning (ML) techniques that emerged in recent years provide a new avenue to disclose the structures and reaction in complex catalytic systems. Here we review briefly the history of atomic simulations in catalysis and then focus on the recent trend shifting toward ML potential calculations. The advanced methods developed by our group are outlined to illustrate how complex structures and reaction networks can be resolved using the ML potential in combination with efficient global optimization methods. The future of atomic simulation in catalysis is outlooked.

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Machine learning potential era of zeolite simulation

Abstract: Zeolite, for its great variety and complexity in structure and wide applications in chemistry, has long been the hot topic in chemical research. This Perspective first presents a short retrospect...

Cited by 19 publications

References 104 publications

Superior Thermostability of Poly-Silicic Acid Analogues of Zeolite Composite/Secondary Building Units: A Theoretical Investigation

Superior Thermostability of Poly-Silicic Acid Analogues of Zeolite Composite/Secondary Building Units: A Theoretical Investigation

Use of Multiscale Data-Driven Surrogate Models for Flowsheet Simulation of an Industrial Zeolite Production Process

Machine-learning atomic simulation for heterogeneous catalysis

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