Development of optimization method for truss structure by quantum annealing.

In this study, we developed a new method of topology optimization for truss structures by quantum annealing. To perform quantum annealing analysis with real variables, representation of real numbers as a sum of random number combinations is employed. The nodal displacement is expressed with binary variables. The Hamiltonian H is formulated on the basis of the elastic strain energy and position energy of a truss structure. It is confirmed that truss deformation analysis is possible by quantum annealing. For the analysis of the optimization method for the truss structure, the cross-sectional area of the truss is expressed with binary variables. The iterative calculation for the changes in displacement and cross-sectional area leads to the optimal structure under the prescribed boundary conditions.

Novel real number representations in Ising machines and performance evaluation: Combinatorial random number sum and constant division.

Quantum annealing machines are next-generation computers for solving combinatorial optimization problems. Although physical simulations are one of the most promising applications of quantum annealing machines, a method how to embed the target problem into the machines has not been developed except for certain simple examples. In this study, we focus on a method of representing real numbers using binary variables, or quantum bits. One of the most important problems for conducting physical simulation by quantum annealing machines is how to represent the real number with quantum bits. The variables in physical simulations are often represented by real numbers but real numbers must be represented by a combination of binary variables in quantum annealing, such as quadratic unconstrained binary optimization (QUBO). Conventionally, real numbers have been represented by assigning each digit of their binary number representation to a binary variable. Considering the classical annealing point of view, we noticed that when real numbers are represented in binary numbers, there are numbers that can only be reached by inverting several bits simultaneously under the restriction of not increasing a given Hamiltonian, which makes the optimization very difficult. In this work, we propose three new types of real number representation and compared these representations under the problem of solving linear equations. As a result, we found experimentally that the accuracy of the solution varies significantly depending on how the real numbers are represented. We also found that the most appropriate representation depends on the size and difficulty of the problem to be solved and that these differences show a consistent trend for two annealing solvers. Finally, we explain the reasons for these differences using simple models, the minimum required number of simultaneous bit flips, one-way probabilistic bit-flip energy minimization, and simulation of ideal quantum annealing machine.

In-layer inhomogeneity of molecular dynamics in quasi-liquid layers of ice.

Quasi-liquid layers (QLLs) are present on the surface of ice and play a significant role in its distinctive chemical and physical properties. These layers exhibit considerable heterogeneity across different scales ranging from nanometers to millimeters. Although the formation of partially ice-like structures has been proposed, the molecular-level understanding of this heterogeneity remains unclear. Here, we examined the heterogeneity of molecular dynamics on QLLs based on molecular dynamics simulations and machine learning analysis of the simulation data. We demonstrated that the molecular dynamics of QLLs do not comprise a mixture of solid- and liquid water molecules. Rather, molecules having similar behaviors form dynamical domains that are associated with the dynamical heterogeneity of supercooled water. Nonetheless, molecules in the domains frequently switch their dynamical state. Furthermore, while there is no observable characteristic domain size, the long-range ordering strongly depends on the temperature and crystal face. Instead of a mixture of static solid- and liquid-like regions, our results indicate the presence of heterogeneous molecular dynamics in QLLs, which offers molecular-level insights into the surface properties of ice.

Novel approach for designing order parameters of clathrate hydrate structures by graph neural network.

Clathrate hydrates continue to be the focus of active research efforts due to their use in energy resources, transportation, and storage-related applications. Therefore, it is crucial to define their essential characteristics from a molecular standpoint. Understanding molecular structure in particular is crucial because it aids in understanding the mechanisms that lead to the formation or dissociation of clathrate hydrates. In the past, a wide variety of order parameters have been employed to classify and evaluate hydrate structures. An alternative approach to inventing bespoke order parameters is to apply machine learning techniques to automatically generate effective order parameters. In earlier work, we suggested a method for automatically designing novel parameters for ice and liquid water structures with Graph Neural Networks (GNNs). In this work, we use a GNN to implement our method, which can independently produce feature representations of the molecular structures. By using the TeaNet-type model in our method, it is possible to directly learn the molecular geometry and topology. This enables us to build novel parameters without prior knowledge of suitable order parameters for the structure type, discover structural differences, and classify molecular structures with high accuracy. We use this approach to classify the structures of clathrate hydrate structures: sI, sII, and sH. This innovative approach provides an appealing and highly accurate replacement for the traditional order parameters. Furthermore, our method makes clear the process of automatically designing a universal parameter for liquid water, ice, and clathrate hydrate to analyze their structures and phases.

Graph-Neural-Network-Based Unsupervised Learning of the Temporal Similarity of Structural Features Observed in Molecular Dynamics Simulations.

Classification of molecular structures is a crucial step in molecular dynamics (MD) simulations to detect various structures and phases within systems. Molecular structures, which are commonly identified using order parameters, were recently identified using machine learning (ML), that is, the ML models acquire structural features using labeled crystals or phases via supervised learning. However, these approaches may not identify unlabeled or unknown structures, such as the imperfect crystal structures observed in nonequilibrium systems and interfaces. In this study, we proposed the use of a novel unsupervised learning framework, denoted temporal self-supervised learning (TSSL), to learn structural features and design their parameters. In TSSL, the ML models learn that the structural similarity is learned via contrastive learning based on minor short-term variations caused by perturbations in MD simulations. This learning framework is applied to a sophisticated architecture of graph neural network models that use bond angle and length data of the neighboring atoms. TSSL successfully classifies water and ice crystals based on high local ordering, and furthermore, it detects imperfect structures typical of interfaces such as the water-ice and ice-vapor interfaces.

Graph neural networks classify molecular geometry and design novel order parameters of crystal and liquid.

Molecular dynamics simulation produces three-dimensional data on molecular structures. The classification of molecular structure is an important task. Conventionally, various order parameters are used to classify different structures of liquid and crystal. Recently, machine learning (ML) methods have been proposed based on order parameters to find optimal choices or use them as input features of neural networks. Conventional ML methods still require manual operation, such as calculating the conventional order parameters and manipulating data to impose rotational/translational invariance. Conversely, deep learning models that satisfy invariance are useful because they can automatically learn and classify three-dimensional structural features. However, in addition to the difficulty of making the learned features explainable, deep learning models require information on large structures for highly accurate classification, making it difficult to use the obtained parameters for structural analysis. In this work, we apply two types of graph neural network models, the graph convolutional network (GCN) and the tensor embedded atom network (TeaNet), to classify the structures of Lennard-Jones (LJ) systems and water systems. Both models satisfy invariance, while GCN uses only length information between nodes. TeaNet uses length and orientation information between nodes and edges, allowing it to recognize molecular geometry efficiently. TeaNet achieved a highly accurate classification with an extremely small molecular structure, i.e., when the number of input molecules is 17 for the LJ system and 9 for the water system, the accuracy is 98.9% and 99.8%, respectively. This is an advantage of our method over conventional order parameters and ML methods such as GCN, which require a large molecular structure or the information of wider area neighbors. Furthermore, we verified that TeaNet could build novel order parameters without manual operation. Because TeaNet can recognize extremely small local structures with high accuracy, all structures can be mapped to a low-dimensional parameter space that can explain structural features. TeaNet offers an alternative to conventional order parameters because of its novelty.

Learned pseudo-random number generator: WGAN-GP for generating statistically robust random numbers.

Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. In this paper, we propose a Wasserstein distance-based generative adversarial network (WGAN) approach to generating PRNGs that fully satisfy the NIST test suite. In this approach, the existing Mersenne Twister (MT) PRNG is learned without implementing any mathematical programming code. We remove the dropout layers from the conventional WGAN network to learn random numbers distributed in the entire feature space because the nearly infinite amount of data can suppress the overfitting problems that occur without dropout layers. We conduct experimental studies to evaluate our learned pseudo-random number generator (LPRNG) by adopting cosine-function-based numbers with poor random number properties according to the NIST test suite as seed numbers. The experimental results show that our LPRNG successfully converted the sequence of seed numbers to random numbers that fully satisfy the NIST test suite. This study opens the way for the "democratization" of PRNGs through the end-to-end learning of conventional PRNGs, which means that PRNGs can be generated without deep mathematical know-how. Such tailor-made PRNGs will effectively enhance the unpredictability and nonarbitrariness of a wide range of information systems, even if the seed numbers can be revealed by reverse engineering. The experimental results also show that overfitting was observed after about 450,000 trials of learning, suggesting that there is an upper limit to the number of learning counts for a fixed-size neural network, even when learning with unlimited data.

Combining Molecular Dynamics and Machine Learning to Analyze Shear Thinning for Alkane and Globular Lubricants in the Low Shear Regime.

Lubricants with desirable frictional properties are important in achieving an energy-saving society. Lubricants at the interfaces of mechanical components are confined under high shear rates and pressures and behave quite differently from the bulk material. Computational approaches such as nonequilibrium molecular dynamics (NEMD) simulations have been performed to probe the molecular behavior of lubricants. However, the low-shear-velocity regions of the materials have rarely been simulated owing to the expensive calculations necessary to do so, and the molecular dynamics under shear velocities comparable with that in the experiments are not clearly understood. In this study, we performed NEMD simulations of extremely confined lubricants, i.e., two molecular layers for four types of lubricants confined in mica walls, under shear velocities from 0.001 to 1 m/s. While we confirmed shear thinning, the velocity profiles could not show the flow behavior when the shear velocity was much slower than thermal fluctuations. Therefore, we used an unsupervised machine learning approach to detect molecular movements that contribute to shear thinning. First, we extracted the simple features of molecular movements from large amounts of MD data, which were found to correlate with the effective viscosity. Subsequently, the extracted features were interpreted by examining the trajectories contributing to these features. The magnitude of diffusion corresponded to the viscosity, and the location of slips that varied depending on the spherical and chain lubricants was irrelevant. Finally, we attempted to apply a modified Stokes-Einstein relation at equilibrium to the nonequilibrium and confined systems. While systems with low shear rates obeyed the relation sufficiently, large deviations were observed under large shear rates.

Prediction of Water Diffusion in Wide Varieties of Polymers with All-Atom Molecular Dynamics Simulations and Deep Generative Models.

Permeation through polymer membranes is an important technology in the chemical industry, and in its design, the self-diffusion coefficient is one of the physical quantities that determine permeability. Since the self-diffusion coefficient sensitively reflects intra- and intermolecular interactions, analysis using an all-atom model is required. However, all-atom simulations are computationally expensive and require long simulation times for the diffusion of small molecules dissolved in polymers. MD-GAN, a machine learning model, is effective in accelerating simulations and reducing computational costs. The target systems for MD-GAN prediction were limited to polyethylene melts in previous studies; therefore, this study extended MD-GAN to systems containing copolymers with branches and successfully predicted water diffusion in various polymers. The correlation coefficient between the predicted self-diffusion coefficient and that of the long-time simulation was 1.00. Additionally, we found that incorporating statistical domain knowledge into MD-GAN improved accuracy, reducing the mean-square displacement prediction outliers from 14.6% to 5.3%. Lastly, the distribution of latent variables with embedded dynamics information within the model was found to be strongly related to accuracy. We believe that these findings can be useful for the practical applications of MD-GAN.

MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data.

Molecular dynamics simulation is a method of investigating the behavior of molecules, which is useful for analyzing a variety of structural and dynamic properties and mechanisms of phenomena. However, the huge computational cost of large-scale and long-time simulations is an enduring problem that must be addressed. MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data [Endo et al., Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32]. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part of the system should be included with the training data. Therefore, the selection of the part of the system is important for efficient learning. In a previous study, only one particle (or vector) of each molecule was extracted as part of the system. The effectiveness of adding information from other particles to the learning process is investigated in this study. When the dynamics of three particles of each molecule were used in the polyethylene experiment, the diffusion was successfully predicted using the training data with a time length of approximately 40%, compared to the single-particle input. Surprisingly, the unobserved transition of diffusion in the training data was also predicted using this method. The reduced cost for the generation of training MD data achieved in this study is useful for accelerating MD-GAN.

Optimal Replica-Exchange Molecular Simulations in Combination with Evolution Strategies.

We have incorporated Evolution Strategies into the Replica-Exchange Monte Carlo simulation method to predict the phase behavior of several example fluids. The replica-exchange method allows one system to exchange temperatures with its neighbors to search for the most stable structure relatively efficiently in a single simulation. However, if the temperature intervals of the replicas are not positioned carefully, there is an issue that local exchange does not occur. Our results for a simple Lennard-Jones fluid and the liquid-crystal Yukawa model demonstrate the utility of the approach when compared to conventional methods. When Evolution Strategies were applied to the Replica-Exchange Monte Carlo simulation, the problem of a significant localized decrease in exchange probability near the phase transition was avoided. By obtaining the optimal temperature intervals, the system efficiently traverses a broader parameter space with a small number of replicas. This is equivalent to accelerating molecular simulations with limited computational resources and can be useful when attempting to predict the phase behavior of complex systems.

A phase-field model by an Ising machine and its application to the phase-separation structure of a diblock polymer.

A novel model to be applied to next-generation accelerators, Ising machines, is formulated on the basis of the phase-field model of the phase-separation structure of a diblock polymer. Recently, Ising machines including quantum annealing machines, attract overwhelming attention as a technology that opens up future possibilities. On the other hand, the phase-field model has demonstrated its high performance in material development, though it takes a long time to achieve equilibrium. Although the convergence time problem might be solved by the next-generation accelerators, no solution has been proposed. In this study, we show the calculation of the phase-separation structure of a diblock polymer as the equilibrium state using phase-field model by an actual Ising machine. The proposed new model brings remarkable acceleration in obtaining the phase-separation structure. Our model can be solved on a large-scale quantum annealing machine. The significant acceleration of the phase-field simulation by the quantum technique pushes the material development to the next stage.

Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein-ligand binding affinities.

Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dynamics induced by ligand binding. However, the relationship between protein dynamics and binding affinity remains unclear. Here, we propose a method that represents ligand-binding-induced protein behavioral change with a simple feature that can be used to predict protein-ligand affinity. From unbiased molecular simulation data, an unsupervised deep learning method measures the differences in protein dynamics at a ligand-binding site depending on the bound ligands. A dimension reduction method extracts a dynamic feature that strongly correlates to the binding affinities. Moreover, the residues that play important roles in protein-ligand interactions are specified based on their contribution to the differences. These results indicate the potential for binding dynamics-based drug discovery.

Efficient Monte Carlo Sampling for Molecular Systems Using Continuous Normalizing Flow.

Monte Carlo molecular simulation is a powerful computational method for simulating molecular behavior. It generates samples of the possible states of molecular systems. To generate a sample efficiently, it is advantageous to avoid suggesting extremely high-energy states that would never become possible states. In this study, we propose a new sampling method for Monte Carlo molecular simulation, that is, a continuous normalizing molecular flow (CNMF) method, which can create various probabilistic distributions of molecular states from some initial distribution. The CNMF method generates samples by solving a first-order differential equation with two-body intermolecular interaction terms. We also develop specific probabilistic distributions using CNMF called inverse square flow, which yields distributions with zero probability density when molecule pairs are in close proximity, whereas probability densities are compressed uniformly from the initial distribution in all other cases. Using inverse square flow, we demonstrate that Monte Carlo molecular simulation is more efficient than the standard simulation. Although the increased computational costs of the CNMF method are non-negligible, this method is feasible for parallel computation and has the potential for expansion.

Endovascular Treatment of Cerebral Venous Circulation Dysfunction Caused by Hemodialysis Shunt: A Case Report.

Objective: Central venous disease, defined as ≥50% stenosis or obstruction of central veins, is one of many life-threatening complications faced by patients on hemodialysis. It often presents as upper limb edema to the arteriovenous (AV) shunt for hemodialysis, although neurological symptoms are rare. We report a case of central venous disease with neurological symptoms associated with endovascular therapy. Case Presentation: A 79-year-old man presented with status epilepticus. His past medical history included rectal carcinoma when he was 69 years old and indication for hemodialysis when he was 79 years old. However, he had no history of neurological disease or epilepsy. On arrival at our facility, CT perfusion revealed venous circulation dysfunction on the left cerebral hemisphere. DSA demonstrated regurgitation from the AV shunt on left upper limb to the cerebral veins and obstruction of the left subclavian vein. Ligation of the causal AV shunt was deemed difficult due to surrounding edema; therefore, endovascular transarterial coil embolization was performed. After completely occluding the AV shunt, patient's condition improved significantly. The patient was discharged 3 days later without neurologic symptoms, with no recurrence of epilepsy was observed to date. Conclusion: Coil embolization of causal AV shunt significantly improved the neurological symptoms of central venous disease.

Mechanical Thrombectomy for Acute Ischemic Stroke Caused by Prosthetic Aortic Valve Endocarditis Due to Exophiala dermatitidis Infection: A Case Report.

Prosthetic valve endocarditis (PVE) can cause large cerebral vessel occlusion. Many reports suggested that mechanical thrombectomy (MT) is effective and useful for early diagnosis from the histopathological findings of thrombus. We present the case of a 62-year-old man, with a history of prosthetic aortic valve replacement and pulmonary vein isolation for his atrial fibrillation, who developed a high fever and an acute neurological deficit, with left hemiplegia and speech disorder. He was diagnosed as having an acute right middle cerebral artery embolism and underwent an MT. The embolic source was found to be a PVE vegetation. However, histopathological analysis of the thrombus could not detect the actual diagnosis. Although he was treated for bacterial endocarditis, his blood culture revealed a rare fungal infection with Exophiala dermatitidis not until >3 weeks after admission. Subsequently, a ß-D-glucan assay also indicated elevated levels. Although he underwent an aortic valve replacement on day 36, MRI showed multiple minor embolic strokes till that day. Early diagnosis of fungal endocarditis and detection of the causative pathogen are still challenging, and the disease has a high risk of occurrence of early and repeated embolic stroke. In addition to clinical findings and pathological studies, ß-D-glucan assay might be a good tool for the diagnosis and evaluation of fungal endocarditis.

A multi-center, prospective study on the progression rate of asymptomatic ventriculomegaly with features of idiopathic normal pressure hydrocephalus on magnetic resonance imaging to idiopathic normal pressure hydrocephalus.

INTRODUCTION: Our previous community-based study demonstrated that some individuals with AVIM [asymptomatic ventriculomegaly with features of idiopathic normal pressure hydrocephalus (iNPH) on magnetic resonance imaging (MRI)] progressed to iNPH in several years. In this hospital-based study, we investigated the progression rate from AVIM to iNPH and its possible predictors. METHODS: We conducted a prospective study of participants with AVIM from several medical institutions/hospitals in Japan. AVIM is defined as "asymptomatic ventriculomegaly with features of iNPH on MRI"; in the present study, asymptomatic was defined as "0 (no symptoms) or 1 (presence of only subjective, but not objective, symptoms) on the iNPH Grading Scale (iNPH-GS)." We also measured possible predicting factors for AVIM-to-iNPH progression, including age, sex, body weight, blood pressure, diabetes mellitus, dyslipidemia, history of mental disease/head injury/sinusitis/smoking/alcohol-intake, Evans index, and the presence of DESH (disproportionately enlarged subarachnoid-space hydrocephalus) findings on brain MRI, and analyzed these potential predictive values. RESULTS: In 2012, 93 participants with AVIM were registered and enrolled in the study. Of these, 52 participants were able to be tracked for three years (until 2015). Of the 52 participants, 27 (52%) developed iNPH during the follow-up period (11 definite, 6 probable, and 10 possible iNPH), whereas 25 participants remained asymptomatic in 2015. Among the possible predictive factors examined, the baseline scores of iNPH-GS predicted the AVIM-to-iNPH progression. CONCLUSIONS: The multicenter prospective study demonstrated that the progression rate from AVIM to iNPH was ~17% per year, and the baseline scores of iNPH-GS predicted the AVIM-to-iNPH progression.

[Paroxysmal Atrial Fibrillation after Percutaneous Thrombectomy on Day 5 of Onset of Cerebral Embolism:A Case Report].

INTRODUCTION: We report a case of embolic stroke with an atypical course after endovascular therapy performed during the subacute stage of progressive stroke, where symptom relapse could not be controlled despite medical treatment. CASE PRESENTATION: An 81-year-old woman developed slight weakness in her left leg and was hospitalized three days after the onset of symptoms. On admission, her consciousness was almost clear and she exhibited left hemiparesis. The computed tomography(CT)and magnetic resonance imaging(MRI)revealed a cerebral infarction in the right caudate head and corona radiata, and CT perfusion showed no difference in the cerebral blood flow. However, three-dimensional computed tomography angiography showed right M1 occlusion. Considering the clinical course of the leg weakness without atrial fibrillation, antiplatelet therapy for atherosclerotic cerebral infarction was administered. Five days after the symptom onset, the left hemiparesis deteriorated. CT and diffusion-weighted MRI showed increasing edema associated with the cerebral infarction, and CTP showed decreased cerebral blood flow in the right middle cerebral artery region. Because angiography revealed an obstruction involving a long lesion with loss of contrast, we suspected an embolic stroke. Endovascular surgery was performed successfully using the Penumbra system. Postoperatively, the hemiparesis resolved and the patient was transferred to the rehabilitation hospital. CONCLUSION: In rare cases, patients with an embolic stroke develop gradual progression of symptoms. To differentiate between cardioembolic stroke and atherosclerotic cerebral infarction in such patients, a follow-up examination of the brain blood flow must be performed, especially when there is a change in symptoms. This may provide useful information for intravascular treatment even in the subacute period.

Detection of molecular behavior that characterizes systems using a deep learning approach.

Molecular dynamics (MD) simulation is a powerful computational method to observe molecular behavior. Although the detection of molecular behavior that characterizes systems is an important task in the study of MD, it is typically difficult and depends on human expert knowledge. Therefore, we propose a novel analysis scheme for MD data using deep neural networks. A key aspect of our scheme is the estimation of statistical distances between different ensembles that are probability distributions over the possible states of systems. This allows us to build low-dimensional embeddings of ensembles to visualize differences between systems in a compact metric space. Furthermore, the molecular behavior that contributes to the differences between systems can also be detected using the trained function of deep neural networks. The applicability of our scheme is demonstrated using three types of MD data. Our scheme could be a powerful tool to clarify the underlying physics in the molecular systems.

[Neuro-Endoscopic Surgery for Multi-Lobular Chronic Subdural Hematoma].

BACKGROUND: Chronic subdural hematoma(CSDH)generally occurs in the elderly, and is usually treated by burr-hole craniotomy with closed-system drainage. Treatment of recurrent CSDH is more challenging, especially when the hematoma is multi-lobular. A variety of approaches to the management of multi-lobular CSDH have been described, including evacuation through a wide craniotomy, placement of an Ommaya reservoir, subdural peritoneal shunting, and embolization of the middle meningeal artery. We have previously reported a method of evacuating multi-lobular CSDH through a small craniotomy using a rigid endoscope and aspiration tube. The objective of this study was to compare our operative method with others from the literature. MATERIALS AND METHODS: Between January 2012 and October 2016, eight patients diagnosed with multi-lobular CSDH using computed tomography(CT)imaging underwent endoscopic evacuation. First, we established a 3×3cm craniotomy at a position where a rigid endoscope and aspiration tube would be able to reach as much of the hematoma cavity as possible in the longitudinal plane. Second, after identifying and removing the outer membrane of the CSDH with the scope, we evacuated the hematoma longitudinally, keeping the inner membrane intact. We also applied monopolar diathermy to any obvious bleeding points and the capillary network on the outer membrane of the CSDH, using the aspiration tube. RESULT: The mean duration of surgery was 42 minutes. Follow-up CT scan revealed no recurrence in any of the cases, and neurologic function improved in all patients postoperatively. CONCLUSION: A multi-lobular CSDH can be drained quickly and effectively using a rigid endoscope and aspiration tube through a small craniotomy. In a cohort of eight patients, postoperative neurologic recovery was observed in all cases with no evidence of recurrence. This technique could be used in any facility with ready access to CT imaging and a rigid endoscope.