Inverse Design of Nanoclusters for Light-Controlled CO₂–HCOOH Interconversion

With global push of hydrogen economy, efficient scenarios for hydrogen storage, transportation, and generation are indispensable. Here we devise a strategy for controllable hydrogen fuel storage and retrieval via light-switched CO2-to-HCOOH interconversion. To realize it, palladium sulfide nanocluster catalysts with multiple specific functionalities are directly searched by our home-developed inverse design approach based on genetic algorithm (IDOGA) and ab initio calculations. Over 500 low-energy PdxSy (x + y ≤ 30) clusters are sieved through a multiobjective function combining stability, activity, optical absorption, and reduction capability of photocarriers. The structure–property relationships and key factors governing the trade-off among these stringent criteria are disclosed. Finally, 14 candidate PdxSy clusters with proper sulfidation degree and high stability in an aqueous environment have been screened. Our IDOGA program provides a general approach for inverse search of nanoclusters with any designated elemental compositions and functionalities for any device applications

Transition-Metal Interlink Neural Network: Machine Learning of 2D Metal–Organic Frameworks with High Magnetic Anisotropy

Two-dimensional (2D) metal–organic framework (MOF) materials with large perpendicular magnetic anisotropy energy (MAE) are important candidates for high-density magnetic storage. The MAE-targeted high-throughput screening of 2D MOFs is currently limited by the time-consuming electronic structure calculations. In this study, a machine learning model, namely, transition-metal interlink neural network (TMINN) based on a database with 1440 2D MOF materials is developed to quickly and accurately predict MAE. The well-trained TMINN model for MAE successfully captures the general correlation between the geometrical configurations and the MAEs. We explore the MAEs of 2583 other 2D MOFs using our trained TMINN model. From these two databases, we obtain 11 unreported 2D ferromagnetic MOFs with MAEs over 35 meV/atom, which are further demonstrated by the high-level density functional theory calculations. Such results show good performance of the extrapolation predictions of TMINN. We also propose some simple design rules to acquire 2D MOFs with large MAEs by building a Pearson correlation coefficient map between various geometrical descriptors and MAE. Our developed TMINN model provides a powerful tool for high-throughput screening and intentional design of 2D magnetic MOFs with large MAE

Transition-Metal Interlink Neural Network: Machine Learning of 2D Metal–Organic Frameworks with High Magnetic Anisotropy

Two-dimensional (2D) metal–organic framework (MOF) materials with large perpendicular magnetic anisotropy energy (MAE) are important candidates for high-density magnetic storage. The MAE-targeted high-throughput screening of 2D MOFs is currently limited by the time-consuming electronic structure calculations. In this study, a machine learning model, namely, transition-metal interlink neural network (TMINN) based on a database with 1440 2D MOF materials is developed to quickly and accurately predict MAE. The well-trained TMINN model for MAE successfully captures the general correlation between the geometrical configurations and the MAEs. We explore the MAEs of 2583 other 2D MOFs using our trained TMINN model. From these two databases, we obtain 11 unreported 2D ferromagnetic MOFs with MAEs over 35 meV/atom, which are further demonstrated by the high-level density functional theory calculations. Such results show good performance of the extrapolation predictions of TMINN. We also propose some simple design rules to acquire 2D MOFs with large MAEs by building a Pearson correlation coefficient map between various geometrical descriptors and MAE. Our developed TMINN model provides a powerful tool for high-throughput screening and intentional design of 2D magnetic MOFs with large MAE

Inverse Design of Nanoclusters for Light-Controlled CO₂–HCOOH Interconversion

With global push of hydrogen economy, efficient scenarios for hydrogen storage, transportation, and generation are indispensable. Here we devise a strategy for controllable hydrogen fuel storage and retrieval via light-switched CO2-to-HCOOH interconversion. To realize it, palladium sulfide nanocluster catalysts with multiple specific functionalities are directly searched by our home-developed inverse design approach based on genetic algorithm (IDOGA) and ab initio calculations. Over 500 low-energy PdxSy (x + y ≤ 30) clusters are sieved through a multiobjective function combining stability, activity, optical absorption, and reduction capability of photocarriers. The structure–property relationships and key factors governing the trade-off among these stringent criteria are disclosed. Finally, 14 candidate PdxSy clusters with proper sulfidation degree and high stability in an aqueous environment have been screened. Our IDOGA program provides a general approach for inverse search of nanoclusters with any designated elemental compositions and functionalities for any device applications

Table_1_Hydrated Sodium Ion Clusters [Na+(H2O)n (n = 1–6)]: An ab initio Study on Structures and Non-covalent Interaction.DOCX

Structural, thermodynamic, and vibrational characteristics of water clusters up to six water molecules incorporating a single sodium ion [Na+(H2O)n (n = 1–6)] are calculated using a comprehensive genetic algorithm combined with density functional theory on global search, followed by high-level ab initio calculation. For n ≥ 4, the coordinated water molecules number for the global minimum of clusters is 4 and the outer water molecules connecting with coordinated water molecules by hydrogen bonds. The charge analysis reveals the electron transfer between sodium ions and water molecules, providing an insight into the variations of properties of O–H bonds in clusters. Moreover, the simulated infrared (IR) spectra with anharmonic correction are in good agreement with the experimental results. The O–H stretching vibration frequencies show redshifts comparing with a free water molecule, which is attributed to the non-covalent interactions, including the ion–water interaction, and hydrogen bonds. Our results exhibit the comprehensive geometries, energies, charge, and anharmonic vibrational properties of Na+(H2O)n (n = 1–6), and reveal a deeper insight of non-covalent interactions.</p

Structures and Spectroscopic Properties of F^–(H₂O)_<i>n</i> with <i>n</i> = 1–10 Clusters from a Global Search Based On Density Functional Theory

Using a genetic algorithm incorporated in density functional theory, we explore the ground state structures of fluoride anion–water clusters F^–(H₂O)_<i>n</i> with <i>n</i> = 1–10. The F^–(H₂O)_<i>n</i> clusters prefer structures in which the F^– anion remains at the surface of the structure and coordinates with four water molecules, as the F^–(H₂O)_<i>n</i> clusters have strong F^––H₂O interactions as well as strong hydrogen bonds between H₂O molecules. The strong interaction between the F^– anion and adjacent H₂O molecule leads to a longer O–H distance in the adjacent molecule than in an individual water molecule. The simulated infrared (IR) spectra of the F^–(H₂O)_1–5 clusters obtained via second-order vibrational perturbation theory (VPT2) and including anharmonic effects reproduce the experimental results quite well. The strong interaction between the F^– anion and water molecules results in a large redshift (600–2300 cm^–1) of the adjacent O–H stretching mode. Natural bond orbital (NBO) analysis of the lowest-energy structures of the F^–(H₂O)_1–10 clusters illustrates that charge transfer from the lone pair electron orbital of F^– to the antibonding orbital of the adjacent O–H is mainly responsible for the strong interaction between the F^– anion and water molecules, which leads to distinctly different geometric and vibrational properties compared with neutral water clusters

Rational Design of Full-Color Fluorescent C₃N Quantum Dots

Carbon-based quantum dots (QDs) exhibit unique photoluminescence due to size-dependent quantum confinement, giving rise to fascinating full-color emission properties. Accurate emission calculations using time-dependent density functional theory are a time-costing and expensive process. Herein, we employed an artificial neural network (ANN) combined with statistical learning to establish the relationship between geometrical/electronic structures of ground states and emission wavelength for C3N QDs. The emission energy of these QDs can be doubly modulated by size and edge effects, which are governed by the number of C4N2 rings and the CH group, respectively. Moreover, these two structural characteristics also determine the phonon vibration mode of C3N QDs to harmonize the emission intensity and lifetime of hot electrons in the electron–hole recombination process, as indicated by nonadiabatic molecular dynamics simulation. These computational results provide a general approach to atomically precise design the full-color fluorescent carbon-based QDs with targeted functions and high performance

Rational Design of Full-Color Fluorescent C₃N Quantum Dots

Carbon-based quantum dots (QDs) exhibit unique photoluminescence due to size-dependent quantum confinement, giving rise to fascinating full-color emission properties. Accurate emission calculations using time-dependent density functional theory are a time-costing and expensive process. Herein, we employed an artificial neural network (ANN) combined with statistical learning to establish the relationship between geometrical/electronic structures of ground states and emission wavelength for C3N QDs. The emission energy of these QDs can be doubly modulated by size and edge effects, which are governed by the number of C4N2 rings and the CH group, respectively. Moreover, these two structural characteristics also determine the phonon vibration mode of C3N QDs to harmonize the emission intensity and lifetime of hot electrons in the electron–hole recombination process, as indicated by nonadiabatic molecular dynamics simulation. These computational results provide a general approach to atomically precise design the full-color fluorescent carbon-based QDs with targeted functions and high performance

Structure Evolution of Transition Metal-doped Gold Clusters M@Au₁₂ (M = 3d–5d): Across the Periodic Table

The comprehensive genetic algorithm (CGA) incorporated with density functional theory (DFT) calculations were used for a global search of the potential energy surfaces of M@Au12 (M = 3d–5d) clusters. The feasibility of the revTPSS functional was confirmed by comparison between experimental and calculated data such as bond lengths and vibrational frequencies of transition metal dimers. We found the ground state structures of Mo/W@Au12 clusters to be the perfect icosahedron cage. The V/Nb/Ta/Tc/Re@Au12 clusters were found to have the distorted icosahedron cages owing to Jahn–Teller effects. The lowest energy structures of Sc/Ti/Cr/Mn/Fe/Co/Ru/Rh/Ir@Au12 have the perfect or distorted magnetic cuboctahedron cages, which can be explained by a 14-electron rule in a cuboctahedral ligand field (M2+@Au122–). Y/Zr/La/Hf@Au12 clusters have the half-cage ground states, while Ni/Cu/Zn/Pt/Ag/Cd/Pd/Au/HgAu12 clusters have oblate ground states. The scalar relativistic X2C method combined with revTPSS/TZP were used to calculate the energy difference between the magnetic cuboctahedron ground state and the icosahedron isomers of Cr@Au12 using energy decomposition analysis-natural orbitals for chemical valence. The magnetic M2+@Au122– model was found to significantly enhance the d orbital interactions of transition metal atoms and reduce Pauli repulsion, resulting in magnetic cuboctahedra as the more stable structures

Typing versus texting and manual versus cell phone calculator wrist motion.

Maximum medial extension and range of motion (ROM) and minimum medial extension (A) and female maximum lateral extension and ROM (B) while texting or typing, and maximum medial extension, maximum lateral extension, and lateral extension ROM while using a cell phone or manual calculator (C, least squares mean ± SEM). Columns with different letters within each comparison are significantly different (p<0.05).</p