We describe how the development of advanced materials via high-throughput experimentation at Intermolecular® is accelerated using guidance from modelling, machine learning (ML) and other data-driven approaches. Focusing on rapid development of materials for the semiconductor industry at a reasonable cost, we review the strengths and the limitations of data-driven methods. ML applied to the experimental data accelerates the development of record-breaking materials, but needs a supply of physically meaningful descriptors to succeed in a practical setting. Theoretical materials design greatly benefits from the external modelling ecosystems that have arisen over the last decade, enabling a rapid theoretical screening of materials, including additional material layers introduced to improve the performance of the material stack as a whole, “dopants” to stabilize a given phase of a polymorphic material, etc. We discuss the relative importance of different approaches, and note that the success rates for seemingly similar problems can be drastically different. We then discuss the methods that assist experimentation by providing better phase identification. Finally, we compare the strengths of different approaches, using as an example the problem of identifying regions of thermodynamic stability in multicomponent systems.

Neutron scattering is one of the most useful methods of studying the structure of matter, with applications to biomedical, structural, magnetic and energy-related materials. Neutron-scattering instruments are installed around research reactors or accelerator-based neutron sources, and neutron guides are critical components of these facilities. They are neutron-transport optical devices consisting of state-of-the-art mirrors often tens of meters long. Here we demonstrate a novel fabrication method of all-metallic neutron guides and axisymmetric mirrors by electroplating from precision mandrels. The process allows for the fabrication of single-piece all-metal guides of prismatic and axisymmetric shapes. We also demonstrate supermirror guides and axisymmetric focusing supermirrors produced with the same technology. We present the fabrication and tests of the multilayer-coated replicated guides and optic and show that the mandrel is reproduced with high fidelity and reliability. Such supermirror optics will provide game-changing improvements in neutron techniques.

Melting, solidification and solid-state transformation of the intermetallic Ni3Sn compound were investigated in situ using synchrotron high-energy X-ray diffraction. It was observed that the compound undergoes a hexagonal to cubic transition before melting. In solidification, a disordered cubic phase crystallizes from the liquid at a large undercooling but it is reordered prior to bulk solidification. In melting and solidification, forced or natural flows are active bringing about significant changes of crystal orientations. These in situ observations provided insights into phase transformations of Ni3Sn at elevated temperatures and their roles in formation of metastable microstructure consisting of coarse grains and subgrains.

Machine learning-based approach is desired for accelerating materials design, development and discovery in combination with high-throughput experiments and simulation. In this work, we propose to apply a Bayesian optimization method to design ultrathin multilayer tungsten-silicon carbide (W-SiC) nanocomposite absorber for high-temperature solar power generation. Based on a semi-analytical scattering matrix method, the design of spectrally selective absorber is optimized over a variety of layer thicknesses to maximize the overall solar absorptance. Our nanofabrication and experimental characterization results demonstrate the capability of the proposed approach for accelerated development of refractory light-absorbing materials. Comparison with other global optimization methods, such as random search, simulated annealing and particle swarm optimization, shows that the Bayesian optimization method can expedite the design of multilayer nanocomposite absorbers and significantly reduce the development cost. This work sheds light on the discovery of novel materials for solar energy and sustainability applications.

We present the current status of a scalable computing framework to address the need of the multidisciplinary effort to study chemical dynamics. Specifically, we are enabling scientists to process and store experimental data, run large-scale computationally expensive high-fidelity physical simulations, and analyze these results using state-of-the-art data analytics, machine learning, and uncertainty quantification methods using heterogeneous computing resources. We present the results of this framework on a single metadata-driven workflow to accelerate an additive manufacturing use-case.

Pairs of conjugate donor-acceptor fluorescent probes have proven themselves useful in stimulated emission depletion (STED) microscopy in recent years. For instance, it has been shown that the lifetime of said probes directly correlates to the resolution of the microscope. However, once the lifetimes of the probes have been optimized, it is desirable to control their fluorescence in order to improve the resolution further. Here, we propose combining model-free control with sliding mode control to track nanosecond pulses of red-shifted acceptor fluorescence in order to inhibit visible light emitted from the image plane, shrink the point spread function, and subsequently improve the resolution of the microscope. This is achieved by automatic adjustment of the STED laser beam pump power. This controller is numerically simulated against a generic model created from Förster resonance energy transfer (FRET) theory. However, since it is data-driven, it can be easily applied to various physical systems with drastically different dynamics. This work provides a reliable theoretic control solution to modern super resolution microscopy for biological imaging.

Thermosetting resins are one of the most widely used functional materials in industrial applications. Although some of the physical properties of thermosetting resins are controlled by changing the functional groups of the raw materials or adjusting their mixing ratios, it was conventionally challenging to construct machine learning (ML) models, which include both mixing ratio and chemical information such as functional groups. To overcome this problem, we propose a machine learning approach based on extended circular fingerprint (ECFP) in this study. First, we predicted the classification of raw materials by the random forest, where ECFP was used as the explanatory variable. Then, we aggregated ECFP for each classification predicted by the random forest. After that, we constructed the prediction model by using the aggregated ECFP, feature quantities of reaction intermediates, and curing conditions of resin as explanatory variables. As a result, the model was able to predict in high accuracy (R^2 = 0.8), for example, the elastic modulus of thermosetting resins. Furthermore, we also show the result of verification of prediction accuracy in first step, such as using the one-hot-encording. Therefore, we confirmed that the properties of thermosetting resins could be predicted using mixed raw materials by the proposed method.

Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.

In situ X-ray diffraction is one of the most useful tools for studying a variety of processes, among which crystallization of nanoparticles where phase purity and size control are desired. Growth kinetics of a single phase can be completely resolved by proper analysis of the diffraction peaks as a function of time. The peak width provides a parameter for monitoring the time evolution of the particle size distribution (PSD), while the peak area (integrated intensity) is directly related to the whole diffracting volume of crystallized material in the sample. However, to precisely describe the growth kinetics in terms of nucleation and coarsening, the correlation between PSD parameters and diffraction peak widths has to be established in each particular study. Corrections in integrated intensity values for physical phenomena such as variation in atomic thermal vibrations and dynamical diffraction effects have also to be considered in certain cases. In this work, a general correlation between PSD median value and diffraction peak width is deduced, and a systematic procedure to resolve time-dependent lognormal PSDs from in situ XRD experiments is described in details. A procedure to correct the integrated intensities for dynamical diffraction effects is proposed. As a practical demonstration, this analytical procedure has been applied to the single-phase crystallization process of bismuth ferrite nanoparticles.

Selective laser sintering methods are workhorses for additively manufacturing polymer-based components. The ease of rapid prototyping also means it is easy to produce illicit components. It is necessary to have a data-calibrated in-situ physical model of the build process in order to predict expected and defective microstructure characteristics that inform component provenance. Toward this end, sintering models are calibrated and characteristics such as component defects are explored. This is accomplished by assimilating multiple data streams, imaging analysis, and computational model predictions in an adaptive Bayesian parameter estimation algorithm. From these data sources, along with a phase-field model, bulk porosity distributions are inferred. Model parameters are constrained to physically-relevant search directions by sensitivity analysis, and then matched to predictions using adaptive sampling. Using this feedback loop, data-constrained estimates of sintering model parameters along with uncertainty bounds are obtained.

We describe experimental approaches to real time examination of the microstructural evolution of Ti 6%Al 4%V upon cooling from above the beta transus (~995 °C) while imaging in the scanning electron microscope. Ti 6%Al 4%V is a two phase, α+β titanium alloy with high strength and corrosion resistance. The β →α transformation on cooling can give rise to different microstructures and properties through various thermal treatments. Fully lamellar microstructures, bi-modal microstructures, and equiaxed microstructures can each be obtained by accessing different cooling rates upon the final treatment above the beta temperature, each resulting in uniquely enhanced material properties.

Utilizing the capabilities of a heating/ tensile stage developed by Kammrath & Weiss Inc., are able to apply real-time imaging techniques in the scanning electron microscope to monitor the development of the microstructure. Annealing temperatures up to 1100 °C are attainable, with cooling rates ranging from 0.1 ° C per second to 3.3 °C per second. This has allowed us to directly observe the formation of lamellae at different annealing temperature/ cooling rate combinations to determine the lamellar microstructure width, separation, and colony size.

Observation of the dynamic interaction between dislocations and carbon atoms is important in steel design. Some steel materials are bake-hardened in several manufacturing processes. Solute carbons are known to segregate on dislocations; this hardens the steel even after low-temperature treatments. The purpose of this study was to develop a method of monitoring a series of microstructural changes in strain aging by in-situ measurement of the electrical resistance in low-carbon steel. In tensile deformation, elastic, Lüders, and uniform plastic deformations could be distinguished by monitoring the changes in electrical resistance. Electrical resistance rapidly increased in the plastic deformation region in the strain-aged specimen. Although the deformation stress hardly changed, the amount of lattice defects monotonously increased. These analyses provide useful information in steel design related to thermomechanical treatments of bake-hardenable steel.