Discovery of low thermal conductivity compounds with first-principles anharmonic lattice dynamics calculations and Bayesian optimization

Compounds of low lattice thermal conductivity (LTC) are essential for seeking thermoelectric materials with high conversion efficiency. Some strategies have been used to decrease LTC. However, such trials have yielded successes only within a limited exploration space. Here we report the virtual screening of a library containing 54,779 compounds. Our strategy is to search the library through Bayesian optimization using for the initial data the LTC obtained from first-principles anharmonic lattice dynamics calculations for a set of 101 compounds. We discovered 221 materials with very low LTC. Two of them have even an electronic band gap < 1 eV, what makes them exceptional candidates for thermoelectric applications. In addition to those newly discovered thermoelectric materials, the present strategy is believed to be powerful for many other applications in which chemistry of materials are required to be optimized.Comment: 6 pages, 4 figure

A potential diagnostic biomarker: Proteasome LMP2/b1i-differential expression in human uterus neoplasm

Uterine leiomyosarcoma (ULMS) develops more often in the muscle tissue layer of the uterine body than in the uterine cervix. The development of gynecologic tumors is often correlated with female hormone secretion; however, the development of uterine ULMS is not substantially correlated with hormonal conditions, and the risk factors are not yet known. Importantly, a diagnostic-biomarker which distinguishes malignant ULMS from benign tumor leiomyoma (LMA) is yet to be established. Accordingly, it is necessary to analyze risk factors associated with uterine ULMS, to establish a treatment method. Proteasome low-molecular mass polypeptide 2(LMP2)/b1i-deficient mice spontaneously develop uterine LMS, with a disease prevalence of ~40% by 14 months of age. We found LMP2/b1i expression to be absent in human LMS, but present in human LMA. Therefore, defective-LMP2/b1i expression may be one of the risk factors for ULMS. LMP2/b1i is a potential diagnostic-biomarker for uterine ULMS, and may be a targeted-molecule for a new therapeutic approach

Recommender system for discovery of inorganic compounds

A recommender system based on experimental databases is useful for the efficient discovery of inorganic compounds. Here, we review studies on the discovery of as-yet-unknown compounds using recommender systems. The first method used compositional descriptors made up of elemental features. Chemical compositions registered in the inorganic crystal structure database (ICSD) were supplied to machine learning for binary classification. The other method did not use any descriptors, but a tensor decomposition technique was adopted. The predictive performance for currently unknown chemically relevant compositions (CRCs) was determined by examining their presence in other databases. According to the recommendation, synthesis experiments of two pseudo-ternary compounds with currently unknown structures were successful. Finally, a synthesis-condition recommender system was constructed by machine learning of a parallel experimental data-set collected in-house using a polymerized complex method. Recommendation scores for unexperimented conditions were then evaluated. Synthesis experiments under the targeted conditions found two yet-unknown pseudo-binary oxides