jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Natsumi Fukaya | Soichiro Ogi | Hikaru Sotome | Kazuhiro J. Fujimoto | Takeshi Yanai | Nils Bäumer | Gustavo Fernández | Hiroshi Miyasaka | Shigehiro Yamaguchi
Source: Journal of the American Chemical Society (article, 2022-12-02)
Field: Materials Science — Materials Chemistry

Impact of Hydrophobic/Hydrophilic Balance on Aggregation Pathways, Morphologies, and Excited-State Dynamics of Amphiphilic Diketopyrrolopyrrole Dyes in Aqueous Media We report the thermodynamic and kinetic aqueous self-assembly of a series of amide-functionalized dithienyldiketopyrrolopyrroles (TDPPs) that bear various hydrophilic oligoethylene glycol (OEG) and hydrophobic alkyl chains. Spectroscopic and microscopic studies showed that the TDPP-based amphiphiles with an octyl group form sheet-like aggregates with J-type exciton coupling. The effect of the alkyl chains on the aggregated structure and the internal molecular orientation was examined via computational studies combining MD simulations and TD-DFT calculations. Furthermore, solvent and thermal denaturation experiments provided a state diagram that indicates the formation of unexpected nanoparticles during the self-assembly into nanosheets when longer OEG side chains are introduced. A kinetic analysis revealed that the nanoparticles were obtained selectively as an on-pathway intermediate state toward the formation of thermodynamically controlled nanosheets. The metastable aggregates were used for seed-initiated supramolecular assembly, which allowed establishing control over the assembly kinetics and the aggregate size. The sheet-like aggregates prepared using the seeding method exhibited coherent vibration in the excited state, indicating a well-ordered orientation of the TDPP units. These results underline the significance of fine tuning of the hydrophobic/hydrophilic balance in the molecular design to kinetically control the assembly of amphiphilic π-conjugated molecules into two-dimensional nanostructures in aqueous media. Chemistry ; Amphiphile ; Alkyl ; Hydrophobic effect ; Aqueous solution ; Supramolecular chemistry ; Self-assembly ; Micelle ; Side chain ; Supramolecular assembly ; Molecular dynamics ; Excited state ; Molecule ; Chemical engineering ; Polymer ; Copolymer ; Organic chemistry ; Computational chemistry Luminescence and Fluorescent Materials ; Supramolecular Self-Assembly in Materials ; Porphyrin and Phthalocyanine Chemistry
Authors: Lan Luo | Fenghua Zhang | Jinsong Leng
Source: Research (review, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Shape Memory Epoxy Resin and Its Composites: From Materials to Applications Shape memory polymers (SMPs) have historically attracted attention for their unique stimulation-responsive and variable stiffness and have made notable progress in aerospace, civil industry, and other fields. In particular, epoxy resin (EP) has great potential due to its excellent mechanical properties, fatigue resistance, and radiation resistance. Herein, we focus on the molecular design and network construction of shape memory epoxy resins (SMEPs) to provide opportunities for performance and functional regulation. Multifunctional and high-performance SMEPs are introduced in detail, including multiple SMEPs, two-way SMEPs, outstanding toughness, and temperature resistance. Finally, emerging applications of SMEPs and their composites in aerospace, four-dimensional printing, and self-healing are demonstrated. Based on this, we point out the challenges ahead and how SMEPs can integrate performance and versatility to meet the needs of technological development. Epoxy ; Aerospace ; Toughness ; Materials science ; Composite material ; Stiffness ; Shape-memory polymer ; Shape-memory alloy ; Heat resistance ; Mechanical engineering ; Aerospace engineering ; Engineering Polymer composites and self-healing ; Photochromic and Fluorescence Chemistry ; Silicone and Siloxane Chemistry
Authors: Luyao Wang | Qingbo Wang | Anna Slita | Oskar Backman | Zahra Gounani | Emil Rosqvist | Jouko Peltonen | Stefan Willför | Chunlin Xu | Jessica M. Rosenholm | Xiaoju Wang
Source: Green Chemistry (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Digital light processing (DLP) 3D-fabricated antimicrobial hydrogel with a sustainable resin of methacrylated woody polysaccharides and hybrid silver-lignin nanospheres Efficient antimicrobial hydrogels are fabricated by digital light processing (DLP) printing out of a sustainable resin, which is formulated with methacrylated O -acetyl-galactoglucomannan and lignin nanospheres decorated with silver nanoparticles. Lignin ; Self-healing hydrogels ; Materials science ; Antimicrobial ; Chemical engineering ; Silver nanoparticle ; Chemistry ; Polymer science ; Nanoparticle ; Polymer chemistry ; Nanotechnology ; Organic chemistry Nanoparticles: synthesis and applications ; 3D Printing in Biomedical Research ; Additive Manufacturing and 3D Printing Technologies
Authors: Maxim Ziatdinov | Yongtao Liu | Anna N. Morozovska | Eugene А. Eliseev | Xiaohang Zhang | Ichiro Takeuchi | Sergei V. Kalinin
Source: Advanced Materials (article, 2022-03-12)
Field: Materials Science — Materials Chemistry

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. Here, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO<sub>3</sub> using piezoresponse force microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available. Workflow ; Probabilistic logic ; Reinforcement learning ; Active learning (machine learning) ; Computer science ; Materials science ; Experimental data ; Artificial intelligence ; Machine learning ; Gaussian process ; Gaussian ; Nanotechnology ; Biological system ; Physics ; Mathematics ; Biology Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Zheren Wang | Olga Kononova | Kevin Cruse | Tanjin He | Haoyan Huo | Yuxing Fei | Yan Zeng | Yingzhi Sun | Zijian Cai | Wenhao Sun | Gerbrand Ceder
Source: Scientific Data (article, 2022-05-25)
Field: Materials Science — Materials Chemistry

Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures. Heuristics ; Computer science ; Construct (python library) ; Scale (ratio) ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Andrew F. Zahrt | Yiming Mo | Kakasaheb Y. Nandiwale | Ron Shprints | Esther Heid | Klavs F. Jensen
Source: Journal of the American Chemical Society (article, 2022-12-02)
Field: Materials Science — Materials Chemistry

Machine-Learning-Guided Discovery of Electrochemical Reactions The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico, and the predictions are used to identify a number of reactions of synthetic or mechanistic interest, 80% of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating the development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry. Workflow ; Chemistry ; Representation (politics) ; Organic molecules ; In silico ; Biochemical engineering ; Set (abstract data type) ; Field (mathematics) ; Artificial intelligence ; Cheminformatics ; Computer science ; Nanotechnology ; Combinatorial chemistry ; Molecule ; Computational chemistry ; Database ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ravithree D. Senanayake | Xiaoxiao Yao | Clarice E. Froehlich | Meghan S. Cahill | Trever R. Sheldon | Mary McIntire | Christy L. Haynes | Rigoberto Hernandez
Source: Journal of Chemical Information and Modeling (article, 2022-11-17)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Carbon Dot Synthesis: Prediction of Emission Color and Wavelength Carbon dots (CDs) have attracted great attention in a range of applications due to their bright photoluminescence, high photostability, and good biocompatibility. However, it is challenging to design CDs with specific emission properties because the syntheses involve many parameters, and it is not clear how each parameter influences the CD properties. To help bridge this gap, machine learning, specifically an artificial neural network, is employed in this work to characterize the impact of synthesis parameters on and make predictions for the emission color and wavelength for CDs. The machine reveals that the choice of reaction method, purification method, and solvent relate more closely to CD emission characteristics than the reaction temperature or time, which are frequently tuned in experiments. After considering multiple models, the best performing machine learning classification model achieved an accuracy of 94% in predicting relative to actual color. In addition, hybrid (two-stage) models incorporating both color classification and an artificial neural network <i>k</i>-ensemble model for wavelength prediction through regression performed significantly better than either a standard artificial neural network or a single-stage artificial neural network <i>k</i>-ensemble regression model. The accuracy of the model predictions was evaluated against CD emission wavelengths measured from experiments, and the minimum mean average error is 25.8 nm. Overall, the models developed in this work can effectively predict the photoluminescence emission of CDs and help design CDs with targeted optical properties. Wavelength ; Carbon fibers ; Materials science ; Computer science ; Optoelectronics ; Artificial intelligence ; Nanotechnology ; Composite material Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Yaoyi Chen | Andreas Krämer | Nicholas E. Charron | Brooke E. Husic | Cecilia Clementi | Frank Noé
Source: Refubium (Universitätsbibliothek der Freien Universität Berlin) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning implicit solvation for molecular dynamics Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or certain physical properties compared to explicit solvent models as the many-body effects of the neglected solvent molecules are difficult to model as a mean field. Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data. Following the previous ML–CG models CGnet and CGSchnet, we introduce ISSNet, a graph neural network, to model the implicit solvent potential of mean force. ISSNet can learn from explicit solvent simulation data and be readily applied to molecular dynamics simulations. We compare the solute conformational distributions under different solvation treatments for two peptide systems. The results indicate that ISSNet models can outperform widely used generalized Born and surface area models in reproducing the thermodynamics of small protein systems with respect to explicit solvent. The success of this novel method demonstrates the potential benefit of applying machine learning methods in accurate modeling of solvent effects for in silico research and biomedical applications. Solvation ; Leverage (statistics) ; Molecular dynamics ; Solvent ; Solvent models ; Computer science ; Artificial neural network ; Implicit solvation ; Force field (fiction) ; Machine learning ; Statistical physics ; Artificial intelligence ; Chemistry ; Computational chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Venkat Kapil | Edgar A. Engel
Source: Proceedings of the National Academy of Sciences (article, 2022-02-07)
Field: Chemistry — Organic Chemistry

A complete description of thermodynamic stabilities of molecular crystals Predictions of relative stabilities of (competing) molecular crystals are of great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge for modeling, as often minuscule free energy differences are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations for <i>general</i> molecular compounds, which simultaneously account for all effects, have hitherto not been computationally viable. Here we present an efficient "end to end" framework that seamlessly combines state-of-the art electronic structure calculations, machine-learning potentials, and advanced free energy methods to calculate ab initio Gibbs free energies for general organic molecular materials. The facile generation of machine-learning potentials for a diverse set of polymorphic compounds-benzene, glycine, and succinic acid-and predictions of thermodynamic stabilities in qualitative and quantitative agreement with experiments highlight that predictive thermodynamic studies of industrially relevant molecular materials are no longer a daunting task. Thermodynamics ; Materials science ; Statistical physics ; Physics Chemical Thermodynamics and Molecular Structure ; Crystallography and molecular interactions ; Thermal and Kinetic Analysis
Authors: Tianxing Da | Hui-Kang Ren | Wen-ke He | Siyi Gong | Tao Chen
Source: Journal of environmental chemical engineering (article, 2022-08-19)
Field: Chemistry — Inorganic Chemistry

Prediction of uranium adsorption capacity on biochar by machine learning methods Biochar ; Uranium ; Adsorption ; Environmental science ; Chemistry ; Materials science ; Metallurgy ; Organic chemistry Radioactive element chemistry and processing ; Radioactivity and Radon Measurements ; Geochemistry and Geologic Mapping
Authors: Aditya Nandy | Chenru Duan | Conrad Goffinet | Heather J. Kulik
Source: JACS Au (article, 2022-04-27)
Field: Materials Science — Materials Chemistry

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts Despite decades of effort, no earth-abundant homogeneous catalysts have been discovered that can selectively oxidize methane to methanol. We exploit active learning to simultaneously optimize methane activation and methanol release calculated with machine learning-accelerated density functional theory in a space of 16 M candidate catalysts including novel macrocycles. By constructing macrocycles from fragments inspired by synthesized compounds, we ensure synthetic realism in our computational search. Our large-scale search reveals that low-spin Fe(II) compounds paired with strong-field (e.g., P or S-coordinating) ligands have among the best energetic tradeoffs between hydrogen atom transfer (HAT) and methanol release. This observation contrasts with prior efforts that have focused on high-spin Fe(II) with weak-field ligands. By decoupling equatorial and axial ligand effects, we determine that negatively charged axial ligands are critical for more rapid release of methanol and that higher-valency metals [i.e., M(III) vs M(II)] are likely to be rate-limited by slow methanol release. With full characterization of barrier heights, we confirm that optimizing for HAT does not lead to large oxo formation barriers. Energetic span analysis reveals designs for an intermediate-spin Mn(II) catalyst and a low-spin Fe(II) catalyst that are predicted to have good turnover frequencies. Our active learning approach to optimize two distinct reaction energies with efficient global optimization is expected to be beneficial for the search of large catalyst spaces where no prior designs have been identified and where linear scaling relationships between reaction energies or barriers may be limited or unknown. Catalysis ; Methanol ; Chemistry ; Methane ; Density functional theory ; Ligand (biochemistry) ; Chemical physics ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Taiwo W. Quadri | Lukman O. Olasunkanmi | Omolola E. Fayemi | Hassane Lgaz | Omar Dagdag | El‐Sayed M. Sherif | Awad A. Alrashdi | Ekemini D. Akpan | Han‐Seung Lee | Eno E. Ebenso
Source: Arabian Journal of Chemistry (article, 2022-03-30)
Field: Chemistry — Organic Chemistry

Computational insights into quinoxaline-based corrosion inhibitors of steel in HCl: Quantum chemical analysis and QSPR-ANN studies The inhibition of mild steel deterioration via organic substances has become popular nowadays. Among the myriads of organic substances applied as potential inhibitors, quinoxalines stand out as toxic-free, cheap and effective compounds in different electrolytes. This report investigates the computational aspects of selected quinoxaline compounds tested as suppressors of mild steel deterioration in HCl medium using quantum chemical method (Density Functional Theory, DFT) and quantitative structure property relationship (QSPR). Feature selection tool was utilized to choose five top molecular descriptors (constitutional indices) that were used to characterize the quinoxaline molecules. Linear (ordinary least squares regression) and nonlinear (artificial neural network) modelling were adopted to correlate the selected constitutional indices of the studied quinoxalines with their experimental inhibition performances. The nonlinear model showed better performance as shown by the obtained results; RMSE of 5.4160, MSE of 29.3336, MAD of 2.3816 and MAPE of 5.0389. The developed models were utilized to determine the inhibition performances of ten new quinoxaline-based corrosion inhibitors which showed excellent inhibition performances of 87.88 to 95.73%. Quinoxaline ; Chemistry ; Quantitative structure–activity relationship ; Quantum chemical ; Density functional theory ; Computational chemistry ; Biological system ; Artificial neural network ; Corrosion ; Linear regression ; Quantum ; Molecular descriptor ; Molecule ; Organic chemistry ; Artificial intelligence ; Machine learning ; Stereochemistry ; Computer science ; Quantum mechanics Synthesis and Biological Evaluation ; Chemistry and Chemical Engineering ; Corrosion Behavior and Inhibition
Authors: Hyunsoo Park | Yeonghun Kang | Wonyoung Choe | Jihan Kim
Source: Journal of Chemical Information and Modeling (article, 2022-02-23)
Field: Chemistry — Inorganic Chemistry

Mining Insights on Metal–Organic Framework Synthesis from Scientific Literature Texts Identifying optimal synthesis conditions for metal-organic frameworks (MOFs) is a major challenge that can serve as a bottleneck for new materials discovery and development. A trial-and-error approach that relies on a chemist's intuition and knowledge has limitations in efficiency due to the large MOF synthesis space. To this end, 46,701 MOFs were data mined using our in-house developed code to extract their synthesis information from 28,565 MOF papers. The joint machine-learning/rule-based algorithm yields an average F1 score of 90.3% across different synthesis parameters (i.e., metal precursors, organic precursors, solvents, temperature, time, and composition). From this data set, a positive-unlabeled learning algorithm was developed to predict the synthesis of a given MOF material using synthesis conditions as inputs, and this algorithm successfully predicted successful synthesis in 83.1% of the synthesized data in the test set. Finally, our model correctly predicted three amorphous MOFs (with their representative experimental synthesis conditions) as having low synthesizability scores, while the counterpart crystalline MOFs showed high synthesizability scores. Our results show that big data extracted from the texts of MOF papers can be used to rationally predict synthesis conditions for these materials, which can accelerate the speed in which new MOFs are synthesized. Bottleneck ; Computer science ; Intuition ; Metal-organic framework ; Chemical space ; Organic synthesis ; Algorithm ; Artificial intelligence ; Data mining ; Machine learning ; Chemistry ; Organic chemistry ; Drug discovery Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography