JIA LIN

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In the field of materials research, machine learning (ML) techniques have emerged as indispensable tools. However, the opaqueness in decision making by models can compromise the trustworthiness of results, underscoring the crucial need for model interpretability. Explainable machine learning (XML) strives to augment researchers’ comprehension of material properties and performance. Yet, reliance on high-quality datasets and scarcity of prior knowledge pose challenges for XML research, particularly when dealing with smaller datasets. In this study, using spinel as a representative example, we successfully addressed the data challenges in XML through a cationic perturbation strategy. We demonstrate an effective approach for handling information scarcity in small datasets, thus offering a feasible method for material research and broadening the scope of XML applications in materials science. Furthermore, our investigation successfully uncovered potential causal relationships underlying material properties and validated their consistency with physical cognition. These causal relationships can serve as experimental guides, facilitating the design and optimization of new materials. Consequently, this research holds significant scientific merit in advancing XML in the realm of materials science, while providing profound insights into material properties and fostering the development of reliable ML-based materials research.

Abstract Actin is a key component of the cytoskeleton of eukaryotic cells and is involved in numerous cellular functions.

An overview of solvent replacement strategies shows that there is great progress in green chemistry for replacing hazardous di-polar aprotic solvents, such as N,N-dimethylformamide (DMF), 1-methyl-2-pyrrolidinone (NMP), and 1,4-dioxane (DI), used in processing active industrial ingredients (APIs). In synthetic chemistry, alcohols, carbonates, ethers, eucalyptol, glycols, furans, ketones, cycloalkanones, lactones, pyrrolidinone or solvent mixtures, 2-methyl tetrahydrofuran in methanol, HCl in cyclopentyl methyl ether, or trifluoroacetic acid in propylene carbonate or surfactant water (no organic solvents) are suggested replacement solvents. For the replacement of dichloromethane (DCM) used in chromatography, ethyl acetate ethanol or 2-propanol in heptanes, with or without acetic acid or ammonium hydroxide additives, are suggested, along with methanol acetic acid in ethyl acetate or methyl tert-butyl ether, ethyl acetate in ethanol in cyclohexane, CO2-ethyl acetate, CO2-methanol, CO2-acetone, and CO2-isopropanol. Supercritical CO2 (scCO2) can be used to replace many organic solvents used in processing materials from natural sources. Vegetable, drupe, legume, and seed oils used as co-extractants (mixed with substrate before extraction) can be used to replace the typical organic co-solvents (ethanol, acetone) used in scCO2 extraction. Mixed solvents consisting of a hydrogen bond donor (HBD) solvent and a hydrogen bond acceptor (HBA) are not addressed in GSK or CHEM21 solvent replacement guides. Published data for 100 water-soluble and water-insoluble APIs in mono-solvents show polarity ranges appropriate for the processing of APIs with mixed solvents. When water is used, possible HBA candidate solvents are acetone, acetic acid, acetonitrile, ethanol, methanol, 2-methyl tetrahydrofuran, 2,2,5,5-tetramethyloxolane, dimethylisosorbide, Cyrene, Cygnet 0.0, or diformylxylose. When alcohol is used, possible HBA candidates are cyclopentanone, esters, lactone, eucalytol, MeSesamol, or diformylxylose. HBA—HBA mixed solvents, such as Cyrene—Cygnet 0.0, could provide interesting new combinations. Solubility parameters, Reichardt polarity, Kamlet—Taft parameters, and linear solvation energy relationships provide practical ways for identifying mixed solvents applicable to API systems.

Haixue Jia Jia Lin Dianyu Wang Xue Lv Qian Wang Zhongyan Wang