Recent progress in the machine learning-assisted rational design of alloys

Alloys designed with the traditional trial and error method have encountered several problems,such as long trial cycles and high costs.The rapid development of big data and artificial intelligence provides a new path for the efficient development of metallic materials,that is,machine learning-assisted design.In this paper,the basic strategy for the machine learning-assisted rational design of alloys was introduced.Research progress in the property-oriented reversal design of alloy composition,the screening design of alloy composition based on models established using element physical and chemical features or microstructure factors,and the optimal design of alloy composition and process parameters based on iterative feedback optimization was reviewed.Results showed the great advantages of machine learning,including high efficiency and low cost.Future development trends for the machine learning-assisted rational design of alloys were also discussed.Interpretable modeling,integrated modeling,high-throughput combination,multi-objective optimization,and innovative platform building were suggested as fields of great interest.

Machine learning-based ionic liquids design and process simulation for CO_(2) separation from flue gas

Rational design of ionic liquids(ILs),which is highly dependent on the accuracy of the model used,has always been crucial for CO_(2)separation from flue gas.In this study,a support vector machine(SVM)model which is a machine learning approach is established,so as to improve the prediction accuracy and range of IL melting points.Based on IL melting points data with 600 training data and 168 testing data,the estimated average absolute relative deviations(AARD)and squared correlation coefficients(R^(2))are 3.11%,0.8820 and 5.12%,0.8542 for the training set and testing set of the SVM model,respectively.Then,through the melting points model and other rational design processes including conductor-like screening model for real solvents(COSMO-RS)calculation and physical property constraints,cyano-based ILs are obtained,in which tetracyanoborate[TCB]-is often ruled out due to incorrect estimation of melting points model in the literature.Subsequently,by means of process simulation using Aspen Plus,optimal IL are compared with excellent IL reported in the literature.Finally,1-ethyl-3-methylimidazolium tricyanomethanide[EMIM][TCM]is selected as a most suitable solvent for CO_(2)separation from flue gas,the process of which leads to 12.9%savings on total annualized cost compared to that of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide[EMIM][Tf_(2)N].

Efficient Activity Enhancement of a Lipase from Sporisorium reilianum for the Synthesis of a Moxifloxacin Chiral Intermediate via Rational Design

Lipase-catalyzed stereoselective resolution of cis-(±)-dimethyl 1-acetylpiperidine-2,3-dicarboxylate(cis-(±)-1)is an attractive route for the synthesis of(S,S)-2,8-diazobicyclo[4.3.0]nonane,an important chiral intermediate of the fluoroquinolone antibiotic,moxifloxacin.In our previous study,a lipase from Sporisorium reilianum(SRL)was identified to possess excellent thermostability and pH stability.However,the low enzymatic activity of the SRL is a challenge that must be addressed.A rational design was initially employed for SRL tailoring according to the engineered Candida antarctica lipase B(CALB),resulting in a beneficial variant called SRL-I194N/V195L.Subsequently,two key amino acid residues in loop 6,L145 and L154,which might modulate the lid conformation between open and closed,were identified.A tetra-site variant,SRL-I194N/V195L/L145V/L154G(V13),with a significantly enhanced activity of 87.8 U∙mg^(−1) was obtained;this value was 2195-fold higher than that of wild-type SRL.Variant V13 was used to prepare optically pure(2S,3R)-dimethyl 1-acetylpiperidine-2,3-dicarboxylate((2S,3R)-1),resolving 1 mol∙L^(−1) cis-(±)-1 with a conversion of 49.9%in 2 h and absolute stereoselectivity(E>200).Excellent stability with a half-life of 92.5 h was also observed at 50℃.Overall,the study findings reveal a lipase with high activity toward cis-(±)-1 at an industrial level and may offer a general strategy for enhancing the enzyme activity of other lipases and other classes of enzymes with a lid moiety.

NEW DESIGN METHOD FOR HYPOID GEARS

A digital model is presented for the purpose of design, manufacture and measurement of hypoid gear, based on the non-uniform rational B-spline surface （NURBS） method. The digital model and the function-oriented active design technique are combined to form a new design method for hypoid gears. The method is well adaptable to CNC bevel gear cutting machines and CNC-controlled gear inspection machines, and can be used to create the initial machine tool cutting location data or program measurement path. The presented example verifies the method is correct.

An Intelligent Manufacturing Platform of Polymers:Polymeric Material Genome Engineering

Polymeric materials with excellent performance are the foundation for developing high-level technology and advanced manufacturing.Polymeric material genome engineering(PMGE)is becoming a vital platform for the intelligent manufacturing of polymeric materials.However,the development of PMGE is still in its infancy,and many issues remain to be addressed.In this perspective,we elaborate on the PMGE concepts,summarize the state-of-the-art research and achievements,and highlight the challenges and prospects in this field.In particular,we focus on property estimation approaches,including property proxy prediction and machine learning prediction of polymer properties.The potential engineering applications of PMGE are discussed,including the fields of advanced composites,polymeric materials for communications,and integrated circuits.

Rational design of metal selenides nanomaterials for alkali metal ion(Li^(+)/Na^(+)/K^(+))batteries:current status and perspectives

Recently,metal selenides have obtained widespread attention as electrode materials for alkali(Li^(+)/Na^(+)/K^(+))batteries due to their promising theoretical capacity and mechanism.Nevertheless,metal selenides,similar to metal oxides and sulfides,also suffer from severe volume explosion during repeated charge/discharge processes,which results in the structure collapse and the following pulverization of electrode materials.Hence,it leads to poor cycle stability and influencing their further application.In order to solve these issues,some special strategies,including elemental doping,coupling with carbon materials,synthesis of the bimetal selenides with heterostructure,etc.,have been gradually applied to design novel electrode materials with outstanding electrochemical performance.Herein,the recent research progress on metal selenides as anodes for alkali ion batteries is summarized,including the regulation of crystal structure,synthesis strategies,modification methods,and electrochemical mechanisms and kinetics.Besides,the challenges of metal selenides and the perspective for future electrode material design are proposed.It is hoped to pave a way for the development of metal selenide electrode materials for the potential applications for alkali metal ion(Li^(+)/Na^(+)/K^(+))batteries.

Direct carbon dioxide hydrogenation to produce bulk chemicals and liquid fuels via heterogeneous catalysis

The hydrogenation of carbon dioxide(CO_(2))to produce chemicals and transportation liquid fuels in huge demand via heterogeneous thermochemical catalysis achieved using renewable energy has received increasing attention,and substantial advances have been made in this research field in recent years.In this study,we summarize our progress in the rational design and construction of highly efficient catalysts for CO_(2) hydrogenation to methanol,lower olefins,aromatics,and gasolineand jet fuel-range hydrocarbons.The structure‐performance relationship,nature of the active sites,and mechanism of the reactions occurring over these catalysts are explored by combining computational and experimental evidence.The results of this study will promote further fundamental studies and industrial applications of heterogeneous catalysts for CO_(2) hydrogenation to produce bulk chemicals and liquid fuels.

Hybrid enzyme catalysts synthesized by a de novo approach for expanding biocatalysis

The two major challenges in industrial enzymatic catalysis are the limited number of chemical reaction types that are catalyzed by enzymes and the instability of enzymes under harsh conditions in industrial catalysis.Expanding enzyme catalysis to a larger substrate scope and greater variety of chemical reactions and tuning the microenvironment surrounding enzyme molecules to achieve high enzyme performance are urgently needed.In this account,we focus on our efforts using the de novo approach to synthesis hybrid enzyme catalysts that can address these two challenges and the structure-function relationship is discussed to reveal the principles of designing hybrid enzyme catalysts.We hope that this account will promote further efforts toward fundamental research and wide applications of designed enzyme hybrid catalysts for expanding biocatalysis.

Iron(Fe,Ni,Co)-based transition metal compounds for lithium-sulfur batteries:Mechanism,progress and prospects

Lithium-sulfur batteries(LSBs)have a high theoretical capacity,which is considered as one of the most promising high-energy-density secondary batteries due to the double electrons reaction of sulfur.However,the shuttle effects of lithium polysulfides(Li PSs)and sluggish redox kinetics lead to their materials capacity loss and cycle stability deterioration,which restrains LSBs commercialization.Metallic compounds as additions can improve the electrochemical performance of the Li-S system,through the trap of Li PSs and accelerate the conversion of the soluble Li PSs.Among of them,the iron group elements(Fe,Ni,Co)-based compounds are the promising materials for the LSBs,due to their unique outer electronic structure and its tunable properties,low cost,abundant in the earth,environmental benignity,controllable and scalable prepared,and so on.In this review,we have made a summary for iron-based compounds to capture Li PSs according to lithium bond,sulfur bond and magnetic force.The type of iron-based compound including oxides,sulfides,nitrides,phosphides,carbides,and so on,and we have investigated the electrocatalytic mechanism of these materials.Besides,some improvement strategies are proposed,such as the engineering of the special micro/nanostructure,defect concentrations,band structures,and heterostructures.We hope to shed an in-depth light on the rationally design and fabrication of robust,commercial and stable materials for high-performance LSBs.

Progress of polymer reaction engineering:From process engineering to product engineering

Polymer reaction engineering studies the design,operation,and optimization of reactors for industrial scale polymerization,based on the theory of polymerization kinetics and transfer processes(e.g.,flow,heat and mass transfer).Although the foundation and development of this discipline are less than80 years,the global production of polymers has exceeded 400 million tons per annum.It demonstrates that polymer reaction engineering is of vital importance to the polymer industry.Along with the matu rity of production processes and market saturation for bulk polymers,emerging industries such as information technology,modern transportation,biomedicine,and new energy have continued to develop.As a result,the research objective for polymer reaction engineering has gradually shifted from maximizing the efficiency of the polymerization process to the precise regulation of high-end product-oriented macro molecules and their aggregation structures,i.e.,from polymer process engineering to polymer product engineering.In this review,the frontiers of polymer reaction engineering are introduced,including the precise regulation of polymer chain structure,the control of primary aggregation structure,and the rational design of polymer products.We narrow down the topic to the polymerization reaction engineering of vinyl monomers.Moreover,the future prospects are provided for the field of polymer reaction engineering.

Protein A-based ligands for affinity chromatography of antibodies

Protein A chromatography is a key technology in the industrial production of antibodies,and a variety of commercial protein A adsorbents are available in shelf.High stability and binding capacity of a protein A adsorbent are two key issues for successful practice of protein A chromatography.Earlier versions of protein A adsorbents ever exhibited serious fragility to typical cleaning-in-place protocols(e.g.washing with sodium hydroxide solution),and suffered from low binding capacity,harsh elution,ligand leakage and other problems involved in industrial applications.During the last three decades,various techniques and approaches have been applied in the improvement of chemical stability and enhancement of binding capacity of protein A-based ligands and adsorbents for antibody purifications.This mini-review focuses on the technical explorations in protein A-based affinity adsorbents,especially protein A-based ligands,including the efforts to increase the chemical stability by site-directed mutations and to improve the binding capacity by ligand polymerization and site-directed immobilization.Moreover,the efforts to develop short peptide ligands based on the structure of protein A,including the biomimetic design strategies and the synthesis of peptide-mixed mode hybrid ligands are discussed.These peptide and peptidebased hybrid ligands exhibit high affinity and selectivity to antibodies,but noteworthy differences in the binding mechanism of antibody from protein A.As a result,bound antibody to the ligands could be effectively eluted under mild conditions.Perspectives for the development of the protein A-based peptide ligands have been extensively discussed,suggesting that the ligands represent a direction for technological development of antibody purification.

A structural topological optimization method for multi-displacement constraints and any initial topology configuration

In density-based topological design, one expects that the final result consists of elements either black （solid material） or white （void）, without any grey areas. Moreover, one also expects that the optimal topology can be obtained by starting from any initial topology configuration. An improved structural topological optimization method for multidisplacement constraints is proposed in this paper. In the proposed method, the whole optimization process is divided into two optimization adjustment phases and a phase transferring step. Firstly, an optimization model is built to deal with the varied displacement limits, design space adjustments, and reasonable relations between the element stiffness matrix and mass and its element topology variable. Secondly, a procedure is proposed to solve the optimization problem formulated in the first optimization adjustment phase, by starting with a small design space and advancing to a larger deign space. The design space adjustments are automatic when the design domain needs expansions, in which the convergence of the proposed method will not be affected. The final topology obtained by the proposed procedure in the first optimization phase, can approach to the vicinity of the optimum topology. Then, a heuristic algorithm is given to improve the efficiency and make the designed structural topology black/white in both the phase transferring step and the second optimization adjustment phase. And the optimum topology can finally be obtained by the second phase optimization adjustments. Two examples are presented to show that the topologies obtained by the proposed method are of very good 0/1 design distribution property, and the computational efficiency is enhanced by reducing the element number of the design structural finite model during two optimization adjustment phases. And the examples also show that this method is robust and practicable.

A review on the rational design and fabrication of nanosized highentropy materials

High-entropy materials are mainly composed of high-entropy alloys(HEAs)and their derivates.Among them,HEAs account for a big part.As a new kind of alloy,they are now arousing great interests because of their high mechanical strength,extraordinary fracture toughness,corrosion resistance compared with traditional alloys.These characteristics allow the use of HEAs in various fields,including mechanical manufacturing,heat-resistant,radiation-resistant,corrosion-resistant,wear-resistant coatings,energy storage,heterocatalysis,etc.In order to promote the extensive application of HEAs,it is of significance to realize their rational design and preparation.In this paper,a systematic review focusing on the rational design and fabrication of nanosized HEAs is given.The design principles of how to match different elements in HEAs and the premise for the formation of single-phase solid solution HEAs are first illustrated.Computation methods for the prediction of formation conditions and properties of HEAs are also in discussion.Then,a detailed description and comparison of the synthesis methods of HEAs and their derivate,as well as their growing mechanism under various synthetic environments is provided.The commonly used characterization methods for the detection of HEAs,along with the typical cases of the application of HEAs in industrial materials,energy storage materials and catalytic materials are also included.Finally,the challenges and perspectives in the design and synthesis of HEAs would be proposed.We hope this review will give guidance for the future development of HEAs materials.

The role of machine learning in carbon neutrality:Catalyst property prediction,design,and synthesis for carbon dioxide reduction

Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation of natural resources that have accompanied the development of human society.The carbon dioxide reduction reaction(CO_(2)RR)is a promising strategy to capture and convert carbon dioxide(CO_(2))into value-added chemical products.However,the traditional trial-and-error method makes it expensive and time-consuming to understand the deeper mechanism behind the reaction,discover novel catalysts with superior performance and lower cost,and determine optimal support structures and electrolytes for the CO_(2)RR.Emerging machine learning(ML)techniques provide an opportunity to integrate material science and artificial intelligence,which would enable chemists to extract the implicit knowledge behind data,be guided by the insights thereby gained,and be freed from performing repetitive experiments.In this perspective article,we focus on recent ad-vancements in ML-participated CO_(2)RR applications.After a brief introduction to ML techniques and the CO_(2)RR,we first focus on ML-accelerated property prediction for potential CO_(2)RR catalysts.Then we explore ML-aided prediction of catalytic activity and selectivity.This is followed by a discussion about ML-guided catalyst and electrode design.Next,the potential application of ML-assisted experimental synthesis for the CO_(2)RR is discussed.

Transformation of 1-D Chiral-chained Titanium Phosphate to 2-D Layer Structure Through a 1-D Zigzag Chain

The transformation of titanium phosphate from 1-D chiral- chain(JTP-A) to 2-D layer(TP-J1) has been carefully investigated. Through a hydrolysis-condensation self-assembly pathway, the crystals of TP-J1 can be obtained from the JTP-A phase under hydrothermal conditions. An intermediate material with zigzag chain during the transformation was observed by XRD characterization. A hypothesis of the transformation mechanism is also described in this article. It is noteworthy that ethylenediamine plays an important role in the transformation.

AI-assisted food enzymes design and engineering:a critical review

Food enzymes are basic components used for food processing.Through catalysis,food enzymes can function as removing allergy,enriching absorbable nutrients,improving food texture,and adjusting flavors.Food enzymes work in various conditions,which brought out the need for engineering these enzymes with harsh environment tolerance and higher catalytic efficiency.Artificial intelligence(AI)has recently provided solutions for structural modeling,finding modification hot spots,and guiding mutations toward specific needs,which greatly benefit enzyme engineering.AI-based tools showed great advantages in cutting down the computational time,enabling higher prediction accuracy,and providing trainable models suited for wide uses.In this review,we describe the functions and uses of food enzymes,as well as their utility limitations.The necessity and advantages of using AI-based tools in enzyme engineering,and the differences between using traditional and AI-based tools are mainly discussed.Few AI-based tools for enzyme engineering were introduced and described their function.The perspective of using AI tools and the future challenges were discussed.

In Silico Pharmacokinetics Studies for Quinazolines Proposed as EGFR Inhibitors

In silico pharmacokinetics studies can aid the search for molecules with potential ability to be drug candidates. In this paper, a number of quinazoline candidates for epidermal growth factor receptor inhibitors—EGFR, important targets for the treatment of cancer, are computationally analyzed. The literature described that 69 quinazoline molecules were synthesized and the respective half maximum inhibitory concentrations (IC50) were obtained. A bilinear parabolic model was built to investigate the druglikeness by correlating the corresponding lipophilicities, which can be represented by the ideal Log P , with the optimal biological activity in terms of pIC50 values. Structural characteristics leading to improved pharmacokinetics parameters were then analyzed. Compound 56 exhibited the lowest IC50 and, therefore, it had the highest ability to inhibit the EGFR. In the present work, the most potent inhibitor 56 is not calculated to be the most promising drug candidate, since it’s out of the parabolic model obtained due to a Log P above 5, which is not within the expected optimum range. Finally, this work is an example of computational prediction that an experimentally, highly active EGFR inhibitor can be unsuccessful as drug candidate because of pitfalls in pharmacokinetics parameters.

Parametric Selection and Continual Combination of Building Surface

A method to reparametrize G retional curve to obtain a C^1 curve is given. A practical G^1 continual connective between adjacent NURUS patches along common guadratic boundary curve is presented in this paper, and a specific algorithm for control points and weights of NURBS patches is discussed.

Rationally designed amyloid inhibitors based on amyloid-related structural studies

Amyloid proteins correlate with a series of degenerative diseases. Targeting amyloid aggregation has remained a hot topic in therapeutic studies. Numerous inhibitors have been developed, but very few have been approved for marketing. Meanwhile, the growing knowledge of amyloid structural characteristics provides a basis for the rational design of inhibitors. Here we introduce the high-resolution structural findings of amyloid fibrils in recent years and discuss the reported strategies toward rationally designed inhibitors based on amyloid-related structural studies.

Ir single atoms on NiFeZn-LDH matrix for exceptional oxygen evolution reaction

To address the sluggish kinetics of the oxygen evolution reaction(OER),a potential approach is to rationally design and fabricate extremely effective single atom catalysts(SACs).Using an appropriate matrix to stabilize single-atom active centers with optimal geometric and electronic structures is crucial for enhancing catalytic activity.Herein,we report the design and fabrication of Ir single atoms on NiFeZn layered double hydroxide(Ir-SAC/NiFeZn-LDH)electrocatalyst for highly efficient and stable OER.It is investigated that the NiFeZn support exhibits abundant defect sites and unsaturated coordination sites.These sites function to anchor and stabilize single Ir single atoms on the support.The strong synergetic electronic interaction between the Ir single atoms and the NiFeZn matrix resulted in remarkable OER performance of the as-fabricated Ir-SAC/NiFeZn catalyst.With a loading Ir content of 1.09 wt.%,this catalyst demonstrates a highly stable OER activity,with an overpotential of 196 mV at 10 mA·cm^(−2) and a small Tafel slope of 35 mV·dec^(−1) for the OER in a 1 M KOH solution.These results significantly surpass the performance of the commercially available IrO_(2) catalyst.