jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Axel Straube | Francesca R. Novara
Source: Chemistry International (article, 2023-07-01)
Field: Chemistry — Physical and Theoretical Chemistry

Open Data Sesame! Key Takeaways from Chemistry Europe’s First Data Day And what do research professionals want? According to the recent Wiley society survey, many are looking for greater transparency-including enhanced communication with the general public and open peer review practices-and increased accessibility. Clear guidelines and technical support are wishes that come up repeatedly, but despite all support for the idea of Open Data, mandated data sharing is something that only a minority of the survey respondents would look forward to Key (lock) ; Chemistry ; Computer science History and advancements in chemistry ; Various Chemistry Research Topics ; Research Data Management Practices
Authors: Aleks Reinhardt | Pin Yu Chew | Bingqing Cheng
Source: arXiv (Cornell University) (preprint, 2023-08-21)
Field: Materials Science — Materials Chemistry

A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals Computing the solubility of crystals in a solvent using atomistic simulations is notoriously challenging due to the complexities and convergence issues associated with free-energy methods, as well as the slow equilibration in direct-coexistence simulations. This paper introduces a molecular-dynamics workflow that simplifies and robustly computes the solubility of molecular or ionic crystals. This method is considerably more straightforward than the state-of-the-art, as we have streamlined and optimised each step of the process. Specifically, we calculate the chemical potential of the crystal using the gas-phase molecule as a reference state, and employ the S0 method to determine the concentration dependence of the chemical potential of the solute. We use this workflow to predict the solubilities of sodium chloride in water, urea polymorphs in water, and paracetamol polymorphs in both water and ethanol. Our findings indicate that the predicted solubility is sensitive to the chosen potential energy surface. Furthermore, we note that the harmonic approximation often fails for both molecular crystals and gas molecules at or above room temperature, and that the assumption of an ideal solution becomes less valid for highly soluble substances. Solubility ; Molecular dynamics ; Molecule ; Crystal (programming language) ; Ionic bonding ; Solvent ; Chemistry ; Thermodynamics ; Chemical physics ; Materials science ; Physical chemistry ; Computer science ; Computational chemistry ; Physics ; Ion ; Organic chemistry Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Spectroscopy and Quantum Chemical Studies
Authors: Sean M. Stafford | Alexander A. Aduenko | Marcus Djokic | Yu-Hsiu Lin | José L. Mendoza-Cortés
Source: arXiv (Cornell University) (preprint, 2023-09-29)
Field: Materials Science — Materials Chemistry

Transforming Materials Discovery for Artificial Photosynthesis: High-Throughput Screening of Earth-Abundant Semiconductors We present a highly efficient workflow for designing semiconductor structures with specific physical properties, which can be utilized for a range of applications, including photocatalytic water splitting. Our algorithm generates candidate structures composed of earth-abundant elements that exhibit optimal light-trapping, high efficiency in \ce{H2} and/or \ce{O2} production, and resistance to reduction and oxidation in aqueous media. To achieve this, we use an ionic translation model trained on the Inorganic Crystal Structure Database (ICSD) to predict over thirty thousand undiscovered semiconductor compositions. These predictions are then screened for redox stability under Hydrogen Evolution Reaction (HER) or Oxygen Evolution Reaction (OER) conditions before generating thermodynamically stable crystal structures and calculating accurate band gap values for the compounds. Our approach results in the identification of dozens of promising semiconductor candidates with ideal properties for artificial photosynthesis, offering a significant advancement toward the conversion of sunlight into chemical fuels. Semiconductor ; Photocatalysis ; Oxygen evolution ; Water splitting ; Artificial photosynthesis ; Materials science ; Redox ; Ionic bonding ; Nanotechnology ; Band gap ; Hydrogen production ; Chemistry ; Optoelectronics ; Hydrogen ; Electrochemistry ; Catalysis ; Physical chemistry ; Ion Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Ruibin Liu | Erik Vazquez-Montelongo | Shuhua Ma | Jana Shen
Source: ChemRxiv (preprint, 2023-07-06)
Field: Chemistry — Organic Chemistry

Quantum Descriptors for Predicting and Understanding the Structure-Activity Relationships of Michael Acceptor Warheads Predictive modeling and understanding chemical warhead reactivities have the potential to accelerate targeted covalent drug discovery. Recently, the carbanion formation free energies as well as other ground-state electronic properties from the den- sity functional theory (DFT) calculations have been proposed as predictors of glutathione reactivities of Michael acceptors; however, no clear consensus exists. By profiling the thiol-Michael reactions of a diverse set of singly- and doubly-activated olefins including several model warheads related to afatinib, here we reexamined the question of whether low-cost electronic properties can be used as predictors of reaction barriers. The electronic properties related to the carbanion intermediate were found to be strong predictors, e.g., the change in the Cβ charge accompanying carbanion formation. The least expensive reactant-only properties, the electrophilicity index and the Cβ charge also show strong rank correlations, suggesting their utility as quantum descriptors. A second objective of the work is to clarify the effect of the β-dimethylaminomethyl (DMAM) substitution which is incorporated in the warheads of several FDA-approved covalent drugs. Our data suggests that the beta-DMAM substitution is cationic at neutral pH in solution and it promotes acrylamide’s intrinsic reactivity by enhancing the charge accumulation at Calpha upon carbanion formation. In contrast, the inductive effect of the beta-trimethylaminomethyl substitution is diminished due to steric hindrance. Together, these results reconcile the current views of the intrinsic reactivities of acrylamides and contribute to large-scale predictive modeling and understanding of the structure-activity relationships of Michael acceptors for rational TCI design. Carbanion ; Chemistry ; Steric effects ; Electrophile ; Computational chemistry ; Electrophilic substitution ; Michael reaction ; Stereochemistry ; Organic chemistry Click Chemistry and Applications ; Organic and Inorganic Chemical Reactions ; Chemical Reaction Mechanisms
Authors: Jessica Freeze | Víctor S. Batista
Source: ChemRxiv (preprint, 2023-05-10)
Field: Materials Science — Materials Chemistry

GiFE: A Molecular-Size Agnostic and Understandable Gibbs Free Energy Function A fully defined and molecular-size agnostic Gibb’s free energy function that uses strictly structural parameters as input would permit real-time energetics determination and optimization of molecules heretofore intractable at ab initio levels. Here we present, the first part of a linear function for Gibbs free energy (GiFE Function) that covers the elements {H,N,O,C,F} and is molecular-size agnostic, using only atomic structure to generate the input variables. Critically, the GiFE function is capable not only of producing the value of Gibbs free energy for a given complex in constant time, but also may serve as a function over which molecules may be optimized in O(sqrt{n}) time. The prediction of individual and reaction free energies are demonstrated, as well as explanations of chemical understanding generated from the function and an outlining of how the rest of this function may be constructed, with examples covering {H,B,C,N,O,F,S,Si,Cl,Br,I}. Gibbs free energy ; Function (biology) ; Energy (signal processing) ; Ab initio ; Molecule ; Constant (computer programming) ; Thermodynamics ; Statistical physics ; Chemistry ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Khaled Younes | Mayssara Antar | Hamdi Chaouk | Yahya Kharboutly | Omar Mouhtady | Emil Obeid | Eddie Gazo Hanna | Jalal Halwani | Nimer Murshid
Source: Gels (article, 2023-06-06)
Field: Materials Science — Surfaces, Coatings and Films

Towards Understanding Aerogels’ Efficiency for Oil Removal—A Principal Component Analysis Approach In this study, our aim was to estimate the adsorption potential of three families of aerogels: nanocellulose (NC), chitosan (CS), and graphene (G) oxide-based aerogels. The emphasized efficiency to seek here concerns oil and organic contaminant removal. In order to achieve this goal, principal component analysis (PCA) was used as a data mining tool. PCA showed hidden patterns that were not possible to seek by the bi-dimensional conventional perspective. In fact, higher total variance was scored in this study compared with previous findings (an increase of nearly 15%). Different approaches and data pre-treatments have provided different findings for PCA. When the whole dataset was taken into consideration, PCA was able to reveal the discrepancy between nanocellulose-based aerogel from one part and chitosan-based and graphene-based aerogels from another part. In order to overcome the bias yielded by the outliers and to probably increase the degree of representativeness, a separation of individuals was adopted. This approach allowed an increase in the total variance of the PCA approach from 64.02% (for the whole dataset) to 69.42% (outliers excluded dataset) and 79.82% (outliers only dataset). This reveals the effectiveness of the followed approach and the high bias yielded from the outliers. Principal component analysis ; Nanocellulose ; Outlier ; Graphene ; Robust principal component analysis ; Variance (accounting) ; Computer science ; Pattern recognition (psychology) ; Representativeness heuristic ; Aerogel ; Materials science ; Artificial intelligence ; Statistics ; Data mining ; Mathematics ; Cellulose ; Nanotechnology ; Chemical engineering ; Engineering Surface Modification and Superhydrophobicity ; Aerogels and thermal insulation
Authors: Nuno M. Fortunato | Xiaoqing Li | Stephan Schönecker | Ruiwen Xie | Andreas Taubel | Franziska Scheibel | Ingo Opahle | Oliver Gutfleisch | Hongbin Zhang
Source: TUbilio (Technical University of Darmstadt) (preprint, 2023-06-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-throughput design of all-d-metal Heusler alloys for magnetocaloric applications Due to their versatile composition and customizable properties, A$_2$BC Heusler alloys have found applications in magnetic refrigeration, magnetic shape memory effects, permanent magnets, and spintronic devices. The discovery of all-$d$-metal Heusler alloys with improved mechanical properties compared to those containing main group elements, presents an opportunity to engineer Heuslers alloys for energy-related applications. Using high-throughput density functional theory calculations, we screened magnetic all-$d$-metal Heusler compounds and identified 686 (meta)stable compounds. Our detailed analysis revealed that the inverse Heusler structure is preferred when the electronegativity difference between the A and B/C atoms is small, contrary to conventional Heusler alloys. Additionally, our calculations of Pugh ratios and Cauchy pressures demonstrated that ductile and metallic bonding are widespread in all-$d$-metal Heuslers, supporting their enhanced mechanical behaviour. We identified 49 compounds with a double-well energy surface based on Bain path calculations and magnetic ground states, indicating their potential as candidates for magnetocaloric and shape memory applications. Furthermore, by calculating the free energies, we propose that 11 compounds exhibit structural phase transitions, and propose isostructural substitution to enhance the magnetocaloric effect. Magnetic refrigeration ; Materials science ; Electronegativity ; Density functional theory ; Heusler compound ; Pseudoelasticity ; Condensed matter physics ; Spintronics ; Metal ; Thermodynamics ; Metallurgy ; Ferromagnetism ; Magnetization ; Magnetic field ; Computational chemistry ; Microstructure ; Chemistry ; Physics Heusler alloys: electronic and magnetic properties
Authors: Yeonjoon Kim | Jaeyoung Cho | Hojin Jung | Lydia Meyer | Gina M. Fioroni | Keunhong Jeong | Robert L. McCormick | Peter C. St. John | Seonah Kim
Source: ChemRxiv (preprint, 2023-05-10)
Field: Materials Science — Materials Chemistry

Design Green Chemicals by Predicting Vaporization Properties Using Explainable Graph Attention Networks Computational predictions of vaporization properties aid the de novo design of green chemicals, including clean alternative fuels and working fluids for efficient thermal energy recovery. Here, we developed chemically explainable graph attention networks to predict five physical properties pertinent to performance in utilizing renewable energy: heat of vaporization (HoV), critical temperature, flash point, boiling point, and liquid heat capacity. The predictive model for HoV was trained using ~150,000 data points, with considering their uncertainties and temperature dependence. Next, this model was expanded to the other properties through transfer learning to overcome the limitations due to fewer data points (700-7,500). Chemical interpretability of the model was then investigated, demonstrating that the model explains molecular structural effects on vaporization properties. Finally, the developed predictive models were applied to the design of chemicals that have desirable properties as efficient and green working fluids and fuels, enabling fast and accurate screening before experiments. Vaporization ; Interpretability ; Boiling point ; Renewable energy ; Computer science ; Graph ; Flash point ; Process engineering ; Machine learning ; Chemistry ; Engineering ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; Process Optimization and Integration ; Catalysis and Oxidation Reactions
Authors: Veerpal Kaur | Surbhi Garg | Sabyasachi Rakshit | Richard Kelly | Harriet Riley | Helen Saxton | Danny Andrews | Ershad Abubacker | Becky Webb | Kirstine Anderson | Matthew J. Bown | Laura Cooper | Hannah Fielding | Clare Fitzgerald | Anoushka Handa | Claire Harding | Alan Holder | Charlie Palmer | Rosie Rothwell | Donna Smith | Laura Smith | Jade Holliday | Natalie Ford | Jeanne Andres | P Patrick | Kamal Conti | Paul Batra | Carla Iacomi | Sanchari Soares | Nathalie Dasgupta | Agne `s Lattuati-Derieux | Noe lle Timbart | Rukshala Me lanie Nicolas | Anton | Ste phane Moularat ; Qin | Li-Jun Hou | Jing Chen | Er-Xia He | Bo Chen | Xuechou Wang | Yumei Zhou | Liang Dai | * He | Qipu Lin | ; | Haruhiko Fukaya | Hikaru Yanai | ; Jiaqi Hou | Leipeng Xue | Tianxu Yu | Jiahui Li | Shibo | Sneha Verma | Nik Khadijah Nik Aznan | Kathryn Garside | Thomas Penfold ; Anindita Thakur | Ramakrishnan Ganesan | * | Jayati Ray Dutta | ; Huai-Qin | Wei Wang | Zhou
Source: Chemical Communications (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Contents list Permissions Request permissions Contents list Chem. Commun., 2023, 59, 6937 DOI: 10.1039/D3CC90193A This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Computer science ; Attribution ; Library science ; World Wide Web ; Chemistry ; Computer security ; Psychology Radioactive element chemistry and processing
Authors: Ramakrishnan Kannan | Cristina García–Cardona | Balasubramaniam Radhakrishnan | Sudip K. Seal
Source:  (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

A Deep Learning Pipeline for Optimizing Large-scale Phase Field Simulations Phase field (PF) simulations are computationally expensive but remain a key analysis tool to understand the complex mechanisms of additive manufacturing (AM) processes. Each PF simulation-aided analysis requires thousands of node hours on leadership-class supercomputers. One of the main goals of these analyses is the study of microstructure evolution during the build process which begins with the onset of nucleation. Nucleation occurs under certain thermomechanical conditions which are not known a priori and many PF simulations are required to identify ranges of input thermo-mechanical parameters that can result in the onset of nucleation. Since many of the simulations do not result in nucleation, an analysis campaign often ends up wasting tremendous amounts of precious computing resources executing nucleation-absent simulations. The goal of this work is to design and train deep learning models to inform a PF simulation about the likelihood of the occurrence of nucleation in a future simulation time-step based on the state summary over a finite number of past time-steps of a running simulation. If the prediction determines that the running simulation is unlikely to reach nucleation in the allotted time, then its execution is stopped immediately ultimately resulting in vast reduction in wasted computations when accrued over all the PF simulations typically performed in a single or multiple analysis campaign(s). The paper presents the performance of a machine learning pipeline that uses a convolutional neural network (CNN) model to learn an embedding which is then used with a self-attention network to build a multi-task deep learning model to predict the likelihood of nucleation. The model also predicts the input parameters used in a simulation. Performance is compared with a baseline pipeline that uses an off-the-shelf LeNet-5 model to learn the initial embedding. Despite their smaller size, performance results indicate significant improvement in accuracy of the proposed models compared to the larger baseline models. Computer science ; Nucleation ; Pipeline (software) ; Deep learning ; Process (computing) ; Field (mathematics) ; Convolutional neural network ; Artificial intelligence ; Computation ; Feature (linguistics) ; Task (project management) ; Computational science ; Machine learning ; Distributed computing ; Algorithm ; Physics ; Engineering Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Machine Learning in Materials Science
Authors: Clément Molinet‐Chinaglia | Esther Vera | Luis Cardenas | P. Vernoux | L. Piccolo | S. Loridant
Source: theses.fr (ABES) (article, 2023-01-11)
Field: Materials Science — Materials Chemistry

Etude fondamentale de la dynamique structurale de catalyseurs Pt/CeO2 et son utilisation pour accélérer la réaction de gaz à l'eau Although very efficient for the water-gas shift reaction, Pt/CeO2 catalysts must be made economically competitive by limiting the platinum content. Thus, the aim of this thesis work was to optimize their catalytic activities by using the catalyst structural dynamics, the mechanisms of which are still poorly understood. An innovative method, based on mechanical mixtures, has highlighted the low mobility of platinum atoms on the scale of powder grains during an oxidative pretreatment at 500 °C. This treatment also induces redox modifications linked to the species adsorbed and desorbed on/from the ceria surface, as revealed by in situ/operando Raman and infrared spectroscopies as well as temperature-programmed methods. Starting from an ultradispersed state of platinum and different surface concentrations, it was shown that metallic nanoparticles are the most active species for the water-gas shift. The use of reducing pretreatments and oxidizing posttreatments allowed the activation of catalysts with low amount of platinum through the formation of such nanoparticles. Additionally, an oxidizing pre or posttreatment can limit the detrimental agglomeration of platinum at high contents. The complexity of the catalyst structural modifications shows that a suitable pretreatment must be chosen for each metal loading. Water-gas shift reaction ; Catalysis ; Dynamics (music) ; Materials science ; Computer science ; Process engineering ; Chemical engineering ; Chemistry ; Physics ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Valentin Munteanu | Vladimir Starostin | Alessandro Greco | Linus Pithan | Alexander Gerlach | Alexander Hinderhofer | Stefan Kowarik | Frank Schreiber
Source: arXiv (Cornell University) (preprint, 2023-06-28)
Field: Materials Science — Materials Chemistry

Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem Due to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This so-called phase problem poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this, we present an approach that utilizes prior knowledge to regularize the training process over larger parameter spaces. We demonstrate the effectiveness of our method in various scenarios, including multilayer structures with box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. By leveraging the input of prior knowledge, we can improve the training dynamics and address the underdetermined ("ill-posed") nature of the problem. In contrast to previous methods, our approach scales favorably when increasing the complexity of the inverse problem, working properly even for a 5-layer multilayer model and an N-layer periodic multilayer model with up to 17 open parameters. Underdetermined system ; Inverse problem ; Artificial neural network ; Computer science ; Phase problem ; Phase (matter) ; Neutron ; Range (aeronautics) ; Inverse ; Reflectivity ; Algorithm ; Artificial intelligence ; Optics ; Mathematics ; Physics ; Materials science ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Tim Hsu | Babak Sadigh | Nicolas Bertin | Cheol Woo Park | James Chapman | Vasily V. Bulatov | Fei Zhou
Source: Research Square (preprint, 2023-01-16)
Field: Materials Science — Materials Chemistry

Score-based denoising for atomic structure identification Benchmark (surveying) ; Computer science ; Noise reduction ; Algorithm ; Noise (video) ; Crystal (programming language) ; Molecular dynamics ; Statistical physics ; Function (biology) ; Crystal structure prediction ; Task (project management) ; Crystal structure ; Feature (linguistics) ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry ; Engineering ; Image (mathematics) ; Crystallography Machine Learning in Materials Science ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: Hamid Reza Rajabi | Zinab Moradi Alvand | Ali Mirzaei
Source: Research Square (preprint, 2023-08-29)
Field: Materials Science — Materials Chemistry

Sonochemical-assisted synthesis combined with the plant-mediated approach for efficient and green synthesis of copper oxide nanoparticles: Study the effect of extraction techniques and comparative evaluation of some biological activities Copper ; Nanoparticle ; Extraction (chemistry) ; Nanotechnology ; Copper oxide ; Combinatorial chemistry ; Chemistry ; Materials science ; Organic chemistry Nanoparticles: synthesis and applications ; Copper-based nanomaterials and applications ; TiO2 Photocatalysis and Solar Cells