A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein.

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds.

Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient. We propose a machine learning protocol, the inductive transfer learning force field (ITLFF), to construct protein force fields in seconds with any level of accuracy from a small dataset. This process is achieved by incorporating an inductive transfer learning algorithm into deep neural networks, which learn knowledge of any high-level calculations from a large dataset of low-level method. Here, we use a double-hybrid density functional theory (DFT) as a case functional, but ITLFF is suitable for any high-precision functional. The performance of the selected 18 proteins indicates that compared with the fragment-based double-hybrid DFT algorithm, the force field constructed by ITLFF achieves considerable accuracy with a mean absolute error of 0.0039 kcal/mol/atom for energy and a root mean square error of 2.57 $\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more than 30 000 times faster and obtains more significant efficiency benefits as the system increases. The outstanding performance of ITLFF provides promising prospects for accurate and efficient protein dynamic simulations and makes an important step toward protein folding simulation. Due to the ability of ITLFF to utilize the knowledge acquired in one task to solve related problems, it is also applicable for various problems in biology, chemistry and material science.

Single atom modified two-dimensional bismuthenes for toxic gas detection.

Accurate detection of toxic gases at low concentrations is often difficult because they are colorless, odorless, flammable and denser than air. Therefore, it is urgent to develop highly stable and sensitive toxic gas detectors. However, most gas sensors operate at high temperatures, making the detection of toxic gases more challenging. Two-dimensional materials with high specific surface area and abundant modulation methods of properties provide new inspirations for the development of new toxic gas sensing materials. Here, bismuthene, a single element two-dimensional material with high carrier mobility and excellent stability, was used as a substrate material to investigate the effects of anchoring and doping on its gas detection performance by density functional theory (DFT) calculations. It is revealed that the surface structure altered by single metal atoms (Ba, Be, Ca, K, Li, Mg, Na, and Sr) can promote the improvement of gas detection sensitivity. Buckled honeycomb bismuthene (bBi) with the Be atom anchored (A-Be-Bi) show superior sensitivity to H2S, while D-Ca-Bi, D-Li-Bi, D-Mg-Bi and D-Sr-Bi also have relatively high toxic gas detection sensitivity. We further discussed the recovery times of these modified bBis at various temperatures to determine the potential for applications. The ultra-fast recovery time of less than 0.5 seconds demonstrates the potential of these systems at room temperature and can be applied to the manufacture of toxic gas sensors used under practical sensing conditions.

A novel free-standing metal organic frameworks-derived cobalt sulfide polyhedron array for shuttle effect suppressive lithium-sulfur batteries.

Metal-organic-frameworks-derived nanostructures have received broad attention for secondary batteries. However, many strategies focus on the preparation of dispersive materials, which need complicated steps and some additives for making electrodes of batteries. Here, we develop a novel free-standing Co9S8polyhedron array derived from ZIF-67, which grows on a three-dimensional carbon cloth for lithium-sulfur (Li-S) battery. The polar Co9S8provides strong chemical binding to immobilize polysulfides, which enables efficiently suppressing of the shuttle effect. The free-standing S@Co9S8polyhedron array-based cathode exhibits ultrahigh capacity of 1079 mAh g-1after cycling 100 times at 0.1 C, and long cycling life of 500 cycles at 1 C, recoverable rate-performance and good temperature tolerance. Furthermore, the adsorption energies towards polysulfides are investigated by using density functional theory calculations, which display a strong binding with polysulfides.

Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices.

Fin field-effect transistors (FinFETs) have been widely used in electronic devices on account of their excellent performance, but this new type of device is facing many challenges because of size constraints. Two-dimensional (2D) materials with a layer structure can meet the required thickness of FinFETs and provide ideal carrier transport performance. In this work, we used 2D tellurene as the parent material and modified it with doping techniques to improve electronic device performance. High-performance FinFET devices were prepared with 23 systems screened from 385 doping systems by a combination of first-principle calculations and a machine-learning (ML) model. Moreover, theoretical calculations demonstrated that 1S1@Te and 2S2@Te have high carrier mobility and stability with an electron mobility and a hole mobility of 6.211 × 104 cm2 V-1 S-1 and 1.349 × 104 cm2 V-1 S-1, respectively. This work can provide a reference for subsequent experiments and advance the development of functional materials by using an ML-assisted design paradigm.

A Data-Driven Platform for Two-Dimensional Hybrid Lead-Halide Perovskites.

The exceptional properties of two-dimensional hybrid organic-inorganic lead-halide perovskites (2D HOIPs) have led to a rapid increase in the number of low-dimensional materials for optoelectronic engineering and solar energy conversion. The flexibility and controllability of 2D HOIPs create a vast structural space, which presents an urgent issue to effectively explore 2D HOIPs with better performance for practical applications. However, the traditional RP-DJ classification method falls short in describing the influence of structure on the electronic properties of 2D HOIPs. To overcome this limitation, we employed inorganic structure factors (SF) as a classification descriptor, which considers the influence of inorganic layer distortion of 2D HOIPs. And we investigated the relationship between SF, other physicochemical features, and band gaps of 2D HOIPs. By using this structural descriptor as a feature for a machine learning model, a database of 304920 2D HOIPs and their structural and electronic properties was generated. A large number of previously neglected 2D HOIPs were discovered. With the establishment of this database, experimental data and machine learning methods were combined to develop a 2D HOIPs exploration platform. This platform integrates searching, download, analysis, and online prediction, providing a useful tool for the further discovery of 2D HOIPs.

Encapsulating Metal-Organic-Framework Derived Nanocages into a Microcapsule for Shuttle Effect-Suppressive Lithium-Sulfur Batteries.

Long-term stable secondary batteries are highly required. Here, we report a unique microcapsule encapsulated with metal organic frameworks (MOFs)-derived Co3O4 nanocages for a Li-S battery, which displays good lithium-storage properties. ZIF-67 dodecahedra are prepared at room temperature then converted to porous Co3O4 nanocages, which are infilled into microcapsules through a microfluidic technique. After loading sulfur, the Co3O4/S-infilled microcapsules are obtained, which display a specific capacity of 935 mAh g-1 after 200 cycles at 0.5C in Li-S batteries. A Coulombic efficiency of about 100% is achieved. The constructed Li-S battery possesses a high rate-performance during three rounds of cycling. Moreover, stable performance is verified under both high and low temperatures of 50 °C and -10 °C. Density functional theory calculations show that the Co3O4 dodecahedra display large binding energies with polysulfides, which are able to suppress shuttle effect of polysulfides and enable a stable electrochemical performance.

An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites.

Lead-free double perovskites are regarded as stable and green optoelectronic alternatives to single perovskites, but may exhibit indirect band gaps and high effective masses, thus limiting their maximum photovoltaic efficiency. Considering that the trial-and-error experimental and computational approaches cannot quickly identify ideal candidates, we propose an ensemble learning workflow to screen all suitable double perovskites from the periodic table, with a high predictive accuracy of 92% and a computed speed that is ∼108 faster than ab initio calculations. From ∼23 314 unexplored double perovskites, we successfully identify six candidates that exhibit suitable band gaps (1.0-2.0 eV), where two have direct band gaps and low effective masses. They all show good thermal stabilities that are hopefully able to be synthesized. The proposed ML workflow immensely shortens the screening cycle for double perovskites, which will greatly promote the development and application of photovoltaic devices.

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression.

State of health (SOH) prediction of supercapacitors aims to provide reliable lifetime control and avoid system failure. Gaussian process regression (GPR) has emerged for SOH prediction because of its capability of capturing nonlinear relationships between features, and tracking SOH attenuations effectively. However, traditional GPR methods based on explicit functions require multiple screenings of optimal mean and covariance functions, which results in data scarcity and increased time consumption. In this study, we propose a GPR-implicit function learning, which is a prior knowledge algorithm for calculating mean and covariance functions from a preliminary data set instead of screening. After introducing the implicit function, the average root mean square error (Average RMSE) is 0.0056 F and the average mean absolute percent error (Average MAPE) is 0.6%, where only the first 5% of the data are trained to predict the remaining 95% of the cycles, thereby decreasing the error by more than three times than previous studies. Furthermore, less cycles (i.e., 1%) are trained while still obtaining low prediction errors (i.e., Average RMSE is 0.0094 F and Average MAPE is 1.01%). This work highlights the strength of GPR-implicit function model for SOH prediction of energy storage devices with high precision and limited property data.

Machine-Learning-Enabled Tricks of the Trade for Rapid Host Material Discovery in Li-S Battery.

The shuttle effect has been a major obstacle to the development of lithium-sulfur batteries. The discovery of new host materials is essential, but lengthy and complex experimental studies are inefficient for the identification of potential host materials. We proposed a machine learning method for the rapid discovery of an AB2-type sulfur host material to suppress the shuttle effect using the 2DMatPedia database, discovering 14 new structures (PdN2, TaS2, PtN2, TaSe2, AgCl2, NbSe2, TaTe2, AgF2, NiN2, AuS2, TmI2, NbTe2, NiBi2, and AuBr2) from 1320 AB2-type compounds. These structures have strong adsorptions of greater than 1.0 eV for lithium polysulfides and appreciable electron-transportation capability, which can serve as the most promising AB2-type host materials in lithium-sulfur batteries. On the basis of a small data set, we successfully predicted Li2S6 adsorption at arbitrary sites on substrate materials using transfer learning, with a considerably low mean absolute error (below 0.05 eV). The proposed data-driven method, as accurate as density functional theory calculations, significantly shortens the research cycle of screening AB2-type sulfur host materials by approximately 8 years. This method provides high-precision and expeditious solutions for other high-throughput calculations and material screenings based on adsorption energy predictions.

Sinoacutine inhibits inflammatory responses to attenuates acute lung injury by regulating NF-κB and JNK signaling pathways.

BACKGROUND: Stephania yunnanensis H. S. Lo is widely used as an antipyretic, analgesic and anti-inflammatory herbal medicine in SouthWest China. In this study, we investigated the anti-inflammatory activity and mechanism of sinoacutine (sino), one of the primary components extracted from this plant. METHODS: A RAW264.7 cell model was established using lipopolysaccharide (LPS) induced for estimation of cytokines in vitro, qPCR was used to estimate gene expression, western blot analysis was used to estimate protein level and investigate the regulation of NF- κB, JNK and MAPK signal pathway. In addition, an acute lung injury model was established to determine lung index and levels of influencing factors. RESULTS: Using the RAW264.7 model, we found that sino reduced levels of nitric oxide (NO), tumour necrosis factor-α (TNF-α), interleukin (IL)-1ß and prostaglandin E2 (PGE2) but increased levels of IL-6. qPCR analysis revealed that sino (50, 25 µg/ml) inhibited gene expression of nitric oxide synthase (iNOS). western blot analysis showed that sino significantly inhibited protein levels of both iNOS and COX-2. Further signalling pathway analysis validated that sino also inhibited phosphorylation of p65 in the NF-κB and c-Jun NH2 terminal kinase (JNK) signalling pathways but promoted the phosphorylation of extracellular signal regulated kinase (ERK) and p38 in the MAPK signalling pathway. In addition, in a mouse model induced by LPS, we determined that sino reduced the lung index and the levels of myeloperoxidase (MPO), NO, IL-6 and TNF-α in lung tissues and bronchoalveolar lavage fluid (BALF) in acute lung injury (ALI). CONCLUSION: Taken together, our results demonstrate that sino is a promising drug to alleviate LPS-induced inflammatory reactions.