A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

EwaldSolidSolution: A High-Throughput Application to Quickly Sample Stable Site Arrangements for Ionic Solid Solutions.

Data-driven materials design of ionic solid solutions often requires sampling (meta)stable site arrangements among the massive number of possibilities, which has been hampered by the lack of relevant methods. Herein, we develop a quick high-throughput sampling application for site arrangements of ionic solid solutions. Given the Ewald Coulombic energies for an initial site arrangement, EwaldSolidSolution updates the modified parts of the energy with varying sites only, which can be exhaustively estimated by using massively parallel processing. Given two representative examples of solid electrolytes, Li10GeP2S12 and Na3Zr2Si2PO12, EwaldSolidSolution successfully calculates the Ewald Coulombic energies of 211,266,225 (235,702,467) site arrangements for Li10GeP2S12 (Na3Zr2Si2PO12) with 216 (160) ion sites per unit cell in 1223.2 (1187.9) seconds: 0.0057898 (0.0050397) milliseconds per site arrangement. The computational cost is enormously saved in comparison with an existing application, which estimates the energy of a site arrangement on the second timescale. The positive correlations between the Ewald Coulombic energies and those estimated by density functional theory calculations show that (meta)stable samples are easily revealed by our computationally inexpensive algorithm. We also reveal that the different-valence nearest-neighbor pairs are distinctively formed in the low-energy site arrangements. EwaldSolidSolution will boost the materials design of ionic solid solutions by attracting broad interest.

Heterosynaptic MoS2 Memtransistors Emulating Biological Neuromodulation for Energy-Efficient Neuromorphic Electronics.

Heterosynaptic neuromodulation is a key enabler for energy-efficient and high-level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three-terminal heterosynaptic memtransistor using an intentional-defect-generated molybdenum disulfide channel. Particularly, the defect-mediated space-charge-limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate-tuned filling of trap states. The device acts as an artificial synapse controlled by sub-femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy-efficient neuromorphic electronics.

Targeted Fusogenic Liposomes for Effective Tumor Delivery and Penetration of Lipophilic Cargoes.

Nanoparticle-based drug delivery has been widely used for effective anticancer treatment. However, a key challenge restricting the efficacy of nanotherapeutics is limited tissue penetration within solid tumors. Here, we report a targeted fusogenic liposome (TFL) that can selectively deliver lipophilic cargo to the plasma membranes of tumor cells. TFL is prepared by directly attaching tumor-targeting peptides to the surface of FL instead of the cationic moieties. The lipophilic cargo loaded in the membrane of TFL is transferred to the plasma membranes of tumor cells and subsequently packaged in the extracellular vesicles (EVs) released by the cells. Systemically administered TFL accumulates in the perivascular region of tumors, where the lipophilic cargo is unloaded to the tumor cell membranes and distributed autonomously throughout the tumor tissue via extracellular vesicle-mediated intercellular transfer. When loaded with a lipophilic pro-apoptotic drug, thapsigargin (Tg), TFL significantly inhibits tumor growth in a mouse colorectal cancer model. Furthermore, the combination treatment with TFL (Tg) potentiates the antitumor efficacy of FDA-approved liposomal doxorubicin, whose therapeutic effect is limited to perivascular regions without significant toxicity.

A Learning-Rate Modulable and Reliable TiOx Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing.

Realization of memristor-based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system-level. In this sense, uniform and reliable titanium oxide (TiOx ) memristor array devices are fabricated to be utilized as constituent device element in hardware neural network, representing passive matrix array structure enabling vector-matrix multiplication process between multisignal and trained synaptic weight. In particular, in situ convolutional neural network hardware system is designed and implemented using a multiple 25 × 25 TiOx memristor arrays and the memristor device parameters are developed to bring global constant voltage programming scheme for entire cells in crossbar array without any voltage tuning peripheral circuit such as transistor. Moreover, the learning rate modulation during in situ hardware training process is successfully achieved due to superior TiOx memristor performance such as threshold uniformity (≈2.7%), device yield (> 99%), repetitive stability (≈3000 spikes), low asymmetry value of ≈1.43, ambient stability (6 months), and nonlinear pulse response. The learning rate modulable fast-converging in situ training based on direct memristor operation shows five times less training iterations and reduces training energy compared to the conventional hardware in situ training at ≈95.2% of classification accuracy.

Bird-Inspired Self-Navigating Artificial Synaptic Compass.

Extrasensory neuromorphic devices that can recognize, memorize, and learn stimuli imperceptible to human beings are of considerable interest in interactive intelligent electronics research. This study presents an artificially intelligent magnetoreceptive synapse inspired by the magnetocognitive ability used by birds for navigation and orientation. The proposed synaptic platform is based on arrays of ferroelectric field-effect transistors with air-suspended magneto-interactive top-gates. A suspended gate of an elastomeric composite with superparamagnetic particles laminated with an electrically conductive polymer is mechanically deformed under a magnetic field, facilitating control of the magnetic-field-dependent contact area of the suspended gate with an underlying ferroelectric layer. The remanent polarization of the ferroelectric layer is electrically programmed with the deformed suspended gate, resulting in analog conductance modulation as a function of the magnitude, number, and time interval of the input magnetic pulses. The proposed extrasensory magnetoreceptive synapse may be used as an artificially intelligent synaptic compass that facilitates barrier-adaptable navigation and mapping of a moving object.

Run-off election-based decision method for the training and inference process in an artificial neural network.

Generally, the decision rule for classifying unstructured data in an artificial neural network system depends on the sequence results of an activation function determined by vector-matrix multiplication between the input bias signal and the analog synaptic weight quantity of each node in a matrix array. Although a sequence-based decision rule can efficiently extract a common feature in a large data set in a short time, it can occasionally fail to classify similar species because it does not intrinsically consider other quantitative configurations of the activation function that affect the synaptic weight update. In this work, we implemented a simple run-off election-based decision rule via an additional filter evaluation to mitigate the confusion from proximity of output activation functions, enabling the improved training and inference performance of artificial neural network system. Using the filter evaluation selected via the difference among common features of classified images, the recognition accuracy achieved for three types of shoe image data sets reached ~ 82.03%, outperforming the maximum accuracy of ~ 79.23% obtained via the sequence-based decision rule in a fully connected single layer network. This training algorithm with an independent filter can precisely supply the output class in the decision step of the fully connected network.

Artificially Intelligent Tactile Ferroelectric Skin.

Lightweight and flexible tactile learning machines can simultaneously detect, synaptically memorize, and subsequently learn from external stimuli acquired from the skin. This type of technology holds great interest due to its potential applications in emerging wearable and human-interactive artificially intelligent neuromorphic electronics. In this study, an integrated artificially intelligent tactile learning electronic skin (e-skin) based on arrays of ferroelectric-gate field-effect transistors with dome-shape tactile top-gates, which can simultaneously sense and learn from a variety of tactile information, is introduced. To test the e-skin, tactile pressure is applied to a dome-shaped top-gate that measures ferroelectric remnant polarization in a gate insulator. This results in analog conductance modulation that is dependent upon both the number and magnitude of input pressure-spikes, thus mimicking diverse tactile and essential synaptic functions. Specifically, the device exhibits excellent cycling stability between long-term potentiation and depression over the course of 10 000 continuous input pulses. Additionally, it has a low variability of only 3.18%, resulting in high-performance and robust tactile perception learning. The 4 × 4  device array is also able to recognize different handwritten patterns using 2-dimensional spatial learning and recognition, and this is successfully demonstrated with a high degree accuracy of 99.66%, even after considering 10% noise.

One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications.

One-dimensional (1D) devices are becoming the most desirable format for wearable electronic technology because they can be easily woven into electronic (e-) textile(s) with versatile functional units while maintaining their inherent features under mechanical stress. In this study, we designed 1D fiber-shaped multi-synapses comprising ferroelectric organic transistors fabricated on a 100-µm Ag wire and used them as multisynaptic channels in an e-textile neural network for wearable neuromorphic applications. The device mimics diverse synaptic functions with excellent reliability even under 6000 repeated input stimuli and mechanical bending stress. Various NOR-type textile arrays are formed simply by cross-pointing 1D synapses with Ag wires, where each output from individual synapse can be integrated and propagated without undesired leakage. Notably, the 1D multi-synapses achieved up to ~90 and ~70% recognition accuracy for MNIST and electrocardiogram patterns, respectively, even in a single-layer neural network, and almost maintained regardless of the bending conditions.

Realizing Cancer Precision Medicine by Integrating Systems Biology and Nanomaterial Engineering.

Many clinical trials for cancer precision medicine have yielded unsatisfactory results due to challenges such as drug resistance and low efficacy. Drug resistance is often caused by the complex compensatory regulation within the biomolecular network in a cancer cell. Recently, systems biological studies have modeled and simulated such complex networks to unravel the hidden mechanisms of drug resistance and identify promising new drug targets or combinatorial or sequential treatments for overcoming resistance to anticancer drugs. However, many of the identified targets or treatments present major difficulties for drug development and clinical application. Nanocarriers represent a path forward for developing therapies with these "undruggable" targets or those that require precise combinatorial or sequential application, for which conventional drug delivery mechanisms are unsuitable. Conversely, a challenge in nanomedicine has been low efficacy due to heterogeneity of cancers in patients. This problem can also be resolved through systems biological approaches by identifying personalized targets for individual patients or promoting the drug responses. Therefore, integration of systems biology and nanomaterial engineering will enable the clinical application of cancer precision medicine to overcome both drug resistance of conventional treatments and low efficacy of nanomedicine due to patient heterogeneity.

Materials design of Kitaev spin liquids beyond the Jackeli-Khaliullin mechanism.

The Kitaev spin liquid provides a rare example of well-established quantum spin liquids in more than one dimension. It is obtained as the exact ground state of the Kitaev spin model with bond-dependent anisotropic interactions. The peculiar interactions can be yielded by the synergy of spin-orbit coupling and electron correlations for specific electron configuration and lattice geometry, which is known as the Jackeli-Khaliullin mechanism. Based on this mechanism, there has been a fierce race for the materialization of the Kitaev spin liquid over the last decade, but the candidates have been still limited mostly to 4d- and 5d-electron compounds including cations with the low-spin d 5 electron configuration, such as Ir4+ and Ru3+. Here we discuss recent efforts to extend the material perspective beyond the Jackeli-Khaliullin mechanism, by carefully reexamining the two requisites, formation of the j eff = 1/2 doublet and quantum interference between the exchange processes, for not only d- but also f-electron systems. We present three examples: the systems including Co2+ and Ni3+ with the high-spin d 7 electron configuration, Pr4+ with the f 1-electron configuration, and polar asymmetry in the lattice structure. In particular, the latter two are intriguing since they may realize the antiferromagnetic Kitaev interactions, in contrast to the ferromagnetic ones in the existing candidates. This partial overview would stimulate further material exploration of the Kitaev spin liquids and its topological properties due to fractional excitations.

Ultrathin Conformable Organic Artificial Synapse for Wearable Intelligent Device Applications.

Ultrathin conformable artificial synapse platforms that can be used as on-body or wearable chips suggest a path to build next-generation, wearable, intelligent electronic systems that can mimic the synaptic operations of the human brain. So far, an artificial synapse architecture with ultimate mechanical flexibility in a freestanding form while maintaining its functionalities with high stability and accuracy on any conformable substrate has not been demonstrated yet. Here, we demonstrate the first ultrathin artificial synapse (∼500 nm total thickness) that features freestanding ferroelectric organic neuromorphic transistors (FONTs), which can stand alone without a substrate or an encapsulation layer. Our simple dry peel-off process allows integration of the freestanding FONTs with an extremely thin film that is transferable to various conformable substrates. The FONTs exhibit excellent and reliable synaptic functions, which can be modulated by diverse electrical stimuli and relative timing (or temporal order) between the pre- and postsynaptic spikes. Furthermore, the FONTs show sustainable synaptic plasticity even under folded condition ( R = 50 µm, ε = 0.48%) for more than 6000 input spikes. Our study suggests that the ultrathin conformable organic artificial synapse platforms are considered as one of key technologies for realization of wearable intelligent electronics in the future.

Synaptic Barristor Based on Phase-Engineered 2D Heterostructures.

The development of energy-efficient artificial synapses capable of manifoldly tuning synaptic activities can provide a significant breakthrough toward novel neuromorphic computing technology. Here, a new class of artificial synaptic architecture, a three-terminal device consisting of a vertically integrated monolithic tungsten oxide memristor, and a variable-barrier tungsten selenide/graphene Schottky diode, termed as a 'synaptic barrister,' are reported. The device can implement essential synaptic characteristics, such as short-term plasticity, long-term plasticity, and paired-pulse facilitation. Owing to the electrostatically controlled barrier height in the ultrathin van der Waals heterostructure, the device exhibits gate-controlled memristive switching characteristics with tunable programming voltages of 0.2-0.5 V. Notably, by electrostatic tuning with a gate terminal, it can additionally regulate the degree and tuning rate of the synaptic weight independent of the programming impulses from source and drain terminals. Such gate tunability cannot be accomplished by previously reported synaptic devices such as memristors and synaptic transistors only mimicking the two-neuronal-based synapse. These capabilities eventually enable the accelerated consolidation and conversion of synaptic plasticity, functionally analogous to the synapse with an additional neuromodulator in biological neural networks.

Controllable Switching Filaments Prepared via Tunable and Well-Defined Single Truncated Conical Nanopore Structures for Fast and Scalable SiOx Memory.

The controllability of switching conductive filaments is one of the central issues in the development of reliable metal-oxide resistive memory because the random dynamic nature and formation of the filaments pose an obstacle to desirable switching performance. Here, we introduce a simple and novel approach to control and form a single silicon nanocrystal (Si-NC) filament for use in SiOx memory devices. The filament is formed with a confined vertical nanoscale gap by using a well-defined single vertical truncated conical nanopore (StcNP) structure. The physical dimensions of the Si-NC filaments such as number, size, and length, which have a significant influence on the switching properties, can be simply engineered by the breakdown of an Au wire through different StcNP structures. In particular, we demonstrate that the designed SiOx memory junction with a StcNP of pore depth of ∼75 nm and a bottom diameter of ∼10 nm exhibited a switching speed of up to 6 ns for both set and reset process, significantly faster than reported SiOx memory devices. The device also exhibited a high ON-OFF ratio, multistate storage ability, acceptable endurance, and retention stability. The influence of the physical dimensions of the StcNP on the switching features is discussed based on the simulated temperature profiles of the Au wire and the nanogap size generated inside the StcNP structure during electromigration.

Low-Temperature-Grown KNbO3 Thin Films and Their Application to Piezoelectric Nanogenerators and Self-Powered ReRAM Device.

Amorphous KNbO3 (KN) film containing KN nanocrystals was grown on TiN/SiO2/Si substrate at 350 °C. This KN film showed a dielectric constant (εr) and a piezoelectric strain constant (d33) of 43 and 80 pm/V at 10 V, respectively, owing to the existence of KN nanocrystals. Piezoelectric nanogenerators (PNGs) were fabricated using KN films grown on the TiN/polyimide/poly(ethylene terephthalate) substrates. The PNG fabricated with the KN film grown at 350 °C showed an open-circuit output voltage of 2.5 V and a short-circuit current of 70 nA. The KN film grown at 350 °C exhibited a bipolar resistive switching behavior with good reliability characteristics that can be explained by the formation and rupture of the oxygen vacancy filaments. The KN resistive random access memory device powered by the KN PNG also showed promising resistive switching behavior. Moreover, the KN film shows good biocompatibility. Therefore, the KN film can be used for self-powered biomedical devices.

Structurally Engineered Nanoporous Ta2O5-x Selector-Less Memristor for High Uniformity and Low Power Consumption.

A memristor architecture based on metal-oxide materials would have great promise in achieving exceptional energy efficiency and higher scalability in next-generation electronic memory systems. Here, we propose a facile method for fabricating selector-less memristor arrays using an engineered nanoporous Ta2O5-x architecture. The device was fabricated in the form of crossbar arrays, and it functions as a switchable rectifier with a self-embedded nonlinear switching behavior and ultralow power consumption (∼2.7 × 10-6 W), which results in effective suppression of crosstalk interference. In addition, we determined that the essential switching elements, such as the programming power, the sneak current, the nonlinearity value, and the device-to-device uniformity, could be enhanced by in-depth structural engineering of the pores in the Ta2O5-x layer. Our results, on the basis of the structural engineering of metal-oxide materials, could provide an attractive approach for fabricating simple and cost-efficient memristor arrays with acceptable device uniformity and low power consumption without the need for additional addressing selectors.