jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Riley J. Hickman | Matteo Aldeghi | Florian Häse | Alán Aspuru‐Guzik
Source: arXiv (Cornell University) (preprint, 2022-03-29)
Field: Materials Science — Materials Chemistry

Bayesian optimization with known experimental and design constraints for chemistry applications Optimization strategies driven by machine learning, such as Bayesian optimization, are being explored across experimental sciences as an efficient alternative to traditional design of experiment. When combined with automated laboratory hardware and high-performance computing, these strategies enable next-generation platforms for autonomous experimentation. However, the practical application of these approaches is hampered by a lack of flexible software and algorithms tailored to the unique requirements of chemical research. One such aspect is the pervasive presence of constraints in the experimental conditions when optimizing chemical processes or protocols, and in the chemical space that is accessible when designing functional molecules or materials. Although many of these constraints are known a priori, they can be interdependent, non-linear, and result in non-compact optimization domains. In this work, we extend our experiment planning algorithms Phoenics and Gryffin such that they can handle arbitrary known constraints via an intuitive and flexible interface. We benchmark these extended algorithms on continuous and discrete test functions with a diverse set of constraints, demonstrating their flexibility and robustness. In addition, we illustrate their practical utility in two simulated chemical research scenarios: the optimization of the synthesis of o-xylenyl Buckminsterfullerene adducts under constrained flow conditions, and the design of redox active molecules for flow batteries under synthetic accessibility constraints. The tools developed constitute a simple, yet versatile strategy to enable model-based optimization with known experimental constraints, contributing to its applicability as a core component of autonomous platforms for scientific discovery. Computer science ; Chemical space ; Bayesian optimization ; Robustness (evolution) ; Flexibility (engineering) ; A priori and a posteriori ; Mathematical optimization ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Vineeth Venugopal | Sumit Pai | Elsa Olivetti
Source: arXiv (Cornell University) (preprint, 2022-10-31)
Field: Materials Science — Materials Chemistry

MatKG: The Largest Knowledge Graph in Materials Science -- Entities, Relations, and Link Prediction through Graph Representation Learning This paper introduces MatKG, a novel graph database of key concepts in material science spanning the traditional material-structure-property-processing paradigm. MatKG is autonomously generated through transformer-based, large language models and generates pseudo ontological schema through statistical co-occurrence mapping. At present, MatKG contains over 2 million unique relationship triples derived from 80,000 entities. This allows the curated analysis, querying, and visualization of materials knowledge at unique resolution and scale. Further, Knowledge Graph Embedding models are used to learn embedding representations of nodes in the graph which are used for downstream tasks such as link prediction and entity disambiguation. MatKG allows the rapid dissemination and assimilation of data when used as a knowledge base, while enabling the discovery of new relations when trained as an embedding model. Computer science ; Embedding ; Knowledge graph ; Knowledge base ; Visualization ; Theoretical computer science ; Schema (genetic algorithms) ; Graph ; Information retrieval ; Natural language processing ; Artificial intelligence ; Data mining Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Data Quality and Management
Authors: Nurul Anis Athirah Ab Aziz | Chin Wei Lai | Boon Hoong Ong
Source: JOURNAL OF RENEWABLE MATERIALS (review, 2022-11-24)
Field: Materials Science — Materials Chemistry

A Review of Graphene Oxide Crosslinking as Enhanced Corrosion Shield Application Nowadays, corrosion is not only undesirable, but it also has a significant influence on the industrial sectors and technical innovations that have demand for metals. The global economic damage is expected to reach $2.5 trill... | Find, read and cite all the research you need on Tech Science Press Epoxy ; Materials science ; Corrosion ; Oxide ; Coating ; Curing (chemistry) ; Graphene ; Polymer ; Surface modification ; Nanocomposite ; Composite material ; Metallurgy ; Nanotechnology ; Chemical engineering Corrosion Behavior and Inhibition ; Graphene research and applications ; Conducting polymers and applications
Authors: Cuong Viet Bui | Thomas Rosenau | Hubert Hettegger
Source: Cellulose (article, 2022-11-10)
Field: Chemistry — Spectroscopy

Immobilization of a cellulose carbamate-type chiral selector onto silica gel by alkyne-azide click chemistry for the preparation of chiral stationary chromatography phases Abstract A new synthesis strategy for the preparation of cellulose derivative-based chiral selectors and the subsequent mild immobilization onto pre-functionalized silica gel are introduced, utilizing Cu(I)-catalyzed alkyne-azide Huisgen cycloaddition (“ click ”) chemistry. A cellulose 3,5-dimethylphenyl carbamate derivative carrying propynyl carbamate groups was prepared by a combination of carbonate aminolysis and isocyanate chemistry. For immobilization, 3-azidopropyl-functionalized silica gel as an inert carrier was used, synthesized via a 3-chloropropyl intermediate. The chiral selector, as well as the inorganic/organic hybrid materials (silica gel/chiral selector), were comprehensively characterized by ATR-FTIR, solid-state 13 C and 29 Si NMR, liquid-state NMR, GPC, TGA, and elemental analysis. The enantioseparation performance of the immobilized-type chiral stationary phase was evaluated by HPLC with a set of representative chiral test analytes and different eluents and compared to a respective coated-type (=non-covalently bound) chiral stationary phase carrying the same selector quality and quantity on the same silica gel matrix. The immobilization did not adversely affect the chiral separation performance; on the contrary, in some chromatographic separations the immobilized-type chiral stationary phase surprisingly even surpassed the coated reference material. Graphical abstract Click chemistry ; Silica gel ; Cellulose ; Isocyanate ; Chemistry ; Azide ; Cycloaddition ; Covalent bond ; Organic chemistry ; Chiral column chromatography ; High-performance liquid chromatography ; Combinatorial chemistry ; Chromatography ; Catalysis ; Polyurethane Analytical Chemistry and Chromatography ; Chemical Synthesis and Analysis ; Microfluidic and Capillary Electrophoresis Applications
Authors: M. P. Christophliemk | Anne Heponiemi | Tao Hu | Ulla Lassi
Source: Topics in Catalysis (article, 2022-12-26)
Field: Materials Science — Materials Chemistry

Preparation of Porous and Durable Metakaolin-Based Alkali-Activated Materials with Active Metal as Composites for Catalytic Wet Air Oxidation Abstract Novel porous and durable metakaolin-based alkali-activated materials (MK-AAMs) with active metal as composites were produced to degrade bisphenol A (BPA) in catalytic wet air oxidation (CWAO). Two composite producing processes were employed. The first process consisted of mixing metakaolin (MK), a foaming agent and active metal oxide (CuO, MnO 2 ) in a strongly alkaline solution of K 2 SiO 3 and KOH. Paste was cured under microwave radiation to produce porous CuO and MnO 2 composites. A porous blank MK-AAM was produced as described above but without active metal and was used as a reference as well. Cu(OH) 2 composite was produced by refluxing a blank MK-AAM in 0.5 M CuSO 4 solution for 24 h. The specific surface area (SSA) of the reference, CuO, MnO 2 , and Cu(OH) 2 composites were 36, 53, 61, 89 m 2 /g, respectively. Mechanical durability was determined in terms of compressive strength and 2.8, 3.4, 3.2, 3.6 MPa were received, respectively. The activity of the reference and the composites were tested in CWAO at 1 MPa and 150 °C for 5 h by using an aqueous model solution of BPA. Under the optimal conditions for CWAO (pressure: 1 MPa; temperature: 150 °C; initial pH 5–6; c[catalyst]: 4.0 g/L) with Cu(OH) 2 composite, the BPA and total organic carbon (TOC) conversions of 100% and 53% were reached. During 5 h oxidation, the composites degraded due to the combined effect of erosion (1.5 wt%) and active metal (Cu, Mn) leaching (1.1 wt%, 3.6 wt%). It was proposed that BPA can be degraded energy-efficiently via CWAO into less harmful compounds under mild reaction conditions without losing the desired properties of the composites. Metakaolin ; Catalysis ; Materials science ; Wet oxidation ; Composite material ; Composite number ; Porosity ; Alkali metal ; Activated carbon ; Aqueous solution ; Oxide ; Metal ; Compressive strength ; Chemical engineering ; Nuclear chemistry ; Chemistry ; Adsorption ; Organic chemistry ; Metallurgy Magnesium Oxide Properties and Applications ; Layered Double Hydroxides Synthesis and Applications ; Concrete and Cement Materials Research
Authors: Mississippi Missouri Bhunia | Kalyan Kumar Chattopadhyay | Paramita Chattopadhyay
Source: ChemRxiv (preprint, 2022-03-03)
Field: Materials Science — Materials Chemistry

Ultra-stable Transformer Oil nanofluids with significant AC Breakdown Voltage enhancement at ultra-low filler fraction of functionalized boron nitride nanosheets This paper reports the purposeful fluorination of hexagonal boron nitride (h-BN) nanofillers and its impact on reinforcement of AC breakdown strength and stability of transformer oil (TO) nanofluid. Fluorine functionalized boron nitride nanosheets (f-BNNs) of ˜5 nm thickness was synthesized in house via wet synthetic exfoliation route of pristine h-BN utilizing Ammonium Fluoride (NH4F) as the shedding agent. This promotes attachment of some highly electro- negative fluorine atoms to boron. This tailored f-BNNs exhibit a diminished band gap and induced electrical conductivity which helps in elevating the AC breakdown Voltage to 26 %- 20% and a surge in resistivity at appreciably low nanofiller fraction of 0.005-0.01wt. %. These noteworthy improvement of electrical insulation properties compared to the state-of-the-art Boron nitride nanoparticles or nanosheets is explained by the parallel role played by fluorine in charge trapping as well as the role played 2D morphology of the nanofillers. Here, fluorine facilitated extrinsic energy bands in the oil-nanofiller interface acted as efficient charge trapping sites and helped to accumulate large quantities of streamer charges, more than h-BN nanosheets or BN nanoparticle for a longer time and improved the electric insulation properties to a large extent. The ultra-high steadiness of the nanofluid is also observed at these lower filler concentrations. 2D morphology, lipophilicity and electro-negativity induced electrostatic repulsion between the f-BNNs nanosheets are attributed to achieve this alluring property of the nanofluid. Such significant improvements at very low filler fractions justifies the fluorination of hexagonal boron nitride as a novel idea and a better alternative among all the reported BN brothers for high voltage applications of nano engineered liquid insulation Boron nitride ; Materials science ; Exfoliation joint ; Composite material ; Chemical engineering ; Nanofluid ; Boron ; Fluorine ; Nanoparticle ; Nitride ; Nanotechnology ; Organic chemistry ; Graphene ; Chemistry ; Metallurgy ; Layer (electronics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Semiconductor materials and devices
Authors: Kulpavee Jitapunkul | Apiphu Chenwittayakhachon | Pawin Iamprasertkun
Source: Frontiers in Materials (article, 2022-11-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transition of electrochemical measurement to machine learning in the perspective of two-dimensional materials Two-dimensional materials (e.g. graphene, and transition metal dichalcogenides) have become ubiquitous in electrochemical contexts including energy storage, electrocatalyst, and ion-selective membranes. This is due to its superior electrochemical properties, specifically “capacitance”, which can be referred to the storage ions at the electrolyte/materials interfaces. Experimental work and computational chemistry were carried out in the past decade for solving and improving the understanding of two-dimensional materials; however, these techniques are relatively expensive, complex, and time-consuming. Therefore, we accentuate the future trend of two-dimensional material study with machine learning as the modest alternative. In this perspective, the intrinsic capacitance properties of the two dimension materials were described from an atomic level, explaining the heteroatom doping to a nanoscopic level, showing (basal vs edge capacitance). The studies also extended to the macroscopic level i.e., the flake size of the two-dimensional materials. We then shed more light on the applicability of machine learning coupled with the “fundamental measurement” for solving electrochemistry of two-dimensional materials. The shallow artificial neural network was demonstrated for the prediction of CV curves using the data from size-dependent graphene. In addition, the application of deep neural networks with complicated architecture has also been explored through the prediction of capacitance for heteroatom-doped graphene. This perspective provides a clear background and creates the connection between fundamental measurement and machine learning for understanding the capacitance properties of two-dimensional materials. Capacitance ; Heteroatom ; Materials science ; Nanotechnology ; Graphene ; Doping ; Electrolyte ; Computer science ; Electrode ; Optoelectronics ; Chemistry ; Physical chemistry ; Organic chemistry Supercapacitor Materials and Fabrication ; Graphene research and applications ; Conducting polymers and applications
Authors: Filip Ekström Kelvinius | Rickard Armiento | Fredrik Lindsten
Source: Physical Review Materials (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Graph-based machine learning beyond stable materials and relaxed crystal structures There has been a recent surge of interest in using machine learning to approximate density functional theory in materials science. However, many of the most performant models are evaluated on large databases of computed properties of, primarily, materials with precise atomic coordinates available, and which have been experimentally synthesized, i.e., which are thermodynamically stable or metastable. These aspects provide challenges when applying such models on theoretical candidate materials, for example for materials discovery, where the coordinates are not known. To extend the scope of this methodology, we investigate the performance of the crystal graph convolutional neural network on a data set of theoretical structures in three related ternary phase diagrams (Ti,Zr,Hf)-Zn-N, which thus include many highly unstable structures. We then investigate the impact on the performance of using atomic positions that are only partially relaxed into local energy minima. We also explore options for improving the performance in these scenarios by transfer learning, either from models trained on a large database of mostly stable systems, or a different but related phase diagram. Models pretrained on stable materials do not significantly improve performance, but models trained on similar data transfer very well. We demonstrate how our findings can be utilized to generate phase diagrams with a major reduction in computational effort. Maxima and minima ; Phase diagram ; Metastability ; Materials science ; Graph ; Computer science ; Convolutional neural network ; Ternary operation ; Reduction (mathematics) ; Machine learning ; Theoretical computer science ; Algorithm ; Phase (matter) ; Mathematics ; Physics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Gabriel Sardinha de Oliveira | Henrique Poltronieri Pacheco
Source: Molecular Catalysis (article, 2022-06-10)
Field: Materials Science — Materials Chemistry

CatS: A predictive and user-friendly framework based on machine learning models for the screening of heterogeneous catalysts Convolutional neural network ; Multidisciplinary approach ; Computer science ; Machine learning ; Work (physics) ; Artificial neural network ; Artificial intelligence ; Catalysis ; Chemistry ; Engineering ; Political science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Seungpyo Kang | Minseon Kim | Kyoungmin Min
Source: arXiv (Cornell University) (preprint, 2022-02-14)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries Li-Ion Solid-State Electrolytes (Li-SSEs) are a promising solution that resolves the critical issues of conventional Li-Ion Batteries (LIBs) such as poor ionic conductivity, interfacial instability, and dendrites growth. In this study, a platform consisting of a high-throughput screening and a machine-learning surrogate model for discovering superionic Li-SSEs among 20,237 Li-containing materials is developed. For the training database, the ionic conductivity of Na SuperIonic CONductor (NASICON) and Li SuperIonic CONductor (LISICON) type SSEs are obtained from the previous literature. Then, the chemical descriptor (CD) and additional structural properties are used as machine-readable features. Li-SSE candidates are selected through the screening criteria, and the prediction on the ionic conductivity of those is followed. Then, to reduce uncertainty in the surrogate model, the ensemble method by considering the best-performing two models is employed, whose mean prediction accuracy is 0.843 and 0.829, respectively. Furthermore, first-principles calculations are conducted for confirming the ionic conductivity of the strong candidates. Finally, six potential superionic Li-SSEs that have not previously been investigated are proposed. We believe that the constructed platform can accelerate the search for Li-SSEs with high ionic conductivity at minimum cost. Fast ion conductor ; Ionic conductivity ; Electrolyte ; Conductivity ; Ionic bonding ; Conductor ; Materials science ; Ion ; Computer science ; Chemistry ; Physical chemistry ; Electrode Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research
Authors: Evan Komp | Stéphanie Valleau
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Low-cost prediction of molecular and transition state partition functions <i>via</i> machine learning We have generated an open-source dataset of over 30 000 organic chemistry gas phase partition functions. With this data, a machine learning deep neural network estimator was trained to predict partition functions of unknown organic chemistry gas phase transition states. This estimator only relies on reactant and product geometries and partition functions. A second machine learning deep neural network was trained to predict partition functions of chemical species from their geometry. Our models accurately predict the logarithm of test set partition functions with a maximum mean absolute error of 2.7%. Thus, this approach provides a means to reduce the cost of computing reaction rate constants <i>ab initio</i>. The models were also used to compute transition state theory reaction rate constant prefactors and the results were in quantitative agreement with the corresponding <i>ab initio</i> calculations with an accuracy of 98.3% on the log scale. Partition (number theory) ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zhuole Lu | Parvin Adeli | Chae-Ho Yim | Ming Jiang | Jacob Rempel | Zhiwen Chen | Shwetank Yadav | Patrick H. J. Mercier | Yaser Abu‐Lebdeh | Chandra Veer Singh
Source: ACS Applied Energy Materials (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Automatically Capturing Key Features for Predicting Superionic Conductivity of Solid-State Electrolytes Using a Neural Network Solid-state batteries (SSBs) are one of the most promising energy storage technologies due to their low flammability and high energy density compared with currently used liquid-state batteries. The main obstacle to SSB development, however, is the large chemical design space for the solid-state electrolytes (SSEs), as it is significantly time-consuming to screen candidates experimentally or from first-principles simulations. Toward this end, machine learning (ML) offers an efficient strategy. However, current ML models use complex manually created features as inputs based on human intuition, which can introduce human bias, are potentially difficult to obtain for many materials, and can result in a cumbersome feature selection process. This work demonstrates that a neural network-based model utilizing only two simple elemental features (group and period) and one simple structural feature (coordination number) can provide excellent predictive performance comparable to previous manual feature-based studies, while automatically capturing any potential secondary features and reducing the need for human intervention in model training. Such a model is potentially more generalizable than manual feature-based models and can be even applied to other material property predictions, while greatly reducing complexity and training time. Computer science ; Artificial neural network ; Artificial intelligence ; Feature (linguistics) ; Process (computing) ; Machine learning ; Property (philosophy) ; Intuition ; Feature selection ; Obstacle Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies