<div>Accelerating the development of novel materials is one of the central goals of the Materials Genome Initiative and improving the predictive power of computational</div><div>material science methods is critical to attain this goal. Molecular dynamics (MD) is one such computational technique that has been used to study a wide range of materials since its invention in the 1950s. In this work we explore some examples of using and increasing the predictive power of MD simulations to understand materials phenomena and provide guidelines to design tailored materials. We first demonstrate the use of MD simulations as a tool to explore the design space of shape memory alloys, using simple interatomic models to identify characteristics of an integrated coherent second phase that will modify the transformation characteristics of the base shape memory alloy to our desire. Our approach provides guidelines to identify potential coherent phases that will achieve tailored transformation temperatures and hysteresis. </div><div><br></div><div>We subsequently explore ideas to enhance the length and time scales accessible via MD simulations. We first discuss the use of kinetic Monte Carlo methods in MD simulations to predict the microstructure evolution of carbon fibers. We ?find our approach to accurately predict the transverse microstructures of carbon fibers, additionally predicting the transverse modulus of these fibers, a quantity difficult to measure via experiments. Another avenue to increase length and time scales accessible via MD simulations is to explore novel implementations of algorithms involved in machine-learned interatomic models to extract performance portability. Our approach here results in significant speedups and an efficient utilization of increasingly common CPU-GPU hybrid architectures.</div><div><br></div><div>We finally explore the use of machine learning methods in molecular dynamics, specifically developing machine learning methods to discover interpretable laws directly from data. As examples, we demonstrate the discovery of integration schemes for MD simulations, and the discovery of melting laws for perovskites and single elements. Overall, this work attempts to illustrate how improving the predictive capabilities of molecular dynamics simulations and incorporating machine learning ideas can help us design novel materials, in line with the goals of the Materials Genome Initiative.</div>

Machine Learning has become more important for materials engineering in the last decade. Globally, automated machine learning (AutoML) is growing in popularity with the increasing demand for data analysis solutions. Yet, it is not frequently used for small tabular data. Comparisons and benchmarks already exist to assess the qualities of AutoML tools in general, but none of them elaborates on the surrounding conditions of materials engineers working with experimental data: small datasets with less than 1000 samples. This benchmark addresses these conditions and draws special attention to the overall competitiveness with manual data analysis. Four representative AutoML frameworks are used to evaluate twelve domain-specific datasets to provide orientation on the promises of AutoML in the field of materials engineering. Performance, robustness and usability are discussed in particular. The results lead to two main conclusions: First, AutoML is highly competitive with manual model optimization, even with little training time. Second, the data sampling for train and test data is of crucial importance for reliable results.

<p>The Materials Genome Initiative (MGI) has established guidelines to accelerate the discovery, development, and implementation of advanced materials in order to address current and future challenges. A key area of interest is the pressing need for more efficient energy storage systems to support technologies such as electric vehicles and renewable energies. In this work, we present an Integrated Computational Materials Engineering approach for the development of novel solid-state electrolyte materials. In particular, we embark on a quest to unravel the potential of ceramic garnet lithium lanthanum zirconium oxide (LLZO) for next-generation battery technologies.</p> <p>Our exploration begins with an overview of the current state of the Materials Innovation Infrastructure (MII) and our rationale behind choosing LLZO. Through the use of machine learning techniques and molecular dynamics simulations, we aim for efficient material optimization. Our findings are reinforced through experiments by using these materials as inorganic fillers in composite polymer electrolytes. Our findings demonstrate that the combined use of these complementary techniques facilitates the discovery of potential alternative solid-state electrolytes. Finally, we propose future research directions in materials science for the design of advanced materials using these integrated approaches. </p>

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Combinatorial techniques, which overcome limitations of actual models of material research permitting to effectively address this large amount of variables, are utilized in this work to prepare combinatorial libraries of the blend of the biodegradable polymers Poly(e-caprolactone) and Poly(lactic acid). These libraries present continuous composition and temperature gradients in an orthogonal fashion that permit to obtain multiple surface morphologies with controllable microstructures due to the blends low critical solution phase behavior (LCST). The goal of this study is to investigate the effect of surface morphology (surface chemical patterning and surface topography) on cell behavior. The varied surface topography of the libraries is used as a valuable tool that permits to assay the interaction between MC3T3-E1 cells and hundreds of different values of critical surface properties, namely, surface roughness and microstructure size. The outcome of this tool is a rapid screening of the effect of surface topography on cell behavior that is orders of magnitude faster than the standard 1-sample for 1 measurement techniques. The results obtained show that cells are very sensitive to surface topography, and that the final effect of surface properties on cell function is intimately related with the stage of the cell developmental process. Meaning that, for example, areas with optimal characteristics to elicit enhancement of cell attachment is not necessarily the same that promotes cell proliferation. This study imparts an improved understanding of an often neglected factor in biomaterials performance: surface morphology (particularly surface topography). The results provide a new insight into the importance of taking into consideration both chemistry and physical surface features for superior biomaterial design.

Molding is a simple manufacturing process whereby fluid fills a master tool and then solidifies in the shape of the tool cavity. The precise nature of material flow during molding has long allowed fabrication of plastic components with sizes 1 mm 1 m. Polymer molding with precise critical dimension control could enable scalable, inexpensive production of micro- and nanostructures for functional or lithographic use. This dissertation reports experiments and simulations on molding of polymer micro- and nanostructures at length scales 1 nm 1 mm. The research investigates two main areas: 1) mass transport during micromolding and 2) polymer mechanical properties during nanomolding at length scales 100 nm. Measurements and simulations of molding features of size 100 nm 1 mm show local mold geometry modulates location and rate of polymer shear and determines fill time. Dimensionless ratios of mold geometry, polymer thickness, and bulk material and process properties can predict flow by viscous or capillary forces, shape of polymer deformation, and mold fill time. Measurements and simulations of molding at length scales 100 nm show the importance of nanoscale physical processes distinct from bulk during mechanical processing. Continuum simulations of atomic force microscope nanoindentation accurately model sub-continuum polymer mechanical response but highlight the need for nanoscale material property measurements to accurately model deformation shape. The development of temperature-controlled nanoindentation enables characterization of nanoscale material properties. Nanoscale uniaxial compression and squeeze flow measurements of glassy and viscoelastic polymer show film thickness determines polymer entanglement with cooperative polymer motions distinct from those observed in bulk. This research allows predictive design of molding processes and highlights the importance of nanoscale mechanical properties that could aid understanding of polymer physics.

While I spent 7 years of study, experience and dedication to this work, the building construction has passed evolution from construction boom to contemporary building crisis. Demand for cheap building construction, materials and family housing increased sharply. Energy prices are rising every year... It seems that we will all have to deal with our essential task today or in the near future . This task is mean to prevent the current rate of degradation and destruction of our planet's climate and our environment. In this point of view, it looks the question of energy-saving construction very topical. By entering the study was to examine the possibilities of environmental friendly housing in rural areas in terms of environmental issues. My hypothesis was if can I determine the suitable candidate for the construction of passive houses themselves by comparing their characteristic of pre-defined construction samples. My objective is to offer builders and designers overview of suitable building systems with the possibility to compare the various factors influencing the decision on the selection of a builder´s construction for a house. The thesis presents the comparison of seven structural systems as a basic element of architecture. In the implementation of energy-efficient house is an architectural form often conditioned by structure. For this reason it is necessary to offer this kind of overview with options and parameters of individual building systems, which can then be used by architect to design a house for the builder - free and easy realization of his own, let´s say DIY (do it yourself). For builders (mean investors) of DIY houses is economy and finance a crucial question, therefore, for this reason I will evaluate suitable building system which is relative performance vs. price in the end. It is necessary to take into account the architecture of the house and especially the space layout and design and the attitude with the context of the rural areas environment. My pupose was to prove that good architectural design can be used with of low-cost, energy-saving and environmental friendly house built in DIY way.

In this project machine learning (ML) interatomic potentials are trained and used in molecular dynamics (MD) simulations to predict the physical properties of total energy, mean squared displacement (MSD) and specific heat capacity for systems of bulk Aluminium and Silicon. The interatomic potentials investigated are potentials trained using the ML models kernel ridge regression (KRR) and moment tensor potentials (MTPs). The simulations using these ML potentials are then compared with results obtained from ab-initio simulations using the gold standard method of density functional theory (DFT), as implemented in the Vienna ab-intio simulation package (VASP). The results show that the MTP simulations reach comparable accuracy compared to the DFT simulations for the properties total energy and MSD for Aluminium, with errors in the orders of magnitudes of meV and 10-5 Å2. Specific heat capacity is not reasonably replicated for Aluminium. The MTP simulations do not reasonably replicate the studied properties for the system of Silicon. The KRR models are implemented in the most direct way, and do not yield reasonably low errors even when trained on all available 10000 time steps of DFT training data. On the other hand, the MTPs require only to be trained on approximately 100 time steps to replicate the physical properties of Aluminium with accuracy comparable to DFT. After being trained on 100 time steps, the trained MTPs achieve mean absolute errors in the orders of magnitudes for the energy per atom and force magnitude predictions of 10-3 and 10-1 respectively for Aluminium, and 10-3 and 10-2 respectively for Silicon. At the same time, the MTP simulations require less core hours to simulate the same amount of time steps as the DFT simulations. In conclusion, MTPs could very likely play a role in accelerating both materials simulations themselves and subsequently the emergence of the data-driven materials design and informatics paradigm.