Optimal experimental design for materials discovery

The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design. However, the large size of the search space containing the candidates and the substantial computational cost of high-fidelity property prediction models makes screening practically challenging. In this work, we propose a general framework for constructing and optimizing a virtual screening (HTVS) pipeline that consists of multi-fidelity models. The central idea is to optimally allocate the computational resources to models with varying costs and accuracy to optimize the return-on-computational-investment (ROCI). Based on both simulated as well as real data, we demonstrate that the proposed optimal HTVS framework can significantly accelerate screening virtually without any degradation in terms of accuracy. Furthermore, it enables an adaptive operational strategy for HTVS, where one can trade accuracy for efficiency.

BackgroundAn accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first.ResultsThe authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks.ConclusionsSimulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/.

Design has become a salient part of most of the scientific and engineering tasks, embracing a wide range of domains including real experimental settings (e.g., material discovery or drug design), simulation-based design, and hyperparameter tuning. Model-based experimental design refers to a broad class of techniques, applicable to domains that a partial knowledge about the underlying process exists. Unlike entropy- based techniques which aim to reduce the whole uncertainty in the process, the mean objective cost of uncertainty (MOCU) is a rigorous statistically-oriented experimental design framework which takes the main objective into account during the decision making. However, the lack of scalability of this framework has restricted its application to domains with very small design spaces. This paper proposes a framework using the combination of Bayesian optimization and MOCU policy, which enables experimental design to much larger design spaces and systems. The reliability, scalability and efficiency of the proposed framework are investigated through experimental design for optimal structural intervention in gene regulatory networks.

Event-sampling method with experimental design: A promising method for investigating microfoundational phenomena within family businesses

Design of experiments for identification of control‐relevant models is at the heart of robust controller design. In a number of prior publications, experiment designs have been developed that generate input/output data for efficient identification of models satisfying the integral controllability (IC) condition. The design of process inputs for such experiments is often, but not always, based on the concept of independent random rotated inputs, with appropriately proportioned amplitudes. However, prior publications do not account for models that may already be partially known before identification. In this work, this issue is addressed by developing a general experiment design framework for efficient identification of partially known models that must satisfy the IC condition. This framework produces optimal designs by solving appropriately formulated optimization problems, based on a number of rigorous theoretical results. Numerical simulations illustrate the proposed approach and potential future extensions are suggested. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2986–3001, 2016

OptEx: An integrated framework for experimental design and combustion kinetic model optimization

Experimental design method to the weld bead geometry optimization for hybrid laser-MAG welding in a narrow chamfer configuration

The transport properties in the mixed state of high-quality Ca0.8La0.2Fe0.98Co0.02As2 single crystal, a newly discovered 112-type iron pnictide superconductor, are comprehensively studied by magneto-resistivity measurement. The field-dependent activation energy, U0, is derived in the framework of thermally activated flux flow (TAFF) theory, yielding a power law dependence U0~Hα with a crossover at a magnetic field around 2 T in both H⊥ab and H//ab, which is ascribed to the different pinning mechanisms. Moreover, we have clearly observed the vortex phase transition from vortex-glass to vortex-liquid according to the vortex-glass model, and vortex phase diagrams are constructed for both H⊥ab and H//ab. Finally, the results of mixed-state Hall effect show that no sign reversal of transverse resistivity ρxy(H) is detected, indicating that the Hall component arising from the vortex flow is also negative based on the time dependent Ginzburg-Landau (TDGL) theory. Meanwhile, the transverse resistivity ρxy(H) and the longitudinal resistivity ρxx(H) follow the relation |ρxy(H)|=Aρxx(H)β well with an exponent β~2.0, which is in line with the results in theories or experiments previously reported on some high-Tc cuprates.

In this paper we focus on the comparison of the equilibrium and transport properties of singular and nonsingular vortex structures in high-temperature compounds. Using the time-dependent Ginzburg–Landau theory we study the dynamics of vortex structures in superconductors with (d+s)-wave pairing. We calculate the angular dependent correction to the viscosity tensor of a singular flux line, which appears due to the admixture of subdominant s-wave order parameter component in the vortex core. The second order phase transition between singular and nonsingular vortices in high-temperature superconductors was simulated within the time-dependent Ginzburg-Landau equations. The semiclassical analysis of the positions of gap nodes in the vortex core is performed both for singular and nonsingular flux lines.

Rapid development of the enantiomeric separation of β-blockers by capillary electrophoresis using an experimental design approach

This work is focused on performing a quantitative assessment of the environmental impacts associated with an organic synthesis reaction, optimized using an experimental design approach. A nucleophilic substitution reaction was selected, employing vanillin as the substrate, a phenolic compound widely used in the food industry and of pharmaceutical interest, considering its antioxidant and antitumoral potential. To carry out the reaction, three different solvents have been chosen, namely acetonitrile (ACN), acetone (Ace), and dimethylformamide (DMF). The syntheses were planned with the aid of a multivariate experimental design to estimate the best reaction conditions, which simultaneously allow a high product yield and a reduced environmental impact as computed by Life Cycle Assessment (LCA) methodology. The experimental results highlighted that the reactions carried out in DMF resulted in higher yields with respect to ACN and Ace; these reactions were also the ones with lower environmental impacts. The multilinear regression models allowed us to identify the optimal experimental conditions able to guarantee the highest reaction yields and lowest environmental impacts for the studied reaction. The identified optimal experimental conditions were also validated by experimentally conducting the reaction in those conditions, which indeed led to the highest yield (i.e., 93%) and the lowest environmental impacts among the performed experiments. This work proposes, for the first time, an integrated approach of DoE and LCA applied to an organic reaction with the aim of considering both conventional metrics, such as reaction yield, and unconventional ones, such as environmental impacts, during its lab-scale optimization.

Organic electrochemical transistors (OECTs) are widely employed in several bioelectronic applications such as biosensors, logic circuits, and neuromorphic engineering, providing a seamless link between the realm of biology and electronics. More specifically, OECTs are endowed with remarkable signal amplification, the ability to operate in an aqueous environment, and the effective transduction of ionic to electrical signals. One main limiting factor preventing OECTs’ wide use is the need for microfabrication processes, typically requiring specialized equipment. From this perspective, a robust and cost-effective production protocol to achieve high-performing OECT would be desirable. Herein, a straightforward stencil-printed OECT fabrication procedure is proposed, where the electrical performance can be controlled by adjusting the electronic channel fabrication conditions. An experimental design approach is undertaken to optimize OECT figures of merit by varying key parameters such as the annealing temperature and time, as well as the transistor active channel length. The resulting OECT devices, fabricated through a high-yield, cost-effective, and fast stencil printing technique, feature large transconductance values at low operating voltages. The experimental design allowed for minimizing the threshold voltage (VT = 260 mV) while keeping a high on/off ratio (7 × 103). A signal-to-noise ratio as high as 40 dB was obtained, which is among the highest for OECTs, operating in an aqueous electrolyte operated in a DC mode. An atomic force microscopy (AFM) characterization has been undertaken to analyze the channel morphology in the OECTs, correlating the annealing conditions with the charge transport properties.

This study covers one of the largest and oldest oil fields in Oman. Despite over 35 years of production through various drive mechanisms, there remains an opportunity to realize additional value in the field.The scale and complexity of the uncertainties associated with such opportunity necessitates the use of experimental design and response surface methods to provide a framework for evaluating sensitivities and estimating impact of uncertainties on field performance. Since data sometimes are incomplete or inconsistent and measurements are ambiguous or inconclusive, the representation of reservoir and geological parameters are often sub optimal. This is especially true for heterogeneous fractured reservoir, such as this field, where the number and range of uncertainties is large.This paper presents the application of experimental design and response surface methods to the field development study of this field. The methodology uses an integrated approach, where cross discipline data is evaluated early in the study to identify and quantify uncertainties with the highest impact. After static and dynamic data are reviewed and quality checked, an initial ranking of uncertainties takes place. A selection of 3D simulation models, as defined by the experimental design approach, are run to construct response surface models, which are then used to assess the impact of these uncertainties on field behavior. The historical performance of the field is critical data used to constrain the range of some uncertainties. The results of this study are used to guide future development of the field.The methodology described in this paper highlights the value of a structured multiple realization approach, using experimental design and response surface modeling, for uncertainty estimation and management.

Achieving optimal magnetic flux expulsion of a Nb3Sn superconducting radio-frequency cavity via spatial temperature gradient

Within the time-dependent Ginzburg-Landau (TDGL) theory we studied the creation of phase-slip lines and the interplay with a vortex lattice in a finite-length superconducting thin stripe with finite-size normal metal leads. In zero magnetic field and with increasing transport current phase-slip lines appear across the sample leading to distinct jumps in the current-voltage characteristics. When a magnetic field is applied, the moving vortex lattice becomes rearranged by the external current and fast and slow moving vortex channels are formed. Curved vortex channels are observed near the normal contacts. We found the remarkable result that at small applied magnetic field the normal-state transition current is increased as compared to the one at zero magnetic field. This effect is more pronounced for larger values of the $\ensuremath{\gamma}$ parameter in the TDGL formalism. This unusual ``field-induced'' increase in the critical current is a consequence of the nonuniform distribution of the current in the sample.