Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization

In order to accurately understand and estimate molecular properties, finding energetically favorable molecular conformations is the most fundamental task for atomistic computational research on molecules and materials. Geometry optimization based on quantum chemical calculations has enabled the conformation prediction of arbitrary molecules, including de novo ones. However, it is computationally expensive to perform geometry optimizations for enormous conformers. In this study, we introduce the gray-box optimization (GBO) framework, which enables optimal control over the entire geometry optimization process, among multiple conformers. Algorithms designed for GBO roughly estimate energetically preferable conformers during their geometry optimization iterations. They then preferentially compute promising conformers. To evaluate the performance of the GBO framework, we applied it to a test set consisting of seven dipeptides and mycophenolic acid to determine their stable conformations at the density functional theory level. We thus preferentially obtained energetically favorable conformations. Furthermore, the computational costs required to find the most stable conformation were significantly reduced (approximately 1% on average, compared to the naive approach for the dipeptides)

Ultrahigh-sensitivity optical power monitor for Si photonic circuits

A phototransistor is a promising candidate as an optical power monitor in Si photonic circuits since the internal gain of photocurrent enables high sensitivity. However, state-of-the-art waveguide-coupled phototransistors suffer from a responsivity of lower than $10^3$ A/W, which is insufficient for detecting very low power light. Here, we present a waveguide-coupled phototransistor consisting of an InGaAs ultrathin channel on a Si waveguide working as a gate electrode to increase the responsivity. The Si waveguide gate underneath the InGaAs ultrathin channel enables the effective control of transistor current without optical absorption by the gate metal. As a result, our phototransistor achieved the highest responsivity of approximately $10^6$ A/W among the waveguide-coupled phototransistors, allowing us to detect light of 621 fW propagating in the Si waveguide. The high responsivity and the reasonable response time of approximately 100 $\mu$s make our phototransistor promising as an effective optical power monitor in Si photonics circuits

Pushing property limits in materials discovery via boundless objective-free exploration

Materials chemists develop chemical compounds to meet often conflicting demands of industrial applications. This process may not be properly modeled by black-box optimization because the target property is not well defined in some cases. Herein, we propose a new algorithm for automated materials discovery called BoundLess Objective-free eXploration (BLOX) that uses a novel criterion based on kernel-based Stein discrepancy in the property space. Unlike other objective-free exploration methods, a boundary for the materials properties is not needed; hence, BLOX is suitable for open-ended scientific endeavors. We demonstrate the effectiveness of BLOX by finding light-absorbing molecules from a drug database. Our goal is to minimize the number of density functional theory calculations required to discover out-of-trend compounds in the intensity–wavelength property space. Using absorption spectroscopy, we experimentally verified that eight compounds identified as outstanding exhibit the expected optical properties. Our results show that BLOX is useful for chemical repurposing, and we expect this search method to have numerous applications in various scientific disciplines

De novo creation of a naked eye–detectable fluorescent molecule based on quantum chemical computation and machine learning

Designing fluorescent molecules requires considering multiple interrelated molecular properties, as opposed to properties that straightforwardly correlated with molecular structure, such as light absorption of molecules. In this study, we have used a de novo molecule generator (DNMG) coupled with quantum chemical computation (QC) to develop fluorescent molecules, which are garnering significant attention in various disciplines. Using massive parallel computation (1024 cores, 5 days), the DNMG has produced 3643 candidate molecules. We have selected an unreported molecule and seven reported molecules and synthesized them. Photoluminescence spectrum measurements demonstrated that the DNMG can successfully design fluorescent molecules with 75% accuracy (n = 6/8) and create an unreported molecule that emits fluorescence detectable by the naked eye

Optimal Matroid Partitioning Problems

This paper studies optimal matroid partitioning problems for various objective functions. In the problem, we are given a finite set E and k weighted matroids (E, mathcal{I}_i, w_i), i = 1, dots, k, and our task is to find a minimum partition (I_1,dots,I_k) of E such that I_i in mathcal{I}_i for all i. For each objective function, we give a polynomial-time algorithm or prove NP-hardness. In particular, for the case when the given weighted matroids are identical and the objective function is the sum of the maximum weight in each set (i.e., sum_{i=1}^kmax_{ein I_i}w_i(e)), we show that the problem is strongly NP-hard but admits a PTAS

QCforever: Quantum chemistry for everyone

To obtain observable physical or molecular properties like ionization potential and fluo- rescent wavelength with quantum chemical (QC) computation, multi-step computation manip- ulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody, are important for effective database con- struction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules. This tool just requires a molecule file for providing its ob- servable properties, automating the computation process of molecular properties (for ionization potential, fluorescence, etc) and output analysis for providing their multi-values for evaluating a molecule. Incorporating the tool in black-box optimization, we can explore molecules that have properties we desired within the limitation of QC

Reordering Model Using Syntactic Information of a Source Tree for Statistical Machine Translation

Abstract This paper presents a reordering model using syntactic information of a source tree for phrase-based statistical machine translation. The proposed model is an extension of IST-ITG (imposing source tree on inversion transduction grammar) constraints. In the proposed method, the target-side word order is obtained by rotating nodes of the source-side parse-tree. We modeled the node rotation, monotone or swap, using word alignments based on a training parallel corpus and sourceside parse-trees. The model efficiently suppresses erroneous target word orderings, especially global orderings. Furthermore, the proposed method conducts a probabilistic evaluation of target word reorderings. In Englishto-Japanese and English-to-Chinese translation experiments, the proposed method resulted in a 0.49-point improvement (29.31 to 29.80) and a 0.33-point improvement (18.60 to 18.93) in word BLEU-4 compared with IST-ITG constraints, respectively. This indicates the validity of the proposed reordering model

Formulation of Ground States for 2DEG at Rough Surfaces and Application to Nonlinear Model of Surface Roughness Scattering in nMOSFETs

Electron mobility in extremely-thin-body (ETB) nanosheet channels and at cryogenic temperature is known to be dominated by surface roughness scattering. However, the conventional model of surface roughness scattering lacks accuracy because it requires the use of excessive roughness parameters to represent the experimental results. One of the main difficulties for the surface roughness scattering model is that the higher-order perturbations should be accurately included in the model because the surface roughness scattering is a strongly nonlinear phenomenon. Therefore, in this study, the formulation of ground states of two-dimensional electron gas (2DEG) at rough surfaces is derived by introducing a concept of the space-averaged perturbation Hamiltonian. This revised formulation of 2DEG at rough surfaces is different from the conventional solution for 2DEG at the flat surface. The space-averaged perturbation Hamiltonian is invisible in the linearized perturbation system, while its effect is significant in the system with the nonlinear perturbation energy. We combine the revised 2DEG formulation with a nonlinear model of surface roughness scattering and calculate the 2DEG mobility of the bulk Si and ETB Si-on-insulator (SOI) nMOSFETs. As a result, the experimental mobility of bulk and ETB SOI nMOSFETs is well explained in a wide temperature range of 4.2 to 300 K by using the roughness parameters experimentally obtained by transmission electron microscopy (TEM), which also supports the understanding of mobility at cryogenic temperature. The revised nonlinear model reveals that surface roughness scattering under the present model is 13 times stronger than that predicted by the conventional linear model

QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization

To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody are important for effective database construction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a Python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules. This tool just requires a molecule file for providing its observable properties, automating the computation process of molecular properties (for ionization potential, fluorescence, etc.) and output analysis for providing their multi-values for evaluating a molecule. Incorporating the tool in black-box optimization, we can explore molecules that have properties we desired within the limitation of QC computation