Machine-learning atomic simulation for heterogeneous catalysis

npj Computational Materials, Journal Year: 2023, Volume and Issue: 9(1)

Published: Jan. 7, 2023

Abstract 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 structures and reaction complex catalytic systems. Here we review briefly history atomic simulations then focus trend shifting toward ML potential calculations. The advanced developed by our group are outlined illustrate how networks can be resolved using combination with efficient global optimization methods. future simulation outlooked.

Language: Английский

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Single‐atom materials: The application in energy conversion

Interdisciplinary materials, Journal Year: 2024, Volume and Issue: 3(1), P. 74 - 86

Published: Jan. 1, 2024

Abstract Single‐atom materials (SAMs) have become one of the most important power sources to push field energy conversion forward. Among main types energy, including thermal electrical solar and biomass SAMs realized ultra‐high efficiency show an appealing future in practical application. More than high activity, uniform active sites also provide a convincible model for chemists design comprehend mechanism behind phenomenon. Therefore, we presented insightful review application single‐atom material conversion. The challenges (e.g., accurate synthesis application) directions machine learning efficient design) applications are included, aiming guidance research next step.

Language: Английский

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Embracing data science in catalysis research

Nature Catalysis, Journal Year: 2024, Volume and Issue: 7(6), P. 624 - 635

Published: April 23, 2024

Language: Английский

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Recent Progress on Computation‐Guided Catalyst Design for Highly Efficient Nitrogen Reduction Reaction

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: 34(34)

Published: April 5, 2024

Abstract Electrochemical nitrogen reduction reaction (NRR) for ammonia synthesis has attracted great interest in recent years, which presents a carbon‐free alternative to the energy‐intensive Haber–Bosch process. Besides, NRR also provides promising coverage route of renewable energy since NH 3 is considered second generation hydrogen while possessing established technologies liquefaction, storage, and transport. However, there are long‐term challenges catalyst design due its low intrinsic activity unsatisfied selectivity. Fortunately, by conducting extensive explorations this field, much progress achieved boosting performance. Herein, from view atomic/electronic level, three promotion effects summarized (i.e., electron effect, geometry ligand effect), tackle Representative studies with taking fully advantages reviewed, realized remarkable Finally, future research directions prospects discussed. It highly expected that review will enable advancement catalysts promote further development electrochemical NRR.

Language: Английский

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ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(3), P. 799 - 811

Published: Jan. 18, 2024

The pursuit of designing smart and functional materials is paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, numerous others. Consequently, researchers are actively involved in the development innovative models strategies for design. Recent advancements analytical tools, experimentation, computer technology additionally enhance design possibilities. Notably, data-driven techniques like artificial intelligence machine learning have achieved substantial progress exploring applications within science. One approach, ChatGPT, a large language model, holds transformative potential addressing complex queries. In this article, we explore ChatGPT's understanding science by assigning some simple tasks subareas computational findings indicate that while ChatGPT may make minor errors accomplishing general tasks, it demonstrates capability to learn adapt through human interactions. However, issues output consistency, probable hidden errors, ethical consequences should be addressed.

Language: Английский

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Ethane Oxidative Dehydrogenation over TiO₂ and M/TiO₂ Catalysts: Unraveling the Surface Structure Evolution, Oxygen Species Type, and Role of Doped Metal in Tuning Catalytic Performance

ACS Catalysis, Journal Year: 2025, Volume and Issue: 15(3), P. 2095 - 2109

Published: Jan. 22, 2025

TiO2 has better catalytic performance toward alkane oxidative dehydrogenation (ODH) due to adjustable surface oxygen species; however, identifying the dynamic evolution process of structure and its effect on type species is still challenging. In this work, combined methods density functional theory calculations kinetic Monte Carlo simulations were employed fully investigate ethane ODH over 15 types single-atom metal-doped (M/TiO2) catalysts. The results clearly unravel mechanism formed during in tuning performance. Surface vacancies enhance with unsaturated Ti4CN as active site, while surface-adsorbed limit Single-atom can change O2(g) adsorption mode dissociation activity adjust further regulate by electronic properties adsorbed atoms. Interestingly, screened V/TiO2–O* catalyst exhibits high C2H4(g) production selectivity at optimal temperature 873.15 K a C2H6(g) partial pressure 0.2 bar, which thoroughly eliminates negative more charge transfer from V atom. This work provides theoretical basis structural clue for designing an regulating metal oxide.

Language: Английский

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A Comprehensive Theoretical Study of the Mechanism for Dry Reforming of Methane on a Ni₄/ZrO₂(101) Catalyst Under External Electric Fields: The Role of Interface and Oxygen Vacancy

ACS Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 3846 - 3859

Published: Feb. 19, 2025

Language: Английский

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Optimization of High-Entropy Alloy Catalyst for Ammonia Decomposition and Ammonia Synthesis

The Journal of Physical Chemistry Letters, Journal Year: 2021, Volume and Issue: 12(21), P. 5185 - 5192

Published: May 26, 2021

The successful synthesis of high-entropy alloy (HEA) nanoparticles, a long-sought goal in materials science, opens new frontier science with applications across catalysis, structural alloys, and energetic materials. Recently, Co25Mo45Fe10Ni10Cu10 HEA made earth-abundant elements was shown to have high catalytic activity for ammonia decomposition, which rivals that state-of-the-art, but prohibitively expensive, ruthenium catalysts. Using computational approach based on first-principles calculations conjunction data analytics machine learning, we build model rapidly compute the adsorption energy H, N, NHx (x = 1, 2, 3) species CoMoFeNiCu surfaces varied compositions atomic arrangements. We show 25/45 Co/Mo ratio identified experimentally as most active composition decomposition increases likelihood surface adsorbs nitrogen equivalently while at same time interacting moderately strongly intermediates. Our study underscores importance modeling learning identify optimize alloys their near-infinite design space.

Language: Английский

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Deciphering Metal–Oxide and Metal–Metal Interplay via Surface Organometallic Chemistry: A Case Study with CO₂ Hydrogenation to Methanol

Journal of the American Chemical Society, Journal Year: 2021, Volume and Issue: 143(18), P. 6767 - 6780

Published: May 4, 2021

Processes that rely on heterogeneous catalysts underpin the production of bulk chemicals and fuels. In spite this, understanding interplay between structure reactivity these complex materials remains elusive—rendering rational improvement existing systems challenging. Herein, we describe efforts to understand capable selective thermochemical conversion CO2 methanol using a surface organometallic chemistry (SOMC) approach. particular, focus remarkable, but often subtle, roles metal–metal synergy metal–support interfaces in determining many different for methanol. Specifically, explore synthetic analytical strategies systematic study synergistic behaviors multi-component catalytic context hydrogenation, discuss how insights obtained can inform design materials. We also address limitations approach employed opportunities expand upon observations emerging from this work, before attempting establish transposable generalizable trends Cu-based beyond.

Language: Английский

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High-throughput oxygen chemical potential engineering of perovskite oxides for chemical looping applications

Energy & Environmental Science, Journal Year: 2022, Volume and Issue: 15(4), P. 1512 - 1528

Published: Jan. 1, 2022

Integrating DFT, machine learning and experimental verifications, a high-throughput screening scheme is performed to rationally engineer the redox properties of SrFeO 3− δ based perovskites for chemical looping applications.

Language: Английский

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