jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sumedha Liyanage | Sanjit Acharya | Prakash Parajuli | Julia L. Shamshina | Noureddine Abidi
Source: Polymers (review, 2021-10-07)
Field: Materials Science — Biomaterials

Production and Surface Modification of Cellulose Bioproducts Petroleum-based synthetic plastics play an important role in our life. As the detrimental health and environmental effects of synthetic plastics continue to increase, the renewable, degradable and recyclable properties of cellulose make subsequent products the "preferred environmentally friendly" alternatives, with a small carbon footprint. Despite the fact that the bioplastic industry is growing rapidly with many innovative discoveries, cellulose-based bioproducts in their natural state face challenges in replacing synthetic plastics. These challenges include scalability issues, high cost of production, and most importantly, limited functionality of cellulosic materials. However, in order for cellulosic materials to be able to compete with synthetic plastics, they must possess properties adequate for the end use and meet performance expectations. In this regard, surface modification of pre-made cellulosic materials preserves the chemical profile of cellulose, its mechanical properties, and biodegradability, while diversifying its possible applications. The review covers numerous techniques for surface functionalization of materials prepared from cellulose such as plasma treatment, surface grafting (including RDRP methods), and chemical vapor and atomic layer deposition techniques. The review also highlights purposeful development of new cellulosic architectures and their utilization, with a specific focus on cellulosic hydrogels, aerogels, beads, membranes, and nanomaterials. The judicious choice of material architecture combined with a specific surface functionalization method will allow us to take full advantage of the polymer's biocompatibility and biodegradability and improve existing and target novel applications of cellulose, such as proteins and antibodies immobilization, enantiomers separation, and composites preparation. Cellulosic ethanol ; Surface modification ; Bioproducts ; Cellulose ; Materials science ; Cellulose fiber ; Environmentally friendly ; Bioplastic ; Nanocellulose ; Nanotechnology ; Waste management ; Chemical engineering ; Chemistry ; Organic chemistry ; Biofuel ; Engineering Advanced Cellulose Research Studies ; Surface Modification and Superhydrophobicity ; Electrospun Nanofibers in Biomedical Applications
Authors: Vijay Gyanani | Jeffrey C. Haley | Roshan Goswami
Source: Pharmaceuticals (review, 2021-08-24)
Field: Materials Science — Biomaterials

Challenges of Current Anticancer Treatment Approaches with Focus on Liposomal Drug Delivery Systems According to a 2020 World Health Organization report (Globocan 2020), cancer was a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. The aim of anticancer therapy is to specifically inhibit the growth of cancer cells while sparing normal dividing cells. Conventional chemotherapy, radiotherapy and surgical treatments have often been plagued by the frequency and severity of side effects as well as severe patient discomfort. Cancer targeting by drug delivery systems, owing to their selective targeting, efficacy, biocompatibility and high drug payload, provides an attractive alternative treatment; however, there are technical, therapeutic, manufacturing and clinical barriers that limit their use. This article provides a brief review of the challenges of conventional anticancer therapies and anticancer drug targeting with a special focus on liposomal drug delivery systems. Medicine ; Drug delivery ; Drug ; Intensive care medicine ; Cancer ; Targeted drug delivery ; Anticancer drug ; Radiation therapy ; Pharmacology ; Internal medicine ; Nanotechnology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Jinzhe Zeng | Timothy J. Giese | Şölen Ekesan | Darrin M. York
Source: Journal of Chemical Theory and Computation (article, 2021-10-13)
Field: Materials Science — Materials Chemistry

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution We develop a new deep potential─range correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase. The new range correction enables short-ranged QM/MM interactions to be tuned for higher accuracy, and the correction smoothly vanishes within a specified cutoff. We further develop an active learning procedure for robust neural network training. We test the DPRc model and training procedure against a series of six nonenzymatic phosphoryl transfer reactions in solution that are important in mechanistic studies of RNA-cleaving enzymes. Specifically, we apply DPRc corrections to a base QM model and test its ability to reproduce free-energy profiles generated from a target QM model. We perform these comparisons using the MNDO/d and DFTB2 semiempirical models because they differ in the way they treat orbital orthogonalization and electrostatics and produce free-energy profiles which differ significantly from each other, thereby providing us a rigorous stress test for the DPRc model and training procedure. The comparisons show that accurate reproduction of the free-energy profiles requires correction of the QM/MM interactions out to 6 Å. We further find that the model's initial training benefits from generating data from temperature replica exchange simulations and including high-temperature configurations into the fitting procedure, so the resulting models are trained to properly avoid high-energy regions. A single DPRc model was trained to reproduce four different reactions and yielded good agreement with the free-energy profiles made from the target QM/MM simulations. The DPRc model was further demonstrated to be transferable to 2D free-energy surfaces and 1D free-energy profiles that were not explicitly considered in the training. Examination of the computational performance of the DPRc model showed that it was fairly slow when run on CPUs but was sped up almost 100-fold when using NVIDIA V100 GPUs, resulting in almost negligible overhead. The new DPRc model and training procedure provide a potentially powerful new tool for the creation of next-generation QM/MM potentials for a wide spectrum of free-energy applications ranging from drug discovery to enzyme design. Range (aeronautics) ; Computer science ; Replica ; Quantum ; Statistical physics ; Energy (signal processing) ; QM/MM ; Molecular dynamics ; Chemistry ; Physics ; Computational chemistry ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Hongjian Tang | Qisong Xu | Mao Wang | Jianwen Jiang
Source: ACS Applied Materials & Interfaces (article, 2021-10-19)
Field: Chemistry — Inorganic Chemistry

Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning At present, 100 000+ metal-organic frameworks (MOFs) have been synthesized, and it is challenging to identity the best candidate for a specific application. In this study, MOFs are rapidly screened via a hierarchical approach for propane/propylene (C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub>) separation. First, the adsorption capacity and selectivity of C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub> mixture in "Computation-Ready, Experimental" (CoRE) MOFs are predicted via a molecular simulation (MS) method. The relationships between separation metrics and structural factors are established, and top-performing CoRE MOFs are identified. Then, machine learning (ML) models are trained and developed upon the CoRE MOFs using pore size, pore geometry, and framework chemistry as feature descriptors. By introducing binned pore size distributions and geometric descriptors, the accuracy of ML models is substantially improved. The feature importance of the descriptors is physically interpreted by the Gini impurities and Shapley Additive Explanations. Subsequently, the ML models are used to rapidly screen experimental "Cambridge Structural Database" (CSD) MOFs and hypothetical MOFs for C<sub>3</sub>H<sub>8</sub>/C<sub>3</sub>H<sub>6</sub> separation. In the CSD MOFs, the out-of-sample predictions are found to agree well with simulation results, demonstrating the excellent transferability of the ML models from the CoRE to CSD MOFs. Moreover, nine CSD MOFs are identified to possess separation performance superior to top-performing CoRE MOFs. Finally, the similarity and diversity among experimental and hypothetical MOFs are visualized and compared by the t-Distributed Stochastic Neighbor Embedding (t-SNE) feature projections. Remarkably, the CoRE and CSD MOFs are revealed to share a close similarity in both chemical and geometric feature spaces. By synergizing MS and ML, the hierarchical approach developed in this study would advance the rapid screening of MOFs across different databases toward industrially important separation processes. Propane ; Metal-organic framework ; Feature (linguistics) ; Materials science ; Core (optical fiber) ; Similarity (geometry) ; Biological system ; Artificial intelligence ; Computer science ; Adsorption ; Thermodynamics ; Chemistry ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Jingbai Li | Patrick Reiser | Benjamin R. Boswell | André Eberhard | Noah Z. Burns | Pascal Friederich | Steven A. Lopez
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations Photochemical reactions are widely used by academic and industrial researchers to construct complex molecular architectures <i>via</i> mechanisms that often require harsh reaction conditions. Photodynamics simulations provide time-resolved snapshots of molecular excited-state structures required to understand and predict reactivities and chemoselectivities. Molecular excited-states are often nearly degenerate and require computationally intensive multiconfigurational quantum mechanical methods, especially at conical intersections. Non-adiabatic molecular dynamics require thousands of these computations per trajectory, which limits simulations to ∼1 picosecond for most organic photochemical reactions. Westermayr <i>et al.</i> recently introduced a neural-network-based method to accelerate the predictions of electronic properties and pushed the simulation limit to 1 ns for the model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub> <sup>+</sup>). We have adapted this methodology to develop the Python-based, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD) software for the <i>cis</i>-<i>trans</i> isomerization of <i>trans</i>-hexafluoro-2-butene and the 4π-electrocyclic ring-closing of a norbornyl hexacyclodiene. We performed a 10 ns simulation for <i>trans</i>-hexafluoro-2-butene in just 2 days. The same simulation would take approximately 58 years with traditional multiconfigurational photodynamics simulations. We generated training data by combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to adaptively sample sparse data regions along reaction coordinates. The final data set of the <i>cis</i>-<i>trans</i> isomerization and the 4π-electrocyclic ring-closing model has 6207 and 6267 data points, respectively. The training errors in energy using feedforward neural networks achieved chemical accuracy (0.023-0.032 eV). The neural network photodynamics simulations of <i>trans</i>-hexafluoro-2-butene agree with the quantum chemical calculations showing the formation of the <i>cis</i>-product and reactive carbene intermediate. The neural network trajectories of the norbornyl cyclohexadiene corroborate the low-yielding <i>syn</i>-product, which was absent in the quantum chemical trajectories, and revealed subsequent thermal reactions in 1 ns. Photoisomerization ; Nanosecond ; Construct (python library) ; Molecular machine ; Computer science ; Chemistry ; Photochemistry ; Nanotechnology ; Materials science ; Physics ; Catalysis ; Isomerization ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Tomohiko Konno | Hodaka Kurokawa | Fuyuki Nabeshima | Yuki Sakishita | Ryō Ogawa | Iwao Hosako | Atsutaka Maeda
Source: Physical review. B./Physical review. B (article, 2021-01-12)
Field: Materials Science — Materials Chemistry

Deep learning model for finding new superconductors Exploration of new superconductors still relies on the experience and intuition of experts, and is largely a process of experimental trial and error. In one study, only 3% of the candidate materials showed superconductivity [Hosono et al., Sci. Technol. Adv. Mater. 16, (2015)]. Here, we report a deep learning model for finding new superconductors. We introduced the method named ``reading periodic table'' that represented the periodic table in a way that allows deep learning to learn to read the periodic table and to learn the law of elements for the purpose of discovering novel superconductors which are outside the training data. It is recognized that it is difficult for deep learning to predict something outside the training data. Although we used only the chemical composition of materials as information, we obtained an ${R}^{2}$ value of 0.92 for predicting ${T}_{\text{c}}$ for materials in a database of superconductors. We also introduced the method named ``garbage-in'' to create synthetic data of nonsuperconductors that do not exist. Nonsuperconductors are not reported, but the data must be required for deep learning to distinguish between superconductors and nonsuperconductors. We obtained three remarkable results. The deep learning can predict superconductivity for a material with a precision of 62%, which shows the usefulness of the model; it found the recently discovered superconductor ${\mathrm{CaBi}}_{2}$ and another one ${\mathrm{Hf}}_{0.5}{\mathrm{Nb}}_{0.2}{\mathrm{V}}_{2}{\mathrm{Zr}}_{0.3}$, neither of which is in the superconductor database; and it found Fe-based high-temperature superconductors (discovered in 2008) from the training data before 2008. These results open the way for the discovery of new high-temperature superconductor families. The candidate materials list, data, and method are openly available on the Internet. Superconductivity ; Periodic table ; Deep learning ; Condensed matter physics ; Artificial intelligence ; Intuition ; Physics ; Computer science ; Machine learning ; Quantum mechanics ; Psychology ; Cognitive science Machine Learning in Materials Science ; Rare-earth and actinide compounds ; X-ray Diffraction in Crystallography
Authors: Dorián László Galata | Zsófia Könyves | Brigitta Nagy | Márk Novák | Lilla Alexandra Mészáros | Edina Szabó | Attila Farkas | György Marosi | Zsombor Kristóf Nagy
Source: International Journal of Pharmaceutics (article, 2021-02-02)
Field: Chemistry — Analytical Chemistry

Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data In this work spectroscopic measurements, process data and Critical Material Attributes (CMAs) are used to predict the in vitro dissolution profile of sustained-release tablets with three machine learning methods, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble of Regression Trees (ERT). Beside the effect of matrix polymer content and compression force, the influence of active pharmaceutical ingredient (API) and matrix polymer particle size distribution (PSD) on the drug release rate of sustained tablets is studied. The matrix polymer PSD was found to be a significant factor, thus this factor was included in the dissolution prediction experiments. In order to evaluate the importance of the inclusion of PSD data, models without PSD data were also prepared and the results were compared. In the developed models, the API and hydroxypropyl-methylcellulose (HPMC) content is predicted from near-infrared (NIR) spectra, the compression force is measured by the tablet press and HPMC particle size is measured off-line. The predictions of ANN, SVM and ERT were compared to the measured dissolution profiles of the validation tablets, ANN yielded the most accurate results. In the presented work, data provided by Process Analytical Technology (PAT) sensors is combined with CMAs for the first time to realize the Real-Time Release Testing (RTRT) of tablet dissolution. Dissolution ; Particle size ; Support vector machine ; Matrix (chemical analysis) ; Process analytical technology ; Algorithm ; Materials science ; Active ingredient ; Computer science ; Biological system ; Machine learning ; Chromatography ; Chemistry ; Chemical engineering ; Composite material ; Engineering ; Bioinformatics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Drug Solubulity and Delivery Systems ; Analytical Methods in Pharmaceuticals
Authors: Yangzesheng Sun | Robert F. DeJaco | Zhao Li | Dai Tang | Stephan Glante | David S. Sholl | Coray M. Colina | Randall Q. Snurr | Matthias Thommes | Martin Hartmann | J. Ilja Siepmann
Source: Science Advances (article, 2021-07-21)
Field: Materials Science — Materials Chemistry

Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently identifying the optimal storage temperature requires modeling hydrogen loading as a continuous function of pressure and temperature. Using data obtained from high-throughput Monte Carlo simulations for zeolites, metal-organic frameworks, and hyper-cross-linked polymers, we develop a meta-learning model that jointly predicts the adsorption loading for multiple materials over wide ranges of pressure and temperature. Meta-learning gives higher accuracy and improved generalization compared to fitting a model separately to each material and allows us to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference. Materials with high optimal temperatures are found in close proximity in the fingerprint space and exhibit high isosteric heats of adsorption. Our method and results provide new guidelines toward the design of hydrogen storage materials and a new route to incorporate machine learning into high-throughput materials discovery. Nanoporous ; Hydrogen storage ; Computer science ; Set (abstract data type) ; Hydrogen ; Materials science ; Nanotechnology ; Chemistry Hydrogen Storage and Materials ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Rohit Pratyush Behera | Hortense Le Ferrand
Source: Matter (article, 2021-09-01)
Field: Materials Science — Biomaterials

Impact-resistant materials inspired by the mantis shrimp's dactyl club Nanotechnology ; Club ; Materials science ; Exploit ; Microstructure ; Toughening ; Biology ; Computer science ; Composite material ; Paleontology Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Silk-based biomaterials and applications
Authors: Kang N. Lee | Dongming Zhu | R.S. Lima
Source: Journal of Thermal Spray Technology (article, 2021-01-01)
Field: Materials Science — Ceramics and Composites

Perspectives on Environmental Barrier Coatings (EBCs) Manufactured via Air Plasma Spray (APS) on Ceramic Matrix Composites (CMCs): A Tutorial Paper Materials science ; Composite material ; Ceramic ; Ceramic matrix composite ; Plasma ; Matrix (chemical analysis) ; Thermal spraying ; Coating Advanced ceramic materials synthesis ; High-Temperature Coating Behaviors ; Advanced materials and composites
Authors: He Zhu | Yalan Huang | Jincan Ren | Binghao Zhang | Yubin Ke | Alex K.‐Y. Jen | Qiang Zhang | Xun‐Li Wang | Qi Liu
Source: Advanced Science (review, 2021-01-22)
Field: Materials Science — Materials Chemistry

Bridging Structural Inhomogeneity to Functionality: Pair Distribution Function Methods for Functional Materials Development The correlation between structure and function lies at the heart of materials science and engineering. Especially, modern functional materials usually contain inhomogeneities at an atomic level, endowing them with interesting properties regarding electrons, phonons, and magnetic moments. Over the past few decades, many of the key developments in functional materials have been driven by the rapid advances in short-range crystallographic techniques. Among them, pair distribution function (PDF) technique, capable of utilizing the entire Bragg and diffuse scattering signals, stands out as a powerful tool for detecting local structure away from average. With the advent of synchrotron X-rays, spallation neutrons, and advanced computing power, the PDF can quantitatively encode a local structure and in turn guide atomic-scale engineering in the functional materials. Here, the PDF investigations in a range of functional materials are reviewed, including ferroelectrics/thermoelectrics, colossal magnetoresistance (CMR) magnets, high-temperature superconductors (HTSC), quantum dots (QDs), nano-catalysts, and energy storage materials, where the links between functions and structural inhomogeneities are prominent. For each application, a brief description of the structure-function coupling will be given, followed by selected cases of PDF investigations. Before that, an overview of the theory, methodology, and unique power of the PDF method will be also presented. Bridging (networking) ; Computer science ; Materials science ; Function (biology) ; Nanotechnology ; Pair distribution function ; Biological system ; Mathematics ; Mathematical analysis ; Biology X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Muhammad Kasim | S. M. Vinko
Source: Physical Review Letters (article, 2021-09-15)
Field: Materials Science — Materials Chemistry

Learning the Exchange-Correlation Functional from Nature with Fully Differentiable Density Functional Theory Improving the predictive capability of molecular properties in ab initio simulations is essential for advanced material discovery. Despite recent progress making use of machine learning, utilizing deep neural networks to improve quantum chemistry modeling remains severely limited by the scarcity and heterogeneity of appropriate experimental data. Here we show how training a neural network to replace the exchange-correlation functional within a fully differentiable three-dimensional Kohn-Sham density functional theory framework can greatly improve simulation accuracy. Using only eight experimental data points on diatomic molecules, our trained exchange-correlation networks enable improved prediction accuracy of atomization energies across a collection of 104 molecules containing new bonds, and atoms, that are not present in the training dataset. Differentiable function ; Density functional theory ; Artificial neural network ; Computer science ; Correlation ; Diatomic molecule ; Ab initio ; Machine learning ; Statistical physics ; Artificial intelligence ; Molecule ; Quantum chemistry ; Computational chemistry ; Physics ; Quantum mechanics ; Chemistry ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography