The effect of Fe vacancies and Cu adhesion on the magnetic properties of Fe3GeTe2.

Fe3GeTe2 is a promising two-dimensional magnetic material that exhibits a high Curie temperature with monolayer geometry. Using the first principles calculation method, the effects of both Fe vacancies and contact with Cu electrodes on the magnetic properties of Fe3GeTe2 are studied. Calculation results determined that Fe vacancies occur preferentially on the FeII site. As a result, the magnetic moment of FeI ions decreases significantly, while the magnetic moment of FeII increases due to the presence of the FeII vacancies. The Cu(111) layers that act as the electrodes have moderately strong bonds with Fe3GeTe2 but they are found not to distort the primary structure of the Fe3GeTe2 monolayers, thus producing a stable Cu(111)/Fe3GeTe2 interface. The magnetic moments of FeI atoms at the Fe3GeTe2 layer surface decrease substantially following the adhesion of Cu(111) layers. The effect for other Fe atoms, however, is relatively weak. These results are useful for experimental studies and can promote the applications of Fe3GeTe2 in spin electronics.

Study of the inorganic substitution in a functionalized UiO-66 metal-organic framework.

In this study the band gap modulation was studied in response to inorganic ion substitution within a thermally stable UiO-66 metal-organic framework (MOF). A combination of density functional theory prediction in conjunction with experimental predictions were used to map out the complete composition space for three inorganic ions (Zr, Ti, Hf) and three functional groups. The three functional groups include an amino group (NH2), a nitro group (NO2), and a hydrogenated case (H). The smallest band gap that experimentally determined was for a partially substituted UiO-66(Ti5Zr1)-NH2 resulting in 2.60 eV. Theoretical results indicated that Ti can be fully substituted within the lattice resulting in a predicted band gap as low as 1.62 eV. Modulation was a result of a mid-gap state introduced through the amino functionalization and HOMO shifting as a result of increased binding of the Ti-O-C bonds.

Ab initio calculation of electronic charge mobility in metal-organic frameworks.

A density functional theory approach coupled with the Boltzmann transport equation within the relaxation time approximation was used to investigate the charge mobility for three MOF functionalization designs. The specific MOF investigated was a Zr-UiO-66 MOF with three functionalizations that included benzenedicarboxylate (BDC), BDC functionalized with an amino group (BDC + NH2), and BDC functionalized with a nitro group (BDC + NO2). Previous experimental studies have confirmed a 40% decrease in the optical band-gap with functionization; this study predicted an accompanying decrease in mobility by 14%. On the contrary, the charge density was found to increase with functionalization. The culmination of these two findings resulted in a predicted conductivity of approximately 3.8 × 10(-8) S cm(-1) for BDC design and decreasing less than 2% for other cases. Furthermore, band conduction was confirmed for this MOF design as a result of the de-localized π electron of the carbon atoms along the organic linker. Overall, the functionalization proved to decrease mobility; however, it was evident that the functionalization has potential for tailoring the spectral layout of low lying unoccupied orbitals and ultimately the charge concentration, which could prove to be important for increasing the overall conductivity of MOFs.

Band gap modulation of functionalized metal-organic frameworks.

Metal-organic frameworks (MOFs) have been envisioned as alternatives to planar metallic catalysts for solar-to-fuel conversion. This is a direct result of their porous structure and the ability to tailor their optical absorption properties. This study investigates the band gap modulation of Zr-UiO-66 MOFs from both the computational and experimental points of view for three linker designs that include benzenedicarboxylate (BDC), BDC-NO2, and BDC-NH2. Emphasis in this study was aimed at understanding the influence of the bonding between the aromatic ring and the functional group. A ground state density functional theory (DFT) calculation was carried out to investigate the projected density of states and the origins of the modulation. A time-dependent density functional theory (TDDFT) calculation of the hydrogen terminated linkers confirmed the modulation and accounted for the electron charge transfer providing comparable optical band gap predictions to experimental results. Computational results confirmed the hybridization of the carbon-nitrogen bond in conjunction with the donor state resulting from the NH2 functionalization. The NO2 functionalization resulted in an acceptor configuration with marginal modification to the valence band maximum. The largest modulation was BDC-NH2 with a band gap of 2.75 eV, followed by BDC-NO2 with a band gap of 2.93 eV and BDC with a band gap of 3.76 eV. The electron effective mass was predicted from the band structure to be 8.9 me for all MOF designs.

Autonomous generation of single photon emitting materials.

The utilization of machine learning in Materials Science underscores the critical importance of the quality and quantity of data in training models effectively. Unlike fields such as image processing and natural language processing, there is limited availability of atomistic datasets, leading to biases in training data. Particularly in the domain of materials discovery, there exists an issue of continuity in atomistic datasets. Experimental data sourced from literature and patents is usually only available for favorable data, resulting in bias in the training dataset. This study focuses on developing a SMILES-based model for generating synthetic datasets of quantum materials using a variational autoencoder. This study centers on the generation of a synthetic dataset of quantum materials specifically for quantum sensing applications, with a focus on two-level quantum molecules that exhibit a dipole blockade. The proposed technique offers an improved sampling algorithm by incorporating newly generated data into the sampling algorithm to create a more normally distributed dataset. Through this technique, the study was able to generate over 1 000 000 candidate quantum materials from a small dataset of only 8000 materials. The generated dataset identified several iodine-containing molecules as promising single photon emitting materials for potential quantum sensing applications.

Autonomous design of new chemical reactions using a variational autoencoder.

Artificial intelligence based chemistry models are a promising method of exploring chemical reaction design spaces. However, training datasets based on experimental synthesis are typically reported only for the optimal synthesis reactions. This leads to an inherited bias in the model predictions. Therefore, robust datasets that span the entirety of the solution space are necessary to remove inherited bias and permit complete training of the space. In this study, an artificial intelligence model based on a Variational AutoEncoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions. This developed technique is demonstrated by generating over 7,000,000 new reactions from a training dataset containing only 7,000 reactions. The generated reactions include molecular species that are larger and more diverse than the training set.

Direct Measurement of the Selective Microwave-Induced Heating of Agglomerates of Dipolar Molecules: The Origin of and Parameters Controlling a Microwave Specific Superheating Effect.

Agglomerates of polar molecules in nonpolar solvents are selectively heated by microwave radiation. The magnitude of the selective heating was directly measured by using the temperature dependence of the intensities of the Stokes and anti-Stokes bands in the Raman spectra of p-nitroanisole (pNA) and mesitylene. Under dynamic heating conditions, a large apparent temperature difference (ΔT) of over 100 °C was observed between the polar pNA solute and the nonpolar mesitylene solvent. This represents the first direct measurement of the selective microwave heating process. The magnitude of the selective microwave heating was affected by the properties of the agglomerated pNA. As the concentration of the pNA increases, the magnitude of the selective heating of the pNA was observed to decrease. This is explained by the tendency of the pNA dipoles to orient in an antiparallel fashion in the aggregates as measured by the Kirkwood g value, which decreased with increasing concentration. This effect reduces the net dipole moment of the agglomerates, which decreases the microwave absorption. After the radiation was terminated, the effective temperature of the dipolar molecules returned slowly to that of the medium. The slow heat transfer was modeled successfully by treating the solutions as a biphasic solvent/solute system. Based on modeling and the fact that the agglomerate can be heated above the boiling temperature of the solvent, an insulating layer of solvent vapor is suggested to form around the heated agglomerate, slowing convective heat transfer out of the agglomerate. This is an effect unique to microwave heating.