AmorProt: Amino Acid Molecular Fingerprints Repurposing based Protein Fingerprint

As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data driven predictions without the need for expensive experiments. Nevertheless, unlike the various molecular fingerprint algorithms that have been developed, protein fingerprint algorithms have rarely been studied. In this study, we proposed the amino acid molecular fingerprints repurposing based protein (AmorProt) fingerprint, a protein sequence representation method that effectively uses the molecular fingerprints corresponding to 20 amino acids. Subsequently, the performances of the tree based machine learning and artificial neural network models were compared using (1) amyloid classification and (2) isoelectric point regression. Finally, the applicability and advantages of the developed platform were demonstrated through a case study and the following experiments: (3) comparison of dataset dependence with feature based methods; (4) feature importance analysis; and (5) protein space analysis. Consequently, the significantly improved model performance and data set independent versatility of the AmorProt fingerprint were verified. The results revealed that the current protein representation method can be applied to various fields related to proteins, such as predicting their fundamental properties or interaction with ligands

Quantum and atomistic simulation of mechanical and electronic properties of nano-materials

In our modern scientific society, many new nano-materials are emerging that exhibit outstanding properties, and researchers claim that they have great potential to replace conventional mechanical and electronic devices. It is important to provide comprehensive understanding of these new materials to determine their utility and limitations. State-of-the-art computational schemes (from quantum to atomistic simulation methods) make it possible to investigate nano-materials with respect to their mechanical, electrical, thermal, vibrational, and other properties and characteristics. In this work, we used quantum to atomistic simulations methods such as density functional theory (DFT), molecular dynamics (MD) simulations, and semi-empirical tight binding (TB) method to investigate the mechanical and electrical properties of carbon based materials (graphene, fullerenes) and molybdenum disulfide (MoS2). The first material we tested was graphene. Graphene has attracted significant attention for the past ten years because of its extraordinary mechanical and electrical properties. The attractive properties of graphene are currently being explored for a number of applications including nanoelectromechanical systems and nanoelectronics. Mechanical experiments have shown that graphene is the strongest material measured so far, and this opens up opportunities for graphene as a great nanomechanical material. In our work, we examined the mechanical properties of graphene under tensile and shear deformation. Using TB and MD simulations, we computed the modulus of elasticity, fracture strength, and shear fracture strain of zigzag and armchair graphene structures at various temperatures. We also compared the result of two different methods to test tensile deformation, and results were consistent across both methods. To predict shear strength and fracture shear strain, we also present an analytical theory based on the kinetic analysis. We show that wrinkling behavior of graphene under shear deformation can be significant. We compute the amplitude to wavelength ratio of wrinkles using molecular dynamics and compare it with existing theory. Our results indicate that graphene can be a promising mechanical material under shear deformation. Our second class of materials tested was fullerene. The encapsulation of a single water molecule inside C60 opens up new opportunities for fabrication of novel electronic systems, such as memories, molecular motors, and mechanical nano-resonators. Specifically, the mechanical and electronic properties change of fullerene structures due to water encapsulation can provide fundamental insights for their applications. We chose to examine the mechanical properties of H2O(n)@C60 under hydrostatic strain and point load using DFT. For mechanical tests, both tension and compression were performed. We found that the bulk modulus and elastic modulus increase as the number of water molecules increase. For fracture behavior, two mechanisms were observed: First, under compression, due to the interaction and bond formation between water and C60, structures with more water molecules begin to exhibit fracture at a lower strain. Second, under tension, fracture is initiated from the bond dissociation of C-C bonds on the C60 surface. We also report on the electronic properties of water encapsulated fullerenes (H2O(n)@C60, H2O(n)@C180, and H2O(n)@C240) under mechanical deformation using density functional theory (DFT). Under a point load, the change in energy gap of empty and water-filled fullerenes is investigated. For C60 and H2O(n)@C60, the energy gap decreases as the tensile strain increases. For H2O(n)@C60, under compression, the energy gap decreases monotonously while for C60, it first decreases and then increases. Similar behavior is also observed for other empty (C180 and C240) and water-filled (H2O(n)@C180, H2O(n)@C240) fullerene structures. The decrease in energy gap of water-filled fullerenes is due to the increased interaction between water and the carbon wall under deformation. Finally, we explored the electronic properties of MoS2 when adsorbed on amorphous HfO2 using density functional theory. A single-layer MoS2, which is a semiconducting material with a direct band gap of 1.8 eV, has garnered a lot of attention because it has been shown that the electron mobility of MoS2 at room temperature is comparable to graphene nano-ribbons. This finding has led to multiple attempts to investigate the mechanical and electronic properties of MoS2 under various conditions. In this study, we examine the band gap modulation in MoS2. The defective sites on both MoS2 and HfO2 are considered- Mo-, S-, and O-vacancy. O-vacancy in HfO2 increases interaction between two substrates, and reduces the band gap significantly. It also induces the n-type doping effect on MoS2. Defects on MoS2 (Mo and S) introduce the finite states in the middle of band gap. In addition, the application of an electric field significantly affects the band gap depending on the direction of the field

Capacitive Sensing of Intercalated H2O Molecules Using Graphene

Understanding the interactions of ambient molecules with graphene and adjacent dielectrics is of fundamental importance for a range of graphene-based devices, particularly sensors, where such interactions could influence the operation of the device. It is well-known that water can be trapped underneath graphene and its host substrate, however, the electrical effect of water beneath graphene and the dynamics of how it changes with different ambient conditions has not been quantified. Here, using a metal-oxide-graphene variable-capacitor (varactor) structure, we show that graphene can be used to capacitively sense the intercalation of water between graphene and HfO2 and that this process is reversible on a fast time scale. Atomic force microscopy is used to confirm the intercalation and quantify the displacement of graphene as a function of humidity. Density functional theory simulations are used to quantify the displacement of graphene induced by intercalated water and also explain the observed Dirac point shifts as being due to the combined effect of water and oxygen on the carrier concentration in the graphene. Finally, molecular dynamics simulations indicate that a likely mechanism for the intercalation involves adsorption and lateral diffusion of water molecules beneath the graphene.Comment: E.J.O. and R.M. made an equal contribution to this wor

Hybrid modeling approach for terpolymerization reactions in CSTR

In order to produce terpolymer of desired quality a model capable of simulating terpolymerization is required. Terpolymerization involves complex reactions, full-scale modeling using the frst principle model is not practical to simulate reaction because there are many parameters to be estimated. In this study, a hybrid model that integrates the frst-principles model and the DNN model is proposed. The proposed hybrid model reduces the parameters that need to be estimated using a cumulative composition model, through the steady-state assumption. Afterward, DNN model in a hybrid model estimates the conversion using measurement data from process sensors, and the terpolymer composition according to conversion is calculated. In the process, by estimating model parameters with error in variables model, hybrid model specifc to the system is constructed. Validation of the hybrid model is performed using measurement data of 600 days and the result shows a good agreement with the actual data. The proposed hybrid model has high fdelity, scalability and robustness to other terpolymerization process. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license(https://creativecommons.org/licenses/by-nc-nd/4.0/)N

Predicting mechanical properties of newly generated two-dimensional materials with minimum uncertainty

Two-dimensional (2D) materials exhibit exceptional properties. Thus, many studies have been conducted to discover novel 2D materials with unique characteristics or to find new ways of utilizing existing 2D materials. However, the existing open databases of 2D materials are often inefficient for this purpose. In this study, a material discovery framework is developed to identify new 2D materials using a deep learning-based generative model. First, a previous 2D database is adopted as a training set to develop a machine learning-based surrogate model for predicting the mechanical properties. Next, 2D candidates are generated, and their structural validity is confirmed by employing a classification model and checking their similarities to existing 2D materials. The uncertainty in the predicted mechanical properties of the generated materials is measured and the actual values are verified using density functional theory calculations. A total of 360 structures are newly identified according to the exploration method and the mean absolute error is significantly reduced from 206.025 to 10.185 N/m. We believe that the developed framework is general and can be further modified to search for novel 2D materials satisfying target physicochemical properties

Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning

Li-ion solid-state electrolytes (Li-SSEs) hold promise to solve critical issues related to conventional Li-ion batteries (LIBs), such as the flammability of liquid electrolytes and dendrite growth. In this study, we develop a platform involving a high-throughput screening process and machine learning surrogate model for identifying superionic Li-SSEs among 19,480 Li-containing materials. Li-SSE candidates are selected based on the screening criteria, and their ionic conductivities are predicted. For the training database, the ionic conductivities and crystal systems of various inorganic SSEs, such as Na SuperIonic CONductor (NASICON), argyrodite, and halide, are obtained from previous literature. Subsequently, a chemical descriptor (CD), crystal system, and number of atoms are used as machine-readable features. To reduce the uncertainty in the surrogate model, the ensemble method, which considers the two best-performing models, is employed; the mean prediction accuracies are found to be 0.887 and 0.886, respectively. Furthermore, first-principles calculations are conducted to confirm the ionic conductivities of the strong candidates. Finally, three potential superionic Li-SSEs that have not been previously investigated are proposed. We believe that the platform constructed and explored in this work can accelerate the search for Li-SSEs with satisfactory performance at a minimum cost

A two-way coupled CFD-DQMOM approach for long-term dynamic simulation of a fluidized bed reactor

For the long-term dynamic simulation of a fluidized bed reactor (FBR), a two-way coupled computational fluid dynamics (CFD)-direct quadrature method of moments (DQMOM) approach is proposed. In this approach, CFD is first used only for hydrodynamic information without simulating any other chemical reactions or physical phenomena. Subsequently, the derived information is applied to the DQMOM calculation in MATLAB. From the calculation, a particle size distribution is obtained and subsequently adopted in a new CFD model to reflect the flow change caused by a change in the particle size distribution. Through several iterative calculations, long-term dynamic simulations are performed. To evaluate the efficacy of the proposed approach, the results from the suggested approach are compared for 60 s with those of the CFD-quadrature method of moments (QMOM) approach, which calculates hydrodynamics and physical phenomena simultaneously in CFD. The proposed approach successfully simulated the FBR for 6 h. The results confirmed that the proposed method can simulate complex flow patterns, which cannot be obtained in conventional CFD models. Another advantage of the approach is that it can be applied to various industrial multiphase reactors without any tuning parameters.N

Correlating atomistic characteristics of zeolites to their 3D-Printed Macro structural properties for prediction of mechanical response

A wide range of mechanical properties are vital in structures, from macro (e.g., load-bearing) down to atomistic (nanomaterial) level. To design structures with the target mechanical properties, it is crucial to understand the correlation between the mechanical characteristics and structural information. To this end, we explored the similarity in the mechanical behavior between atomistic structures and actual 3D-printed zeolite structures. The zeolite structure was chosen because of its various structural parameters such as pore size, distribution, and geometry. Molecular dynamics (MD) simulations confirmed that similar behavior was observed in the mechanical responses at an atomic scale and with a 3D-printed macro-scale structure. 3D printing with ductile thermoplastic polyurethane (TPU) filaments showed a high degree of agreement with microstructure-level simulations. The mechanical response of zeolite structures is classified depending on their linearity and the characteristics with respect to the applied strain, to anticipate the potential applications of mechanical metamaterials. Further comparative analysis was conducted between the structural characteristics and mechanical properties, linking the changes in the stress to factors, such as density, porosity, angle, and bond length. This study demonstrates that metamaterial design with a mechanical response can be achieved using atomic-level structural design degrees of freedom