jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thiago Teixeira Tasso | Jan C. Schlothauer | Helena C. Junqueira | Tiago A. Matias | Koiti Araki | Erica de Liandra-Salvador | Felipe C. T. Antonio | Paula Homem‐de‐Mello | Maurı́cio S. Baptista
Source: Journal of the American Chemical Society (article, 2019-09-06)
Field: Materials Science — Materials Chemistry

Photobleaching Efficiency Parallels the Enhancement of Membrane Damage for Porphyrazine Photosensitizers Photostability is considered a key asset for photosensitizers (PS) used in medical applications as well as for those used in energy conversion devices. In light-mediated medical treatments, which are based on PS-induced harm to diseased tissues, the photoinduced cycle of singlet oxygen generation has always been considered to correlate with PS efficiency. However, recent evidence points to the fundamental role of contact-dependent reactions, which usually cause PS photobleaching. Therefore, it seems reasonable to challenge the paradigm of photostability versus PS efficiency in medical applications. We have prepared a series of Mg(II) porphyrazines (MgPzs) having similar singlet oxygen quantum yields and side groups with different electron-withdrawing strengths that fine-tune their redox properties. A detailed investigation of the photobleaching mechanism of these porphyrazines revealed that it is independent of singlet oxygen, occurring mainly via photoinduced electron abstraction of surrounding electron rich molecules (solvents or lipids), as revealed by the formation of an air-stable radical anion intermediate. When incorporated into phospholipid membranes, photobleaching of MgPzs correlates with the degree of lipid unsaturation, indicating that it is caused by an electron abstraction from the lipid double bond. Interestingly, upon comparing the efficiency of membrane photodamage between two of these MgPzs (with the highest and the lowest photobleaching efficiencies), we found that the higher the rate of PS photobleaching the faster the leakage induced in the membranes. Our results therefore indicate that photobleaching is a necessary step toward inflicting irreversible biological damage. We propose that the design of more efficient PS for medical applications should contemplate contact-dependent reactions as well as strategies for PS regeneration. Photobleaching ; Chemistry ; Singlet oxygen ; Photochemistry ; Photodegradation ; Membrane ; Rose bengal ; Photocatalysis ; Oxygen ; Organic chemistry ; Fluorescence Porphyrin and Phthalocyanine Chemistry ; Photodynamic Therapy Research Studies ; Nanoplatforms for cancer theranostics
Authors: Jiali Li | Tiankai Chen | Kaizhuo Lim | Lingtong Chen | Saif A. Khan | Jianping Xie | Xiaonan Wang
Source: Advanced Intelligent Systems (article, 2019-06-06)
Field: Materials Science — Materials Chemistry

Deep Learning Accelerated Gold Nanocluster Synthesis The understanding of inorganic reactions, especially those far from the equilibrium state, is relatively limited due to the inherent complexity. Poor understanding of the underlying synthetic chemistry constrains the design of efficient synthesis routes toward the desired final products, especially those at atomic precision. Using the synthesis of atomically precise gold nanoclusters as a demonstration platform, a deep learning framework for guiding material synthesis is successfully developed to accelerate the workflow. With only 54 examples, the graph convolutional neural networks (GCNN) plus siamese neural networks (SNN) classification model is trained. The prediction capability is demonstrated with the successful prediction of literature‐reported protocols. In addition, understanding of the synthesis process can be acquired from a decision tree trained by plentiful generated data from a well‐trained classification model. This study not only provides a data‐driven method accelerating gold nanocluster synthesis, but also sheds light on understanding complex inorganic material synthesis with low data. Nanoclusters ; Computer science ; Workflow ; Convolutional neural network ; Deep learning ; Artificial intelligence ; Machine learning ; Graph ; Nanotechnology ; Theoretical computer science ; Materials science ; Database Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science
Authors: Keisuke Suzuki | Takashi Toyao | Zen Maeno | Satoru Takakusagi | Ken‐ichi Shimizu | Ichigaku Takigawa
Source: ChemCatChem (article, 2019-07-09)
Field: Materials Science — Materials Chemistry

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data Abstract The literature provides insights for catalyst design and discovery. Effective analysis of reported data using machine learning (ML) methods offers the ability to gain valuable information. However, utilizing the literature in this way has obstacles such as lack of compositional overlaps, bias from prior published data, and low sample counts for many elements. The present study describes an ML approach that considers elemental features as input representations instead of inputting catalyst compositions directly. This ML method has the potential for catalyst discovery, including catalytic reactions with limited catalyst composition overlap in the available data. Oxidative coupling of methane (OCM), water gas shift (WGS), and CO oxidation reactions were chosen to confirm the effectiveness of the proposed method by analysis using several state‐of‐the‐art ML methods. Among the ML methods tested, gradient boosting regression with XGBoost (XGB) provided the best results, and prediction accuracy was improved by the proposed approach for all three reaction types. In addition, a quantitative value of “feature importance score” was calculated to evaluate the most influential input variables on catalyst performance. Finally, catalyst optimization was explored using ML as a “surrogate” model, and the top 20 promising candidate catalysts were identified for the OCM reaction based on the optimization. The advantages of ML in catalysis analysis as well as the difficulties and limitations originating from the complexity of heterogeneous catalysis were explored. Catalysis ; Heterogeneous catalysis ; Computer science ; Chemistry ; Biological system ; Machine learning ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yuma Iwasaki | Ryohto Sawada | Valentin Stanev | Masahiko Ishida | Akihiro Kirihara | Yasutomo Omori | Hiroko Someya | Ichiro Takeuchi | Eiji Saitoh | Shinichi Yorozu
Source: npj Computational Materials (article, 2019-10-30)
Field: Materials Science — Materials Chemistry

Identification of advanced spin-driven thermoelectric materials via interpretable machine learning Abstract Machine learning is becoming a valuable tool for scientific discovery. Particularly attractive is the application of machine learning methods to the field of materials development, which enables innovations by discovering new and better functional materials. To apply machine learning to actual materials development, close collaboration between scientists and machine learning tools is necessary. However, such collaboration has been so far impeded by the black box nature of many machine learning algorithms. It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics. Here, we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs). Based on prior knowledge of material science and physics, we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials. Guided by this, we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material. This material shows the largest thermopower to date. Machine learning ; Artificial intelligence ; Identification (biology) ; Thermoelectric materials ; Thermoelectric effect ; Computer science ; Field (mathematics) ; Inference ; Nernst effect ; Nernst equation ; Physics ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Quantum many-body systems
Authors: Pavlo O. Dral
Source: Journal of Computational Chemistry (article, 2019-06-20)
Field: Materials Science — Materials Chemistry

<i>MLatom</i>: A program package for quantum chemical research assisted by machine learning MLatom is a program package designed for computationally efficient simulations of atomistic systems with machine-learning (ML) algorithms. It can be used out-of-the-box as a stand-alone program with a user-friendly online manual. The use of MLatom does not require extensive knowledge of machine learning, programming, or scripting. The user need only prepare input files and choose appropriate options. The program implements kernel ridge regression and supports Gaussian, Laplacian, and Matérn kernels. It can use arbitrary, user-provided input vectors and can convert molecular geometries into input vectors corresponding to several types of built-in molecular descriptors. MLatom saves and re-uses trained ML models as needed, in addition to estimating the generalization error of ML setups. Various sampling procedures are supported and the gradients of output properties can be calculated. The core part of MLatom is written in Fortran, uses standard libraries for linear algebra, and is optimized for shared-memory parallel computations. © 2019 Wiley Periodicals, Inc. Computer science ; Fortran ; Scripting language ; Generalization ; Kernel (algebra) ; Computational science ; Oak Ridge National Laboratory ; Computation ; Machine learning ; Theoretical computer science ; Algorithm ; Parallel computing ; Programming language ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Frits Daeyaert | Fengdan Ye | Michael W. Deem
Source: Proceedings of the National Academy of Sciences (article, 2019-02-07)
Field: Materials Science — Materials Chemistry

Machine-learning approach to the design of OSDAs for zeolite beta We report a machine-learning strategy for design of organic structure directing agents (OSDAs) for zeolite beta. We use machine learning to replace a computationally expensive molecular dynamics evaluation of the stabilization energy of the OSDA inside zeolite beta with a neural network prediction. We train the neural network on 4,781 candidate OSDAs, spanning a range of stabilization energies. We find that the stabilization energies predicted by the neural network are highly correlated with the molecular dynamics computations. We further find that the evolutionary design algorithm samples the space of chemically feasible OSDAs thoroughly. In total, we find 469 OSDAs with verified stabilization energies below -17 kJ/(mol Si), comparable to or better than known OSDAs for zeolite beta, and greatly expanding our previous list of 152 such predicted OSDAs. We expect that these OSDAs will lead to syntheses of zeolite beta. Zeolite ; Artificial neural network ; BETA (programming language) ; Computation ; Molecular dynamics ; Range (aeronautics) ; Computer science ; Artificial intelligence ; Energy (signal processing) ; Machine learning ; Biological system ; Materials science ; Algorithm ; Engineering ; Computational chemistry ; Chemistry ; Physics ; Catalysis ; Quantum mechanics ; Organic chemistry ; Aerospace engineering Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies
Authors: Lim Heo | Michael Feig
Source: Proteins Structure Function and Bioinformatics (article, 2019-11-06)
Field: Materials Science — Materials Chemistry

High‐accuracy protein structures by combining machine‐learning with physics‐based refinement Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from coevolutionary analysis via machine learning to have significantly expanded the ability to predict structures for sequences without templates. One such method, AlphaFold, also performs well on sequences where templates are available but without using such information directly. Here we show that combining machine-learning based models from AlphaFold with state-of-the-art physics-based refinement via molecular dynamics simulations further improves predictions to outperform any other prediction method tested during the latest round of CASP. The resulting models have highly accurate global and local structures, including high accuracy at functionally important interface residues, and they are highly suitable as initial models for crystal structure determination via molecular replacement. Computer science ; Artificial intelligence ; Machine learning Enzyme Structure and Function ; Protein Structure and Dynamics ; Glycosylation and Glycoproteins Research
Authors: Na Wu | Yu Zhang | Risu Na | Chunxiao Mi | Susu Zhu | Yong He | Chu Zhang
Source: RSC Advances (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network Variety identification of seeds is critical for assessing variety purity and ensuring crop yield. In this paper, a novel method based on hyperspectral imaging (HSI) and deep convolutional neural network (DCNN) was proposed to discriminate the varieties of oat seeds. The representation ability of DCNN was also investigated. The hyperspectral images with a spectral range of 874-1734 nm were primarily processed by principal component analysis (PCA) for exploratory visual distinguishing. Then a DCNN trained in an end-to-end manner was developed. The deep spectral features automatically learnt by DCNN were extracted and combined with traditional classifiers (logistic regression (LR), support vector machine with RBF kernel (RBF_SVM) and linear kernel (LINEAR_SVM)) to construct discriminant models. Contrast models were built based on the traditional classifiers using full wavelengths and optimal wavelengths selected by the second derivative (2nd derivative) method. The comparison results showed that all DCNN-based models outperformed the contrast models. DCNN trained in an end-to-end manner achieved the highest accuracy of 99.19% on the testing set, which was finally employed to visualize the variety classification. The results demonstrated that the deep spectral features with outstanding representation ability enabled HSI together with DCNN to be a reliable tool for rapid and accurate variety identification, which would help to develop an on-line system for quality detection of oat seeds as well as other grain seeds. Hyperspectral imaging ; Convolutional neural network ; Identification (biology) ; Variety (cybernetics) ; Representation (politics) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Gul Zarren | Bushra Nisar | Farooq Sher
Source: Materials Today Sustainability (review, 2019-05-20)
Field: Materials Science — Polymers and Plastics

Synthesis of anthraquinone-based electroactive polymers: a critical review Nanotechnology ; Monomer ; Polymer ; Sustainable energy ; Smart polymer ; Materials science ; Sustainability ; Computer science ; Electroactive polymers ; Biochemical engineering ; Chemistry ; Organic chemistry ; Engineering ; Electrical engineering ; Renewable energy Conducting polymers and applications ; Advanced battery technologies research ; Supercapacitor Materials and Fabrication
Authors: Sasan Moradi | Yolanda Calventus | Frida Román | John M. Hutchinson
Source: Polymers (article, 2019-07-06)
Field: Materials Science — Materials Chemistry

Achieving High Thermal Conductivity in Epoxy Composites: Effect of Boron Nitride Particle Size and Matrix-Filler Interface For the thermal management of high watt density circuit layers, it is common to use a filled epoxy system to provide an electrically insulating but thermally conducting bond to a metal substrate. An epoxy-thiol system filled with boron nitride (BN), in the form of 2, 30 and 180 µm platelets, has been investigated with a view to achieving enhanced thermal conductivity. The effect of BN content on the cure reaction kinetics has been studied by differential scanning calorimetry and the thermal conductivity of the cured samples has been measured by the Transient Hot Bridge method. The heat of reaction and the glass transition temperature of the fully cured samples are both independent of the BN content, but the cure reaction kinetics is systematically affected by both BN content and particle size. These results can be correlated with the thermal conductivity of the cured systems, which is found to increase with both BN content and particle size. For a given BN content, the thermal conductivity found here is significantly higher than most others reported in the literature; this effect is attributed to a Lewis acid-base interaction between filler and matrix. Materials science ; Composite material ; Thermal conductivity ; Boron nitride ; Epoxy ; Differential scanning calorimetry ; Particle (ecology) ; Particle size ; Glass transition ; Polymer ; Chemical engineering Thermal properties of materials ; Composite Material Mechanics ; Thermal Expansion and Ionic Conductivity
Authors: Rami W. Chakroun | Feihu Wang | Ran Lin | Yin Wang | Hao Su | Danielle Pompa | Honggang Cui
Source: ACS Nano (article, 2019-05-22)
Field: Materials Science — Biomaterials

Fine-Tuning the Linear Release Rate of Paclitaxel-Bearing Supramolecular Filament Hydrogels through Molecular Engineering One key design feature in the development of any local drug delivery system is the controlled release of therapeutic agents over a certain period of time. In this context, we report the characteristic feature of a supramolecular filament hydrogel system that enables a linear and sustainable drug release over the period of several months. Through covalent linkage with a short peptide sequence, we are able to convert an anticancer drug, paclitaxel (PTX), to a class of prodrug hydrogelators with varying critical gelation concentrations. These self-assembling PTX prodrugs associate into filamentous nanostructures in aqueous conditions and consequently percolate into a supramolecular filament network in the presence of appropriate counterions. The intriguing linear drug release profile is rooted in the supramolecular nature of the self-assembling filaments which maintain a constant monomer concentration at the gelation conditions. We found that molecular engineering of the prodrug design, such as varying the number of oppositely charged amino acids or through the incorporation of hydrophobic segments, allows for the fine-tuning of the PTX linear release rate. In cell studies, these PTX prodrugs can exert effective cytotoxicity against glioblastoma cell lines and also primary brain cancer cells derived from patients and show enhanced tumor penetration in a cancer spheroid model. We believe this drug-bearing hydrogel platform offers an exciting opportunity for the local treatment of human diseases. Prodrug ; Supramolecular chemistry ; Paclitaxel ; Self-healing hydrogels ; Context (archaeology) ; Drug delivery ; Biophysics ; Controlled release ; Materials science ; Nanotechnology ; Protein filament ; Combinatorial chemistry ; Chemistry ; Organic chemistry ; Molecule ; Biochemistry ; Cancer ; Biology Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Supramolecular Chemistry and Complexes
Authors: Charles H. Camp
Source: Journal of Research of the National Institute of Standards and Technology (article, 2019-06-24)
Field: Chemistry — Analytical Chemistry

pyMCR: A Python Library for Multivariate Curve Resolution Analysis with Alternating Regression (MCR-AR) pyMCR is a new open-source software library for performing multivariate curve resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a chemometric method for elucidating measurement signatures of analytes and their relative abundance from a series of mixture measurements, without any knowledge of these values a priori. This software library, written in Python, enables users to perform MCR analysis with their choice of error functions for minimization, constraints, and regressors. Further, users can apply different constraints and regressors for signature and abundance calculations. Finally, this library enables users to develop their own constraints, regressors, and error functions or import them from existing libraries. Python (programming language) ; Multivariate statistics ; Computer science ; Software ; A priori and a posteriori ; Regression ; Data mining ; Algorithm ; Minification ; Statistics ; Mathematics ; Machine learning ; Programming language Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Jeffrey Brender | Shun Kishimoto | Hellmut Merkle | Galen D. Reed | Ralph E. Hurd | Albert P. Chen | Jan Henrik Ardenkjær‐Larsen | Jeeva Munasinghe | Keita Saito | Tomohiro Seki | Nobu Oshima | Kazutoshi Yamamoto | Peter L. Choyke | John C. Mitchell | Murali C. Krishna
Source: Scientific Reports (article, 2019-03-04)
Field: Chemistry — Spectroscopy

Dynamic Imaging of Glucose and Lactate Metabolism by 13C-MRS without Hyperpolarization Metabolic reprogramming is one of the defining features of cancer and abnormal metabolism is associated with many other pathologies. Molecular imaging techniques capable of detecting such changes have become essential for cancer diagnosis, treatment planning, and surveillance. In particular, <sup>18</sup>F-FDG (fluorodeoxyglucose) PET has emerged as an essential imaging modality for cancer because of its unique ability to detect a disturbed molecular pathway through measurements of glucose uptake. However, FDG-PET has limitations that restrict its usefulness in certain situations and the information gained is limited to glucose uptake only.<sup>13</sup>C magnetic resonance spectroscopy theoretically has certain advantages over FDG-PET, but its inherent low sensitivity has restricted its use mostly to single voxel measurements unless dissolution dynamic nuclear polarization (dDNP) is used to increase the signal, which brings additional complications for clinical use. We show here a new method of imaging glucose metabolism in vivo by MRI chemical shift imaging (CSI) experiments that relies on a simple, but robust and efficient, post-processing procedure by the higher dimensional analog of singular value decomposition, tensor decomposition. Using this procedure, we achieve an order of magnitude increase in signal to noise in both dDNP and non-hyperpolarized non-localized experiments without sacrificing accuracy. In CSI experiments an approximately 30-fold increase was observed, enough that the glucose to lactate conversion indicative of the Warburg effect can be imaged without hyper-polarization with a time resolution of 12s and an overall spatial resolution that compares favorably to <sup>18</sup>F-FDG PET. Hyperpolarization (physics) ; Carbohydrate metabolism ; Metabolism ; Chemistry ; Nuclear magnetic resonance ; Biochemistry ; Nuclear magnetic resonance spectroscopy ; Physics ; Stereochemistry Advanced NMR Techniques and Applications ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies