Best‐Practice DFT Protocols for Basic Molecular Computational Chemistry**

Angewandte Chemie International Edition, Journal Year: 2022, Volume and Issue: 61(42)

Published: Sept. 14, 2022

Nowadays, many chemical investigations are supported by routine calculations of molecular structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share these quantum-chemical applies density functional theory (DFT) evaluated in atomic-orbital basis sets. This work provides best-practice guidance on the numerous methodological technical aspects DFT three parts: Firstly, we set stage introduce a step-by-step decision tree to choose computational protocol that models experiment as closely possible. Secondly, present recommendation matrix guide choice depending task at hand. A particular focus is achieving an optimal balance between accuracy, robustness, efficiency through multi-level approaches. Finally, discuss selected representative examples illustrate recommended protocols effect choices.

Language: Английский

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Best‐Practice DFT Protocols for Basic Molecular Computational Chemistry**

Angewandte Chemie, Journal Year: 2022, Volume and Issue: 134(42)

Published: Sept. 14, 2022

Abstract Nowadays, many chemical investigations are supported by routine calculations of molecular structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share these quantum‐chemical applies density functional theory (DFT) evaluated in atomic‐orbital basis sets. This work provides best‐practice guidance on the numerous methodological technical aspects DFT three parts: Firstly, we set stage introduce a step‐by‐step decision tree to choose computational protocol that models experiment as closely possible. Secondly, present recommendation matrix guide choice depending task at hand. A particular focus is achieving an optimal balance between accuracy, robustness, efficiency through multi‐level approaches. Finally, discuss selected representative examples illustrate recommended protocols effect choices.

Language: Английский

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Modulating Mxene‐Derived Ni‐Mo_m‐Mo_2‐mTiC₂T_x Structure for Intensified Low‐Temperature Ethanol Reforming

Advanced Energy Materials, Journal Year: 2023, Volume and Issue: 13(40)

Published: Sept. 15, 2023

Abstract The technology of steam reforming bioethanol has drawn great attention to green hydrogen production. However, catalyst deactivation always been a significant obstacle its applications. Here, series y Ni/Mo 2 TiC T x ( Ni/MTC) materials are tailored as robust catalysts for highly efficient long‐term ethanol reforming. results reveal that utilization efficiency up 95.6% and almost total conversion can be achieved at 550 °C using 10Ni/MTC‐72h catalyst. Moreover, this remarkable stability without obvious after 100 h reforming, which attributed the formation Ni─Mo alloy strong interaction Ni‐Mo m ‐Mo 2‐m structure. FTIR‐MS studies demonstrate superiority reinforcing low‐temperature activation, verified by faster acetate species than with Ni/Al O 3 . adsorption energies on surface Ni (−1.07 eV) Ni/MTC (−1.46 compared density functional theory calculations show activating during This study provides new implications stabilized construction, is expected substantially promote development application bioethanol‐to‐hydrogen production technologies.

Language: Английский

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A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 158(8)

Published: Feb. 2, 2023

Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range molecular systems, from liquids to materials. In this study, we explore the possibility combining computational efficiency DeePMD framework and demonstrated accuracy MB-pol data-driven, many-body potential train DNN for large-scale water across its phase diagram. We find that is able reliably reproduce results liquid water, but provides less accurate description vapor-liquid equilibrium properties. This shortcoming traced back inability correctly represent interactions. An attempt explicitly include information about effects new exhibits opposite performance, being properties, losing These suggest DeePMD-based are not "learn" and, consequently, interactions, which implies may limited ability predict properties state points included training process. The can still be exploited on data-driven potentials, thus enable large-scale, "chemically accurate" various with caveat target must been adequately sampled by reference order guarantee faithful representation associated

Language: Английский

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Realistic phase diagram of water from “first principles” data-driven quantum simulations

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: June 8, 2023

Since the experimental characterization of low-pressure region water's phase diagram in early 1900s, scientists have been on a quest to understand thermodynamic stability ice polymorphs molecular level. In this study, we demonstrate that combining MB-pol data-driven many-body potential for water, which was rigorously derived from "first principles" and exhibits chemical accuracy, with advanced enhanced-sampling algorithms, correctly describe quantum nature motion equilibria, enables computer simulations an unprecedented level realism. Besides providing fundamental insights into how enthalpic, entropic, nuclear effects shape free-energy landscape recent progress simulations, encode interactions, has opened door realistic computational studies complex systems, bridging gap between experiments simulations.

Language: Английский

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Near-infrared light-activatable, analgesic nanocomposite delivery system for comprehensive therapy of diabetic wounds in rats

Biomaterials, Journal Year: 2024, Volume and Issue: 305, P. 122467 - 122467

Published: Jan. 7, 2024

Language: Английский

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Constraints on the location of the liquid–liquid critical point in water

Nature Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Language: Английский

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High-entropy materials for electrocatalytic applications: a review of first principles modeling and simulations

Materials Research Letters, Journal Year: 2023, Volume and Issue: 11(9), P. 713 - 732

Published: June 26, 2023

High-entropy materials, for both complexity in structure and superiority performance, have been widely confirmed to be one possible kind of advanced electrocatalyst. Significant efforts dedicated modeling the atomic-level details high-entropy catalysts improve viability bottom-up design electrocatalysts. In this review, first, we survey developments various methods that are based on density functional theory. We review progress theory simulations emulating different Then, advancements materials electrocatalytic applications. Finally, present prospects field.Abbreviations: HEMs: materials; CCMs: compositionally complex DFT: theory; LDA: local approximation; GGA: generalized gradient VASP: Vienna Ab initio simulation package; ECP: effective core potential; PAW: projector-augmented wave VCA: virtual crystal CPA: coherent potential SQS: special quasi-random structures; SSOS: small set ordered SLAE: similar atomic environment; HEAs: alloys; FCC: face-centered cubic; BCC: body-centered HCP: hexagonal close-packed; ORR: oxygen reduction reaction; OER: oxide evolution HER: hydrogen RDS: rate-limiting step; AEM: adsorbate mechanism; LOM: lattice oxidation HEOs: oxides; OVs: vacancies; PDOS: projected densities states; ADR: ammonia decomposition NRR: nitrogen CO2RR: CO2 TMDC: transition metal dichalcogenide; TM: metal; AOR: alcohol GOR: glycerol UOR: urea HEI: intermetallic.

Language: Английский

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Extending density functional theory with near chemical accuracy beyond pure water

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Feb. 13, 2023

Density functional simulations of condensed phase water are typically inaccurate, due to the inaccuracies approximate functionals. A recent breakthrough showed that SCAN approximation can yield chemical accuracy for pure in all its phases, but only when density is corrected. This a crucial step toward first-principles biosimulations. However, weak dispersion forces ubiquitous and play key role noncovalent interactions among biomolecules, not included new approach. Moreover, naïve inclusion HF-SCAN ruins high water. Here we show systematic application principles density-corrected DFT yields (HF-r2SCAN-DC4) which recovers improves over water, also captures vital making it suitable solutions.

Language: Английский

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The Potential of Neural Network Potentials

ACS Physical Chemistry Au, Journal Year: 2024, Volume and Issue: 4(3), P. 232 - 241

Published: March 21, 2024

In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by combination of recent advances in quantum and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are breakthrough new tool that already enabling us to simulate systems at molecular scale with unprecedented accuracy speed, relying on nothing but fundamental laws. The continued development this approach realize Paul Dirac's 80-year-old vision using mechanics unify physics providing invaluable tools for understanding materials science, biology, earth sciences, beyond. era highly accurate efficient first-principles simulations provide wealth training data can be used build automated computational methodologies, such as diffusion models, design optimization scale. Large language models (LLMs) also evolve into increasingly indispensable literature review, coding, idea generation, scientific writing.

Language: Английский

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