Molecular Autonomous Pathfinder Using Deep Reinforcement Learning.

Diffusion in solids is a slow process that dictates rate-limiting processes in key chemical reactions. Unlike crystalline solids that offer well-defined diffusion pathways, the lack of similar structural motifs in amorphous or glassy materials poses great challenges in bridging the slow diffusion process and material failures. To tackle this problem, we propose an AI-guided long-term atomistic simulation approach: molecular autonomous pathfinder (MAP) framework based on deep reinforcement learning (DRL), where the RL agent is trained to uncover energy efficient diffusion pathways. We employ a Deep Q-Network architecture with distributed prioritized replay buffer, enabling fully online agent training with accelerated experience sampling by an ensemble of asynchronous agents. After training, the agents provide atomistic configurations of diffusion pathways with their energy profile. We use a piecewise nudged elastic band to refine the energy profile of the obtained pathway and the corresponding diffusion time on the basis of transition-state theory. With the MAP framework, we demonstrate atomistic diffusion mechanisms in amorphous silica with time scales comparable to experiments.

Alkali hydroxide (LiOH, NaOH, KOH) in water: Structural and vibrational properties, including neutron scattering results.

Structural and vibrational properties of aqueous solutions of alkali hydroxides (LiOH, NaOH, and KOH) are computed using quantum molecular dynamics simulations for solute concentrations ranging between 1 and 10M. Element-resolved partial radial distribution functions, neutron and x-ray structure factors, and angular distribution functions are computed for the three hydroxide solutions as a function of concentration. The vibrational spectra and frequency-dependent conductivity are computed from the Fourier transforms of velocity autocorrelation and current autocorrelation functions. Our results for the structure are validated with the available neutron data for 17M concentration of NaOH in water [Semrouni et al., Phys. Chem. Chem. Phys. 21, 6828 (2019)]. We found that the larger ionic radius [rLi+

Surface Transfer Doping in MoO3-x/Hydrogenated Diamond Heterostructure.

Surface transfer doping is proposed to be a potential solution for doping diamond, which is hard to dope for applications in high-power electronics. While MoO3 is found to be an effective surface electron acceptor for hydrogen-terminated diamond with a negative electron affinity, the effects of commonly existing oxygen vacancies remain elusive. We have performed reactive molecular dynamics simulations to study the deposition of MoO3-x on a hydrogenated diamond (111) surface and used first-principles calculations based on density functional theory to investigate the electronic structures and charge transfer mechanisms. We find that MoO3-x is an effective surface electron acceptor and the spatial extent of doped holes in hydrogenated diamond is extended, promoting excellent transport properties. Charge transfer is found to monotonically decrease with the level of oxygen vacancy, providing guidance for engineering of the surface transfer doping process.

Inelastic Neutron Scattering Study of Phonon Density of States of Iodine Oxides and First-Principles Calculations.

Iodine oxides I2Oy (y = 4, 5, 6) crystallize into atypical structures that fall between molecular- and framework-base types and exhibit high reactivity in an ambient environment, a property highly desired in the so-called "agent defeat materials". Inelastic neutron scattering experiments were performed to determine the phonon density of states of the newly synthesized I2O5 and I2O6 samples. First-principles calculations were carried out for I2O4, I2O5, and I2O6 to predict their thermodynamic properties and phonon density of states. Comparison of the INS data with the Raman and infrared measurements as well as the first-principles calculations sheds light on their distinctive, anisotropic thermomechanical properties.

Validation of a New Methodology to Create Oral Drugs beyond the Rule of 5 for Intracellular Tough Targets.

Establishing a technological platform for creating clinical compounds inhibiting intracellular protein-protein interactions (PPIs) can open the door to many valuable drugs. Although small molecules and antibodies are mainstream modalities, they are not suitable for a target protein that lacks a deep cavity for a small molecule to bind or a protein found in intracellular space out of an antibody's reach. One possible approach to access these targets is to utilize so-called middle-size cyclic peptides (defined here as those with a molecular weight of 1000-2000 g/mol). In this study, we validated a new methodology to create oral drugs beyond the rule of 5 for intracellular tough targets by elucidating structural features and physicochemical properties for drug-like cyclic peptides and developing library technologies to afford highly N-alkylated cyclic peptide hits. We discovered a KRAS inhibitory clinical compound (LUNA18) as the first example of our platform technology.

Induction and Ferroelectric Switching of Flux Closure Domains in Strained PbTiO3 with Neural Network Quantum Molecular Dynamics.

We have developed an extension of the Neural Network Quantum Molecular Dynamics (NNQMD) simulation method to incorporate electric-field dynamics based on Born effective charge (BEC), called NNQMD-BEC. We first validate NNQMD-BEC for the switching mechanisms of archetypal ferroelectric PbTiO3 bulk crystal and 180° domain walls (DWs). NNQMD-BEC simulations correctly describe the nucleation-and-growth mechanism during DW switching. In triaxially strained PbTiO3 with strain conditions commonly seen in many superlattice configurations, we find that flux-closure texture can be induced with application of an electric field perpendicular to the original polarization direction. Upon field reversal, the flux-closure texture switches via a pair of transient vortices as the intermediate state, indicating an energy-efficient switching pathway. Our NNQMD-BEC method provides a theoretical guidance to study electro-mechano effects with existing machine learning force fields using a simple BEC extension, which will be relevant for engineering applications such as field-controlled switching in mechanically strained ferroelectric devices.

Development of Orally Bioavailable Peptides Targeting an Intracellular Protein: From a Hit to a Clinical KRAS Inhibitor.

Cyclic peptides as a therapeutic modality are attracting a lot of attention due to their potential for oral absorption and accessibility to intracellular tough targets. Here, starting with a drug-like hit discovered using an mRNA display library, we describe a chemical optimization that led to the orally available clinical compound known as LUNA18, an 11-mer cyclic peptide inhibitor for the intracellular tough target RAS. The key findings are as follows: (i) two peptide side chains were identified that each increase RAS affinity over 10-fold; (ii) physico-chemical properties (PCP) including Clog P can be adjusted by side-chain modification to increase membrane permeability; (iii) restriction of cyclic peptide conformation works effectively to adjust PCP and improve bio-activity; (iv) cellular efficacy was observed in peptides with a permeability of around 0.4 × 10-6 cm/s or more in a Caco-2 permeability assay; and (v) while keeping the cyclic peptide's main-chain conformation, we found one example where the RAS protein structure was changed dramatically through induced-fit to our peptide side chain. This study demonstrates how the chemical optimization of bio-active peptides can be achieved without scaffold hopping, much like the processes for small molecule drug discovery that are guided by Lipinski's rule of five. Our approach provides a versatile new strategy for generating peptide drugs starting from drug-like hits.

Wrinkles, Ridges, Miura-Ori, and Moiré Patterns in MoSe2 Using Neural Networks.

Effects of lateral compression on out-of-plane deformation of two-dimensional MoSe2 layers are investigated. A MoSe2 monolayer develops periodic wrinkles under uniaxial compression and Miura-Ori patterns under biaxial compression. When a flat MoSe2 monolayer is placed on top of a wrinkled MoSe2 layer, the van der Waals (vdW) interaction transforms wrinkles into ridges and generates mixed 2H and 1T phases and chain-like defects. Under a biaxial strain, the vdW interaction induces regions of Miura-Ori patterns in bilayers. Strained systems analyzed using a convolutional neural network show that the compressed system consists of semiconducting 2H and metallic 1T phases. The energetics, mechanical response, defect structure, and dynamics are analyzed as bilayers undergo wrinkle-ridge transformations under uniaxial compression and moiré transformations under biaxial compression. Our results indicate that in-plane compression can induce self-assembly of out-of-plane metasurfaces with controllable semiconducting and metallic phases and moiré patterns with unique optoelectronic properties.

Surgery assistance system for continuous resection of brain tumors-proposal of continuous tumor resection forceps, tumor cell separation, dehydration, and isolation mechanism.

The tumor resection ratio must be improved due the increased possibility of recurrence or malignancy. The purpose of this study was to develop a system that includes forceps with a continuous suction function and flow cytometry to diagnose the malignancy of the tumor for safe, accurate, and effective surgery. A newly developed continuous tumor resection forceps consists of a triple pipe structure, which enables continuous suction of the tumor by integrating the reflux water and suction system. The forceps includes tip opening/closure detection switch to control the adsorption and suction strength when tip is opened and closed. To perform accurate tumor diagnosis using flow cytometry, a filtering mechanism was developed for dehydrating reflux water from continuous suction forceps. In addition, a cell isolation mechanism comprising a roller pump and shear force loading mechanism was also newly developed. By using a triple pipe structure, a significantly larger tumor collection ratio was observed compared to the previous double-pipe structure. By performing suction pressure control with the opening/closure detection switch, inaccurate suction can be prevented. By widening the filter area of dehydration mechanism, it was possible to improve the reflux water dehydration ratio. The most appropriate filter area was 85 mm2. By using a newly developed cell isolation mechanism, the processing time can be reduced to less than 1/10 of the original time, keeping the same cell isolation ratio, when compared to the existing pipetting method. Neurosurgery assistance system with continuous tumor resection forceps and a cell separation, dehydration and isolation mechanism was developed. An effective and safe tumor resection, accurate and fast diagnosis of malignancy can be achieved by using the current system.

Squishing Skyrmions: Symmetry-Guided Dynamic Transformation of Polar Topologies Under Compression.

Mechanical controllability of recently discovered topological defects (e.g., skyrmions) in ferroelectric materials is of interest for the development of ultralow-power mechano-electronics that are protected against thermal noise. However, fundamental understanding is hindered by the "multiscale quantum challenge" to describe topological switching encompassing large spatiotemporal scales with quantum mechanical accuracy. Here, we overcome this challenge by developing a machine-learning-based multiscale simulation framework─a hybrid neural network quantum molecular dynamics (NNQMD) and molecular mechanics (MM) method. For nanostructures composed of SrTiO3 and PbTiO3, we find how the symmetry of mechanical loading essentially controls polar topological switching. We find under symmetry-breaking uniaxial compression a squishing-to-annihilation pathway versus formation of a topological composite named skyrmionium under symmetry-preserving isotropic compression. The distinct pathways are explained in terms of the underlying materials' elasticity and symmetry, as well as the Landau-Lifshitz-Kittel scaling law. Such rational control of ferroelectric topologies will likely facilitate exploration of the rich ferroelectric "topotronics" design space.

Broadly Applicable and Comprehensive Synthetic Method for N-Alkyl-Rich Drug-like Cyclic Peptides.

We report a versatile and durable method for synthesizing highly N-alkylated drug-like cyclic peptides. This is the first reported method for synthesizing such peptides in parallel with a high success rate and acceptable purity that does not require optimizations for a particular sequence. We set up each reaction condition by overcoming the following issues: (1) diketopiperazine (DKP) formation, (2) insufficient peptide bond formation due to the steric hindrance of the N-alkylated amino acid, and (3) instability of highly N-alkylated peptides under acidic conditions. Using this newly established method, we successfully synthesized thousands of cyclic peptides to explore the scope of this modality in drug discovery. We here demonstrate the syntheses of a hundred representative examples, including our first clinical N-alkyl-rich cyclic peptide (LUNA18) that inhibits an intracellular tough target (RAS), in 31% total yield and 97% purity on average after 23 or 24 reaction steps.

Hydrogen Bonding in Liquid Ammonia.

The nature of hydrogen bonding in condensed ammonia phases, liquid and crystalline ammonia has been a topic of much investigation. Here, we use quantum molecular dynamics simulations to investigate hydrogen bond structure and lifetimes in two ammonia phases: liquid ammonia and crystalline ammonia-I. Unlike liquid water, which has two covalently bonded hydrogen and two hydrogen bonds per oxygen atom, each nitrogen atom in liquid ammonia is found to have only one hydrogen bond at 2.24 Å. The computed lifetime of the hydrogen bond is t ≅ 0.1 ps. In contrast to crystalline water-ice, we find that hydrogen bonding is practically nonexistent in crystalline ammonia-I.

Neural Network for Principle of Least Action.

The principle of least action is the cornerstone of classical mechanics, theory of relativity, quantum mechanics, and thermodynamics. Here, we describe how a neural network (NN) learns to find the trajectory for a Lennard-Jones (LJ) system that maintains balance in minimizing the Onsager-Machlup (OM) action and maintaining the energy conservation. The phase-space trajectory thus calculated is in excellent agreement with the corresponding results from the "ground-truth" molecular dynamics (MD) simulation. Furthermore, we show that the NN can easily find structural transformation pathways for LJ clusters, for example, the basin-hopping transformation of an LJ38 from an incomplete Mackay icosahedron to a truncated face-centered cubic octahedron. Unlike MD, the NN computes atomic trajectories over the entire temporal domain in one fell swoop, and the NN time step is a factor of 20 larger than the MD time step. The NN approach to OM action is quite general and can be adapted to model morphometrics in a variety of applications.

Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics.

Ferroelectric materials exhibit a rich range of complex polar topologies, but their study under far-from-equilibrium optical excitation has been largely unexplored because of the difficulty in modeling the multiple spatiotemporal scales involved quantum-mechanically. To study optical excitation at spatiotemporal scales where these topologies emerge, we have performed multiscale excited-state neural network quantum molecular dynamics simulations that integrate quantum-mechanical description of electronic excitation and billion-atom machine learning molecular dynamics to describe ultrafast polarization control in an archetypal ferroelectric oxide, lead titanate. Far-from-equilibrium quantum simulations reveal a marked photo-induced change in the electronic energy landscape and resulting cross-over from ferroelectric to octahedral tilting topological dynamics within picoseconds. The coupling and frustration of these dynamics, in turn, create topological defects in the form of polar strings. The demonstrated nexus of multiscale quantum simulation and machine learning will boost not only the emerging field of ferroelectric topotronics but also broader optoelectronic applications.

Neural Network Quantum Molecular Dynamics, Intermediate Range Order in GeSe2, and Neutron Scattering Experiments.

A remarkable property of certain covalent glasses and their melts is intermediate range order, manifested as the first sharp diffraction peak (FSDP) in neutron-scattering experiments, as was exhaustively investigated by Price, Saboungi, and collaborators. Atomistic simulations thus far have relied on either quantum molecular dynamics (QMD), with systems too small to resolve FSDP, or classical molecular dynamics, without quantum-mechanical accuracy. We investigate prototypical FSDP in GeSe2 glass and melt using neural-network quantum molecular dynamics (NNQMD) based on machine learning, which allows large simulation sizes with validated quantum mechanical accuracy to make quantitative comparisons with neutron data. The system-size dependence of the FSDP height is determined by comparing QMD and NNQMD simulations with experimental data. Partial pair distribution functions, bond-angle distributions, partial and neutron structure factors, and ring-size distributions are presented. Calculated FSDP heights agree quantitatively with neutron scattering data for GeSe2 glass at 10 K and melt at 1100 K.

Dielectric Constant of Liquid Water Determined with Neural Network Quantum Molecular Dynamics.

The static dielectric constant ϵ_{0} and its temperature dependence for liquid water is investigated using neural network quantum molecular dynamics (NNQMD). We compute the exact dielectric constant in canonical ensemble from NNQMD trajectories using fluctuations in macroscopic polarization computed from maximally localized Wannier functions (MLWF). Two deep neural networks are constructed. The first, NNQMD, is trained on QMD configurations for liquid water under a variety of temperature and density conditions to learn potential energy surface and forces and then perform molecular dynamics simulations. The second network, NNMLWF, is trained to predict locations of MLWF of individual molecules using the atomic configurations from NNQMD. Training data for both the neural networks is produced using a highly accurate quantum-mechanical method, DFT-SCAN that yields an excellent description of liquid water. We produce 280×10^{6} configurations of water at 7 temperatures using NNQMD and predict MLWF centers using NNMLWF to compute the polarization fluctuations. The length of trajectories needed for a converged value of the dielectric constant at 0°C is found to be 20 ns (40×10^{6} configurations with 0.5 fs time step). The computed dielectric constants for 0, 15, 30, 45, 60, 75, and 90°C are in good agreement with experiments. Our scalable scheme to compute dielectric constants with quantum accuracy is also applicable to other polar molecular liquids.

Dielectric Polymer Property Prediction Using Recurrent Neural Networks with Optimizations.

Despite the growing success of machine learning for predicting structure-property relationships in molecules and materials, such as predicting the dielectric properties of polymers, it is still in its infancy. We report on the effectiveness of solving structure-property relationships for a computer-generated database of dielectric polymers using recurrent neural network (RNN) models. The implementation of a series of optimization strategies was crucial to achieving high learning speeds and sufficient accuracy: (1) binary and nonbinary representations of SMILES (Simplified Molecular Input Line System) fingerprints and (2) backpropagation with affine transformation of the input sequence (ATransformedBP) and resilient backpropagation with initial weight update parameter optimizations (iRPROP- optimized). For the investigated database of polymers, the binary SMILES representation was found to be superior to the decimal representation with respect to the training and prediction performance. All developed and optimized Elman-type RNN algorithms outperformed nonoptimized RNN models in the efficient prediction of nonlinear structure-activity relationships. The average relative standard deviation (RSD) remained well below 5%, and the maximum RSD did not exceed 30%. Moreover, we provide a C++ codebase as a testbed for a new generation of open programming languages that target increasingly diverse computer architectures.

Sulfurization of MoO3 in the Chemical Vapor Deposition Synthesis of MoS2 Enhanced by an H2S/H2 Mixture.

The typical layered transition metal dichalcogenide (TMDC) material, MoS2, is considered a promising candidate for the next-generation electronic device due to its exceptional physical and chemical properties. In chemical vapor deposition synthesis, the sulfurization of MoO3 powders is an essential reaction step in which the MoO3 reactants are converted into MoS2 products. Recent studies have suggested using an H2S/H2 mixture to reduce MoO3 powders in an effective way. However, reaction mechanisms associated with the sulfurization of MoO3 by the H2S/H2 mixture are yet to be fully understood. Here, we perform quantum molecular dynamics (QMD) simulations to investigate the sulfurization of MoO3 flakes using two different gaseous environments: pure H2S precursors and a H2S/H2 mixture. Our QMD results reveal that the H2S/H2 mixture could effectively reduce and sulfurize the MoO3 reactants through additional reactions of H2 and MoO3, thereby providing valuable input for experimental synthesis of higher-quality TMDC materials.

Electrical Characterization of a Double-Layered Conductive Pattern with Different Crack Configurations for Durable E-Textiles.

For the conductive patterns of electronic textiles (e-textiles), it is still challenging to maintain low electrical resistance, even under large or cyclic tensile deformation. This study investigated a double-layered pattern with different crack configurations as a possible solution. Patterns with single crack growth exhibit a low initial resistance and resistance change rate. In contrast, patterns with multiple crack growth maintain their conductivity under deformation, where electrical failure occurs in those with single crack growth. We considered that a double-layered structure could combine the electrical characteristics of patterns with single and multiple crack growths. In this study, each layer was theoretically designed to control the crack configuration. Then, meandering copper patterns, silver ink patterns, and their double layers were fabricated on textiles as patterns with single and multiple crack growths and double-layered patterns, respectively. Their resistance changes under the single (large) and cyclic tensile deformations were characterized. The results confirmed that the double-layered patterns maintained the lowest resistance at the high elongation rate and cycle. The resistance change rates of the meandering copper and silver ink patterns were constant, and changed monotonically against the elongation rate/cycle, respectively. In contrast, the change rate of the double-layered patterns varied considerably when electrical failure occurred in the copper layer. The change rate after the failure was much higher than that before the failure, and on the same order as that of the silver ink patterns.

Optically Induced Three-Stage Picosecond Amorphization in Low-Temperature SrTiO3.

Photoexcitation can drastically modify potential energy surfaces of materials, allowing access to hidden phases. SrTiO3 (STO) is an ideal material for photoexcitation studies due to its prevalent use in nanostructured devices and its rich range of functionality-changing lattice motions. Recently, a hidden ferroelectric phase in STO was accessed through weak terahertz excitation of polarization-inducing phonon modes. In contrast, whereas strong laser excitation was shown to induce nanostructures on STO surfaces and control nanopolarization patterns in STO-based heterostructures, the dynamic pathways underlying these optically induced structural changes remain unknown. Here nonadiabatic quantum molecular dynamics reveals picosecond amorphization in photoexcited STO at temperatures as low as 10 K. The three-stage pathway involves photoinduced charge transfer and optical phonon activation followed by nonlinear charge and lattice dynamics that ultimately lead to amorphization. This atomistic understanding could guide not only rational laser nanostructuring of STO but also broader "quantum materials on demand" technologies.