Developing a neural network machine learning interatomic potential for molecular dynamics simulations of La-Si-P systems.

Road vehicles, particularly cars, are one of the primary sources of CO2 emissions in the transport sector. Shifting to unconventional energy sources such as solar and wind power may reduce their carbon footprints considerably. Consequently, using ammonia as a fuel due to its potential benefits, such as its high energy density, being a carbon-free fuel, and its versatility during storage and transportation, has now grabbed the attention of researchers. However, its slow combustion speed, larger combustion chamber requirements, ignition difficulties, and limited combustion stability are still major challenges. Therefore, authors tried to analyze the combustion pressure of ammonia in a constant-volume combustion chamber across different equivalence ratios by adopting a machine learning approach. While conducting the analysis, the experimental values were assessed and subsequently utilized to predict the induced combustion pressure in a constant-volume combustion chamber across various equivalence ratios. In this research, a two-step prediction process was employed. In the initial step, the Random Forest algorithm was applied to assess the combustion pressure. Subsequently, in the second step, artificial neural network machine learning algorithms were employed to pinpoint the most effective algorithm with a lower root-mean-square error and R2. Finally, Linear Regression illustrated the lowest error in both steps with a value of 1.0, followed by Random Forest.

Random forest, extreme gradient boosting and artificial neural network machine learning methods were used to predict the content of myristic acid (C14:0) and conjugated linoleic acid (CLA) based on the milk fatty acids (FA) obtained through Fourier transform infrared spectroscopy. The methods had similar prediction performance for C14:0 (mean average percentage error < 7%). The low prediction accuracy for CLA was probably due to the poor association between the CLA and the input variables. Therefore, machine learning can be used to predict C14:0 and other saturated FA fromed from similar metabolic pathways

Osteoporosis is a prevalent but asymptomatic condition that affects a large population of the elderly, resulting in a high risk of fracture. Several methods have been developed and are available in general hospitals to indirectly assess the bone quality in terms of mineral material level and porosity. In this paper we describe a new method that uses a medical reflex hammer to exert testing stimuli, an electronic stethoscope to acquire impulse responses from tibia, and intelligent signal processing based on artificial neural network machine learning to determine the likelihood of osteoporosis. The proposed method makes decisions from the key components found in the time-frequency domain of impulse responses. Using two common pieces of clinical apparatus, this method might be suitable for the large population screening tests for the early diagnosis of osteoporosis, thus avoiding secondary complications. Following some discussions of the mechanism and procedure, this paper details the techniques of impulse response acquisition using a stethoscope and the subsequent signal processing and statistical machine learning algorithms for decision making. Pilot testing results achieved over 80% in detection sensitivity.

The applications of artificial intelligence in education became a very attractive topic especially during the COVID-19 pandemic due to the high level of uncertainty surrounded the decision making process within the educational institutions. The objective of this study is to create a model that is able to predict the student's score in the SAT test based on the student's performance in the internal assessments of the school and other demographic attributes. The sample includes 37 students of both genders from a private school in the United Arab Emirates (UAE). The findings suggest that it is possible to implement artificial neural networks to estimate the student's performance in the SAT exam based on internal school data. The model accuracy is 87.4 % however, some attributes can be identified as noise data and can be further removed to increase the accuracy. Scholastic Assessment Test Artificial Neural Network Machine learning Students performance.

Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to develop an optimized version of an Artificial Neural Network machine learning method for classifying blazar candidates of uncertain type detected by the Fermi Large Area Telescope γ-ray instrument. The final result of this study increased the classification performance by about 80 ${{\ \rm per\ cent}}$ with respect to previous method, leaving only 15 unclassified blazars out of 573 blazar candidates of uncertain type listed in the LAT 4-year Source Catalog.

Comparison of different interatomic potentials for MD simulations of AlN

Lung cancer is a kind of difficult to diagnose and dangerous cancer. It commonly causes death both men and women so fast accurate analysis of nodules is more important for treatment. Various methods have been used for detecting cancer in early stages. In this paper, machine learning methods compared while detect lung cancer nodule. We applied Principal Component Analysis, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Decision Trees and Artificial Neural Networks machine learning methods to detect anomaly. We compared all methods both after preprocessing and without preprocessing. The experimental results show that Artificial Neural Networks gives the best result with 82,43% accuracy after image processing and Decision Tree gives the best result with 93,24% accuracy without image processing.

Molecular dynamic simulations of the martensitic transformation for the dual-phase structure and dislocation activities in Ti80 alloys

In modern computational materials, machine learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional molecular dynamics (MD) simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, efficient models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. Here, we introduce an efficient equivariant graph neural network (E2GNN) that can enable accurate and efficient interatomic potential and force predictions for molecules and crystals. Rather than relying on higher-order representations, E2GNN employs a scalar-vector dual representation to encode equivariant features. By learning geometric symmetry information, our model remains efficient while ensuring prediction accuracy and robustness through the equivariance. Our results show that E2GNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Furthermore, we conduct MD simulations using the E2GNN force field across solid, liquid, and gas systems. It is found that E2GNN can achieve the accuracy of ab initio MD across all examined systems.

Unveiling the mechanism of the carbon ordering and martensite tetragonality in Fe–C alloys studied via deep-potential molecular dynamics simulations

Molecular dynamics and atomistic based continuum studies of the interfacial behavior of nanoreinforced epoxy

Calcium-dependent lectin I from Pseudomonas aeruginosa (PA-IL) binds specifically to oligosaccharides presenting an alpha-galactose residue at their nonreducing end, such as the disaccharides alphaGal1-2betaGalOMe, alphaGal1-3betaGalOMe, and alphaGal1-4betaGalOMe. This provides a unique model for studying the effect of the glycosidic linkage of the ligands on structure and thermodynamics of the complexes by means of experimental and theoretical tools. The structural features of PA-IL in complex with the three disaccharides were established by docking and molecular dynamics simulations and compared with those observed in available crystal structures, including PA-IL.alphaGal1-2betaGalOMe complex, which was solved at 2.4 A resolution and reported herein. The role of a structural bridge water molecule in the binding site of PA-IL was also elucidated through molecular dynamics simulations and free energy calculations. This water molecule establishes three very stable hydrogen bonds with O6 of nonreducing galactose, oxygen from Pro-51 main chain, and nitrogen from Gln-53 main chain of the lectin binding site. Binding free energies for PA-IL in complex with the three disaccharides were investigated, and the results were compared with the experimental data determined by titration microcalorimetry. When the bridge water molecule was included in the free energy calculations, the simulations predicted the correct binding affinity trends with the 1-2-linked disaccharide presenting three times stronger affinity ligand than the other two. These results highlight the role of the water molecule in the binding site of PA-IL and indicate that it should be taken into account when designing glycoderivatives active against P. aeruginosa adhesion.

Grain boundary induced deformation mechanisms in nanocrystalline Al by molecular dynamics simulation: From interatomic potential perspective

The term molecular mechanics (MM) refers to the use of simple potential-energy functions (e.g., harmonic oscillator or Coulombic potentials) to model molecular systems. Molecular mechanics approaches are widely applied in molecular structure refinement, molecular dynamics (MD) simulations, Monte Carlo (MC) simulations, and ligand-docking simulations. Typically, molecular mechanics models consist of spherical atoms connected by springs which represent bonds. Internal forces experienced in the model structure are described using simple mathematical functions. For example, Hooke’s law is commonly used to describe bonded interactions, and the nonbonded atoms might be treated as inelastic hard spheres or may interact according to a Lennard-Jones potential. Using these simple models, a molecular dynamics simulation numerically solves Newton’s equations of motion, thus allowing structural fluctuations to be observed with respect to time. Dynamic simulation methods are widely used to obtain information on the time evolution of conformations of proteins and other biological macromolecules1–4 and also kinetic and thermodynamic information. Simulations can provide fine detail concerning the motions of individual particles as a function of time. They can be utilized to quantify the properties of a system at a precision and on a time scale that is otherwise inaccessible, and simulation is, therefore, a valuable tool in extending our understanding of model systems. Theoretical consideration of a system additionally allows one to investigate the specific contributions to a property through “computational alchemy”,5 that is, modifying the simulation in a way that is nonphysical but nonetheless allows a model’s characteristics to be probed. One particular example is the artificial conversion of the energy function from that representing one system to that of another during a simulation. This is an important technique in free-energy calculations.6 Thus, molecular dynamics simulations, along with a range of complementary computational approaches, have become valuable tools for investigating the basis of protein structure and function. This review offers an outline of the origin of molecular dynamics simulation for protein systems and how it has developed into a robust and trusted tool. This review then covers more recent advances in theory and an illustrative selection of practical studies in which it played a central role. The range of studies in which MD has played a considerable or pivotal role is immense, and this review cannot do justice to them; MD simulations of biomedical importance were recently reviewed.4 Particular emphasis will be placed on the study of dynamic aspects of protein recognition, an area where molecular dynamics has scope to provide broad and far-ranging insights. This review concludes with a brief discussion of the future potential offered to advancement of the biological and biochemical sciences and the remaining issues that must be overcome to allow the full extent of this potential to be realized. 1.1. Historical Background MD methods were originally conceived within the theoretical physics community during the 1950s. In 1957, Alder and Wainwright7 performed the earliest MD simulation using the so-called hard-sphere model, in which the atoms interacted only through perfect collisions. Rahman8 subsequently applied a smooth, continuous potential to mimic real atomic interactions. During the 1970s, as computers became more widespread, MD simulations were developed for more complex systems, culminating in 1976 with the first simulation of a protein9,10 using an empirical energy function constructed using physics-based first-principles assumptions. MD simulations are now widely and routinely applied and especially popular in the fields of materials science11,12 and biophysics. As will be discussed later in this review, a variety of experimental conditions may be simulated with modern theories and algorithms. The initial simulations only considered single molecules in vacuo. Over time, more realistic or at least biologically relevant simulations could be performed. This trend is continuing today. The initial protein MD simulation, of the small bovine pancreatic trypsin inhibitor (BPTI), covered only 9.2 ps of simulation time. Modern simulations routinely have so-called equilibration periods much longer than that, and production simulations of tens of nanoseconds are routine, with the first microsecond MD simulation being reported in 1998.13 In addition, the original BPTI simulation included only about 500 atoms rather than the 104-106 atoms that are common today. While much of this advancement results from an immense increase in availability of computing power, major theoretical and methodological developments also contribute significantly. The number of publications regarding MD theory and application of MD to biological systems is growing at an extraordinary pace. A single review cannot do justice to the recent applications of MD. Using data from ISI Web of Science, the authors estimate that during 2005 at least 800 articles will be published that discuss molecular dynamics and proteins. The historical counts are shown in Figure 1. Open in a separate window Figure 1 Articles matching ISI Web of Science query “TS=(protein) AND TS=(molecular dynamics)”.

Many interesting physical and biological phenomena can be investigated using molecular modeling techniques, either theoretically or by using computer simulation methods, such as molecular dynamics and Monte Carlo simulations. Due to the increasing power of computer processing units, these simulation methods allowed over the last decades for the dramatic increase in knowledge of the behavior of systems at the molecular level. In the first part of this thesis the foundations of molecular modeling techniques are revisited. Empirical force fields and the physical background between thermodynamics and individual particles are discussed. The applicability of molecular modeling techniques is shown by two representative cases. First, the molecular dynamics simulation method is used to understand the dynamics of specific proteins at the molecular level. This is important, because drug design efforts are increasingly laborious, especially with the paucity of available structural information. Therefore, computational methods are helpful in predicting the structure of proteins, and, more importantly, to predict conformational dynamics leading to protein activation. To that end a specific asthma-related protein, the beta2-adrenergic receptor, is investigated in atomistic detail together with the molecules that can bind to the protein to cause activation or inhibition. Clearly, molecular dynamics simulations are an important tool to provide further knowledge on the activation pathway of this protein. Although these all-atom simulations give some insight on the dynamics, the computational demand does not allow for systems much larger than several nanometers or time scales exceeding several nanoseconds. An attempt to overcome these problems is presented by the development of a coarse grained description of the transmembrane proteins. Because coarse graining reduces the number of degrees of freedom, the computational demands decrease, and larger systems can be investigated. However, to maintain the specific characteristics of transmembrane proteins, the general force field used in molecular modeling techniques needs to be extended with hydrogen bonding capabilities and helical backbone stabilization. This new coarse grained model is applicable to transmembrane proteins, and is used to investigate two independent cases: WALP-peptides and antimicrobial peptides. The first serve as a model system for both experiments and theory to investigate the interaction between transmembrane peptides and lipid membranes, whereas the latter are antibiotics whose pore-forming capacities are of great interest to act as target-specific drug candidates. From the molecular dynamics simulations of the WALP-peptides it is shown that the apparent hydrophobic mismatch between peptide and membrane can be resolved by two mechanisms (membrane thickness adaptation and peptide tilting) and that these two mechanisms occur sequential and not in parallel. In the case of the antimicrobial peptides it is shown that many of the orientations found with the molecular simulation techniques are in agreement with experimental observations. The second case to show the applicability of the molecular modeling techniques is that of the heat transfer characteristics of gas flows in nanochannels. Understanding these characteristics is important, because these very small channels are considered to be promising devices to locally cool systems (such as computer processing units) or to be used in lab-on-chip devices for at home medical diagnostics. Thus, understanding the interactions between the channel walls and the gas flow is of great importance. Unfortunately, the computational cost involved in simulating the solid wall, currently restrains the size of the systems that can be investigated using molecular dynamics simulations. Therefore, instead of the explicit modeling of the solid wall, appropriate boundary conditions are used, such as wall potentials or stochastic models. Both of these boundary conditions are examined in great detail and a new wall potential is presented. Also the investigations of a specific case of a channel with platinum walls with a noble gas (argon or xenon) in between allows to introduce a new method to compute an important heat transfer determining parameter. Furthermore, it is shown that both boundary conditions have their benefits and drawbacks, and that the use of either one depends heavily on the application under consideration. Both cases used to show the applicability of molecular modeling techniques, although very different from each other, indicate the importance of particle simulation methods. Investigating the interactions at the molecular level, and the development of new models allows for an even better understanding of underlying molecular processes.