Using Machine Learning to Predict and Understand Complex Self-Assembly Behaviors of a Multicomponent Nanocomposite.

Blends of nanoparticles, polymers, and small molecules can self-assemble into optical, magnetic, and electronic devices with structure-dependent properties. However, the relationship between a multicomponent nanocomposite's formulation and its assembled structure is complex and cannot be predicted by theory. The blends can be strongly influenced by processing conditions, which can introduce non-equilibrium states. Currently, nanocomposite devices are designed through cycles of experimental trial and error. Machine learning (ML) methods are a compelling alternative because they can use existing datasets to map high-dimensional spaces. These methods do not rely on known relationships between parameters, so they are suited to complex systems without a solid theoretical foundation. Here, a dataset of 595 microscopy images of nanocomposite thin films is used to train a series of ML models. Correlations between the input and output parameters are examined, providing new insights into the system. Finally, the most successful ML model is used to predict the structures of new nanocomposite compositions. The results confirm that ML techniques can be used to improve the efficiency of nanocomposite device design. More broadly, the current study suggests some of the advantages and challenges associated with applying ML to complex systems.

Hydroxylation of the surface of PbS nanocrystals passivated with oleic acid.

Controlling the structure of colloidal nanocrystals (NCs) is key to the generation of their complex functionality. This requires an understanding of the NC surface at the atomic level. The structure of colloidal PbS NCs passivated with oleic acid has been studied theoretically and experimentally. We show the existence of surface OH(-) groups, which play a key role in stabilizing the PbS(111) facets, consistent with x-ray photoelectron spectroscopy as well as other spectroscopic and chemical experiments. The role of water in the synthesis process is also revealed. Our model, along with existing observations of NC surface termination and passivation by ligands, helps to explain and predict the properties of NCs and their assemblies.

Mechanical and electrical properties of CdTe tetrapods studied by atomic force microscopy.

The mechanical and electrical properties of CdTe tetrapod-shaped nanocrystals have been studied with atomic force microscopy. Tapping mode images of tetrapods deposited on silicon wafers revealed that they contact the surface with three of its arms. The length of these arms was found to be 130+/-10 nm. A large fraction of the tetrapods had a shortened vertical arm as a result of fracture during sample preparation. Fracture also occurs when the applied load is a few nanonewtons. Compression experiments with the atomic force microscope tip indicate that tetrapods with the shortened vertical arm deform elastically when the applied force was less than 50 nN. Above 90 nN additional fracture events occurred that further shortened the vertical arm. Loads above 130 nN produced irreversible damage to the other arms as well. Current-voltage characteristics of tetrapods deposited on gold revealed a semiconducting behavior with a current gap of approximately 2 eV at low loads (<50 nN) and a narrowing to about 1 eV at loads between 60 and 110 nN. Atomistic force field calculations of the deformation suggest that the ends of the tetrapod arms are stuck during compression so that the deformations are due to bending modes. Empirical pseudopotential calculation of the electron states indicates that the reduction of the current gap is due to electrostatic effects, rather than strain deformation effects inside the tetrapod.

The use of nanocrystals in biological detection.

In the coming decade, the ability to sense and detect the state of biological systems and living organisms optically, electrically and magnetically will be radically transformed by developments in materials physics and chemistry. The emerging ability to control the patterns of matter on the nanometer length scale can be expected to lead to entirely new types of biological sensors. These new systems will be capable of sensing at the single-molecule level in living cells, and capable of parallel integration for detection of multiple signals, enabling a diversity of simultaneous experiments, as well as better crosschecks and controls.

Synthesis and characterization of hyperbranched mesoporous silica SBA-15.

Branched mesoporous silica SBA-15 materials have been prepared in a simple process using non-ionic surfactant in acidic conditions in the presence of metal ions.