jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew Tamasi | Roshan M. Patel | Carlos H. Borca | Shashank Kosuri | Heloise Mugnier | Rahul Upadhya | N. Sanjeeva Murthy | Michael A. Webb | Adam J. Gormley
Source: ChemRxiv (preprint, 2022-01-24)
Field: Materials Science — Materials Chemistry

Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids Polymer-protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by a vast chemical and composition space. Here, we report a strategy to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique chemistries for each enzyme. Overall, this work broadens our capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions towards the design of robust polymer-protein hybrid materials. Robustness (evolution) ; Rational design ; Copolymer ; Polymer ; Computer science ; Protein engineering ; Synthetic biology ; Biochemical engineering ; Nanotechnology ; Artificial intelligence ; Materials science ; Engineering ; Enzyme ; Chemistry ; Computational biology ; Biology ; Biochemistry ; Organic chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Software Engineering Research
Authors: Simone Giuseppe Giuffrida | Weronika Forysiak | Paweł Cwynar | Róża Szweda
Source: Polymers (review, 2022-01-31)
Field: Materials Science — Biomaterials

Shaping Macromolecules for Sensing Applications—From Polymer Hydrogels to Foldamers Sensors are tools for detecting, recognizing, and recording signals from the surrounding environment. They provide measurable information on chemical or physical changes, and thus are widely used in diagnosis, environment monitoring, food quality checks, or process control. Polymers are versatile materials that find a broad range of applications in sensory devices for the biomedical sector and beyond. Sensory materials are expected to exhibit a measurable change of properties in the presence of an analyte or a stimulus, characterized by high sensitivity and selectivity of the signal. Signal parameters can be tuned by material features connected with the restriction of macromolecule shape by crosslinking or folding. Gels are crosslinked, three-dimensional networks that can form cavities of different sizes and forms, which can be adapted to trap particular analytes. A higher level of structural control can be achieved by foldamers, which are macromolecules that can attain well-defined conformation in solution. By increasing control over the three-dimensional structure, we can improve the selectivity of polymer materials, which is one of the crucial requirements for sensors. Here, we discuss various examples of polymer gels and foldamer-based sensor systems. We have classified and described applied polymer materials and used sensing techniques. Finally, we deliberated the necessity and potential of further exploration of the field towards the increased selectivity of sensory devices. Macromolecule ; Polymer ; Self-healing hydrogels ; Analyte ; Foldamer ; Nanotechnology ; Materials science ; Folding (DSP implementation) ; Selectivity ; Computer science ; Biological system ; Chemistry ; Organic chemistry ; Mechanical engineering ; Polymer chemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Bruno Albuquerque de Castro | Vitor Vecina dos Santos | Guilherme Beraldi Lucas | Jorge Alfredo Ardila‐Rey | Rudolf Ribeiro Riehl | André Luiz Andreoli
Source: Sensors (article, 2022-02-22)
Field: Materials Science — Materials Chemistry

A Comparative Analysis Applied to the Partial Discharges Identification in Dry-Type Transformers by Hall and Acoustic Emission Sensors Dry-type insulated transformers stand out for their higher applicability in substations, high-voltage instrumentation systems, and electrical installations. In this machine, the insulation system is constituted of dielectric materials such as epoxy resin and Nomex paper. Some critical issues in the operation of this equipment, such as overload, moisture, or heat, can induce a slow degradation of the physical-chemical properties of the dielectric materials, which can culminate in the total failure of the transformer. However, before the transformer's shutdown, it is common to detect discharge activity in the insulation system. Based on this issue, this work proposes an experimental and comparative analysis between acoustic emission and Hall-effect sensors, aiming at differentiating discharges in epoxy resin and Nomex paper, materials that constitute the insulation of the dry-type insulated transformers. Two signal processing techniques were studied: traditional frequency analysis and discrete wavelet transform. The objective is to develop signal processing techniques to differentiate each type of discharge since different discharges require different maintenance actions. The results obtained indicate that acoustic emission sensors and Hall sensors are promising in differentiating discharge in epoxy resin and Nomex paper. Furthermore, the pattern recognition tools presented by this work, which associated the wavelet levels energies and the energy of the full signals with the average band and the equivalent bandwidth, were effective to perform feature extraction of power transformer condition. Acoustic emission ; Transformer ; Partial discharge ; Epoxy ; Wavelet ; Materials science ; Electrical engineering ; Signal processing ; Engineering ; Acoustics ; Voltage ; Electronic engineering ; Composite material ; Computer science ; Digital signal processing High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Xiaoliang Li | Lanyue Hu | Chengning Tan | Xiaojie Wang | Qian Ran | Li Chen | Zhongjun Li
Source: Frontiers in Oncology (review, 2022-10-06)
Field: Materials Science — Biomaterials

Platelet-promoting drug delivery efficiency for inhibition of tumor growth, metastasis, and recurrence Nanomedicines are considered one of the promising strategies for anticancer therapy; however, the low targeting efficiency of nanomedicines <i>in vivo</i> is a great obstacle to their clinical applications. Camouflaging nanomedicines with either platelet membrane (PM) or platelet would significantly prolong the retention time of nanomedicines in the bloodstream, enhance the targeting ability of nanomedicines to tumor cells, and reduce the off-target effect of nanomedicines in major organs during the anticancer treatment. In the current review, the advantages of using PM or platelet as smart carriers for delivering nanomedicines to inhibit tumor growth, metastasis, and recurrence were summarized. The opportunities and challenges of this camouflaging strategy for anticancer treatment were also discussed. Nanomedicine ; Medicine ; Metastasis ; Drug delivery ; Cancer research ; Drug ; In vivo ; Platelet ; Pharmacology ; Cancer ; Immunology ; Internal medicine ; Nanotechnology ; Materials science ; Nanoparticle ; Biology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Inflammatory Biomarkers in Disease Prognosis
Authors: Huyen Bui | Daniel Levacher | Mohamed Boutouil | Nassim Sebaïbi
Source: Journal of Composites Science (article, 2022-03-25)
Field: Materials Science — Polymers and Plastics

Effects of Wetting and Drying Cycles on Microstructure Change and Mechanical Properties of Coconut Fibre-Reinforced Mortar Natural fibre-reinforced cementitious composites are commonly used as outer construction materials. They usually suffer weather as a result of being expose to various types of climates. In this study, a series of experimental tests were carried out to investigate the deterioration mechanism and mechanical properties of mortars incorporating coconut fibres due to repeated wetting and drying. The results indicated that although the compressive strength was found to increase after the first cycle, both compressive and flexural strengths underwent a significant decrease in the fifth cycle. In addition, at high temperatures, mortar matrixes retain their stable structure, according to the results of TGA analysis. When wetting and drying curing was applied, there was a significant degradation of fibres in the mortar. Mortar ; Materials science ; Composite material ; Wetting ; Cementitious ; Microstructure ; Curing (chemistry) ; Compressive strength ; Flexural strength ; Cement Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Structural Behavior of Reinforced Concrete
Authors: James P. Andrews | Olga Gkountouna | Estela Blaisten‐Barojas
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning Machine learning techniques including neural networks are popular tools for chemical, physical and materials applications searching for viable alternative methods in the analysis of structure and energetics of systems ranging from crystals to biomolecules. Efforts are less abundant for prediction of kinetics and dynamics. Here we explore the ability of three well established recurrent neural network architectures for reproducing and forecasting the energetics of a liquid solution of ethyl acetate containing a macromolecular polymer-lipid aggregate at ambient conditions. Data models from three recurrent neural networks, ERNN, LSTM and GRU, are trained and tested on half million points time series of the macromolecular aggregate potential energy and its interaction energy with the solvent obtained from molecular dynamics simulations. Our exhaustive analyses convey that the recurrent neural network architectures investigated generate data models that reproduce excellently the time series although their capability of yielding short or long term energetics forecasts with expected statistical distributions of the time points is limited. We propose an <i>in silico</i> protocol by extracting time patterns of the original series and utilizing these patterns to create an ensemble of artificial network models trained on an ensemble of time series seeded by the additional time patters. The energetics forecast improve, predicting a band of forecasted time series with a spread of values consistent with the molecular dynamics energy fluctuations span. Although the distribution of points from the band of energy forecasts is not optimal, the proposed <i>in silico</i> protocol provides useful estimates of the solvated macromolecular aggregate fate. Given the growing application of artificial networks in materials design, the data-based protocol presented here expands the realm of science areas where supervised machine learning serves as a decision making tool aiding the simulation practitioner to assess when long simulations are worth to be continued. Computer science ; Energetics ; Artificial neural network ; Aggregate (composite) ; Artificial intelligence ; Series (stratigraphy) ; Machine learning ; Time series ; Biological system ; Nanotechnology ; Materials science ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shuhao Zhang | Małgorzata Z. Makoś | Ryan B. Jadrich | Elfi Kraka | Kipton Barros | Benjamin Nebgen | Sergei Tretiak | Olexandr Isayev | Nicholas Lubbers | Richard A. Messerly | Justin S. Smith
Source: ChemRxiv (preprint, 2022-11-03)
Field: Materials Science — Materials Chemistry

Exploring the frontiers of chemistry with a generalreactive machine learning potential Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery. Machine learning interatomic potentials (MLIP) have become an efficient alternative to computationally expensive quantum chemistry simulations. In practice, reactive MLIPs require refitting to extensive datasets for each new application, and prior knowledge of reaction networks is required to generate fitting data. In this work, we develop a general reactive MLIP through unbiased active learning with a nanoreactor molecular dynamics inspired sampler. The resulting potential (ANI-nr) is then applied to study five distinct condensed phase reactive chemistry problems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-nr closely matches experiment and/or previous studies using traditional model chemistry methods, without needing to be refit for each application, which enables high-throughput in silico reactive chemistry experimentation. Reactive intermediate ; Nanoreactor ; Chemistry ; Quantum chemistry ; Molecular machine ; Molecular dynamics ; Nanotechnology ; Computer science ; Biochemical engineering ; Computational chemistry ; Molecule ; Materials science ; Organic chemistry ; Catalysis ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Software Engineering Research
Authors: Matthew S. Johnson | William H. Green
Source: ChemRxiv (preprint, 2022-07-06)
Field: Materials Science — Materials Chemistry

A Machine Learning Based Approach to Reaction Rate Estimation Chemical kinetic models are vital to accurately predicting phenomena in a wide variety of fields from combustion to atmospheric chemistry to electrochemistry. However, building an accurate chemical kinetic model requires the efficient and accurate estimation of many reaction rate coefficients for many reaction classes with highly variable amounts of available training data. Current techniques for fast automatic rate estimation tend to be poorly optimized and incredibly tedious to maintain and extend. We have developed a machine learning algorithm for automatically training subgraph isomorphic decision trees (SIDT) to predict rate coefficients for arbitrary reaction types. This method is fully automatic, scalable to virtually any dataset size, human readable, can incorporate qualitative chemical knowledge from experts and provides detailed uncertainty information for estimates. The accuracy of the algorithm is tested against the state of the art rate rules scheme in the RMG-database for five selected reaction families. The SIDT method is shown to significantly improve estimation accuracy across all reaction families and considered statistics. The estimator uncertainty estimates are validated against actual errors. Estimator ; Computer science ; Scalability ; Machine learning ; Reaction rate ; Estimation ; Artificial intelligence ; Algorithm ; Data mining ; Mathematics ; Statistics ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Zhuoying Jiang | Jiajie Hu | Anna Cristina S. Samia | Xiong Yu
Source: Catalysts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Predicting Active Sites in Photocatalytic Degradation Process Using an Interpretable Molecular-Image Combined Convolutional Neural Network Machine-learning models have great potential to accelerate the design and performance assessment of photocatalysts, leveraging their unique advantages in detecting patterns and making predictions based on data. However, most machine-learning models are “black-box” models due to lack of interpretability. This paper describes the development of an interpretable neural-network model on the performance of photocatalytic degradation of organic contaminants by TiO2. The molecular structures of the organic contaminants are represented by molecular images, which are subsequently encoded by feeding into a special convolutional neural network (CNN), EfficientNet, to extract the critical structural features. The extracted features in addition to five other experimental variables were input to a neural network that was subsequently trained to predict the photodegradation reaction rates of the organic contaminants by TiO2. The results show that this machine-learning (ML) model attains a higher accuracy to predict the photocatalytic degradation rate of organic contaminants than a previously developed machine-learning model that used molecular fingerprint encoding. In addition, the most relevant regions in the molecular image affecting the photocatalytic rates can be extracted with gradient-weighted class activation mapping (Grad-CAM). This interpretable machine-learning model, leveraging the graphic interpretability of CNN model, allows us to highlight regions of the molecular structure serving as the active sites of water contaminants during the photocatalytic degradation process. This provides an important piece of information to understand the influence of molecular structures on the photocatalytic degradation process. Interpretability ; Convolutional neural network ; Artificial intelligence ; Computer science ; Photodegradation ; Degradation (telecommunications) ; Artificial neural network ; Photocatalysis ; Deep learning ; Machine learning ; Process (computing) ; Pattern recognition (psychology) ; Biological system ; Chemistry ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Mark Zaretckii | Inga Bashkirova | Sergey Osipenko | Yury Kostyukevich | Е. Н. Николаев | Petr Popov
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

3D chemical structures allow robust deep learning models for retention time prediction We present a robust deep learning method CPORT to predict retention time from 3D molecular structures. It generates 4D tensor representations of 3D conformers, that are processed by a neural network with 3D convolutional and fully-connected layers. Deep learning ; Convolutional neural network ; Computer science ; Artificial intelligence ; Retention time ; Tensor (intrinsic definition) ; Pattern recognition (psychology) ; Chemistry ; Mathematics ; Chromatography ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ziyuan Rao | PoYen Tung | Ruiwen Xie | Wei Ye | Hongbin Zhang | Alberto Ferrari | T.P.C. Klaver | Fritz Körmann | Prithiv Thoudden Sukumar | Alisson Kwiatkowski da Silva | Yao Chen | Zhiming Li | Dirk Ponge | Jörg Neugebauer | Oliver Gutfleisch | Sebastian Bauer | Dierk Raabe
Source: arXiv (Cornell University) (preprint, 2022-02-28)
Field: Materials Science — Materials Chemistry

Machine learning-enabled high-entropy alloy discovery High-entropy alloys are solid solutions of multiple principal elements, capable of reaching composition and feature regimes inaccessible for dilute materials. Discovering those with valuable properties, however, relies on serendipity, as thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. Here, we propose an active-learning strategy to accelerate the design of novel high-entropy Invar alloys in a practically infinite compositional space, based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys (out of millions of possible compositions), we identified 2 high-entropy Invar alloys with extremely low thermal expansion coefficients around 2*10-6 K-1 at 300 K. Our study thus opens a new pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic and electrical properties. High entropy alloys ; Invar ; Entropy (arrow of time) ; Alloy ; Statistical physics ; Materials science ; Computer science ; Thermodynamics ; Artificial intelligence ; Physics ; Metallurgy Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced Materials Characterization Techniques
Authors: Satoshi Noguchi | Hui Wang | Junya Inoue
Source: Scientific Reports (article, 2022-08-20)
Field: Materials Science — Metals and Alloys

Identification of microstructures critically affecting material properties using machine learning framework based on metallurgists’ thinking process In materials science, machine learning has been intensively researched and used in various applications. However, it is still far from achieving intelligence comparable to that of human experts in terms of creativity and explainability. In this paper, we investigate whether machine learning can acquire explainable knowledge without directly introducing problem-specific information such as explicit physical mechanisms. In particular, a potential of machine learning to obtain the capability to identify a part of material structures that critically affects a physical property without human prior knowledge is mainly discussed. The guide for constructing the machine learning framework adopted in this paper is to imitate human researchers' process of thinking in the interpretation and development of materials. Our framework was applied to the optimization of structures of artificial dual-phase steels in terms of a fracture property. A comparison of results of the framework with those of numerical simulation based on governing physical laws demonstrated the potential of our framework for the identification of a part of microstructures critically affecting the target property. Consequently, this implies that our framework can implicitly acquire an intuition in a similar way that human researchers empirically attain the general strategy for material design consistent with the physical background. Identification (biology) ; Process (computing) ; Computer science ; Microstructure ; Materials science ; Metallurgy ; Biology Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels