jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chao Xu | Emily Mazeau | Richard H. West
Source: ChemRxiv (preprint, 2024-04-03)
Field: Materials Science — Materials Chemistry

Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-field Micro-kinetic Modeling for Catalytic Methane Partial Oxidation Mean-field micro-kinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a micro-kinetic model often need to be adjusted when changing species’ binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and when fitting experimental data. When altering reaction enthalpies, the activation energies should also be reasonably altered, to ensure realistic reaction rates. The Blowers-Masel approximation (BMA) relates the reaction barrier to the reaction enthalpy. Unlike the Brønsted-Evans-Polani (BEP) relationship, the BMA requires less data because only one parameter, the intrinsic activation energy, needs to be determined. We validate this application of BMA relations to model surface reactions by comparing against density functional theory (DFT) data taken from literature. By incorporating the BMA rate description into the open-source Cantera software we enable a new workflow, demonstrated herein, allowing rapid screening of catalysts using linear scaling relationships (LSRs) and BMA kinetics within the process simulation software. For demonstration purposes, a catalyst screening for catalytic methane partial oxidation (CMPO) on 81 hypothetical metals is conducted. We compare the results with and without BMA-corrected rates. The heat maps of various descriptors (e.g. CH4 conversion, syngas yield) show that using BMA rates instead of Arrhenius rates (with constant activation energies) changes which metals are most active. Heat maps of sensitivity analyses can help identify which reactions or species are most influential in shaping the descriptor map patterns. Our findings indicate that while using BMA-adjusted rates didn't markedly affect the most sensitive reactions, it did change the most influential species. Methane ; Kinetic energy ; Catalysis ; Activation energy ; Enthalpy ; Thermodynamics ; Chemistry ; Scale (ratio) ; Field (mathematics) ; Partial oxidation ; Energy (signal processing) ; Physical chemistry ; Physics ; Organic chemistry ; Mathematics ; Statistics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Industrial Gas Emission Control
Authors: Aynur Teyyub Mammadova | Elkhan Firad Jafarov | Javid Nushravan Mammadov | Gudrat Shahin Huseynzada
Source: Journal of Young Researcher (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

The role of etherified phenols in sustainable wood preservation: synthesis and application: mini review This paper explores the innovative role of etherified phenols in sustainable wood preservation, offering an eco-friendly alternative to traditional wood preservatives.The focus is on the synthesis, chemical properties, and application of etherified phenols, and their potential to revolutionize the wood preservation industry.Initially, the paper reviews historical methods of wood preservation and highlights the environmental and health challenges associated with traditional preservatives.It then delves into the chemical properties of etherified phenols, illustrating their advantages in terms of biodegradability, lower toxicity, and effectiveness in wood preservation.The methods of synthesizing etherified phenols are discussed, emphasizing recent advancements like green chemistry approaches, catalytic methods, and biocatalytic synthesis.The paper also addresses the economic and environmental impacts of adopting etherified phenols, underscoring the balance between efficacy, safety, and sustainability.Finally, it presents case studies and potential future directions for research and application.This comprehensive analysis demonstrates that etherified phenols are not just a viable alternative but represent a significant step forward in the pursuit of sustainable wood preservation practices. Phenols ; Organic chemistry ; Polymer science ; Pulp and paper industry ; Chemistry ; Biochemical engineering ; Business ; Engineering Wood and Agarwood Research ; Lignin and Wood Chemistry ; Cultural Heritage Materials Analysis
Authors: Wang Shoushun | Mengjie Liu | Yue Yiyuan | Xiude Hu | Yalin Zhang | Zhen Li | Guodong Shen | Ruiguo Dong | Lilong Shi | Bing Yu | Xianqiang Huang
Source:  (preprint, 2024-03-14)
Field: Chemistry — Organic Chemistry

Microwave- and Ionic Liquids-Based Sustainable Preparation of 2-Acylbenzothiazoles Microwave irradiation (MW) and ionic liquids (ILs) are two of the most promising relatively greener synthetic approaches for preparing the value-added chemicals. The breakthrough of their combination in the generation of 2-acylbenzothiazoles is unprec Ionic liquid ; Sonogashira coupling ; Chemistry ; Intramolecular force ; Dehydrogenation ; Combinatorial chemistry ; Microwave ; Microwave irradiation ; Organic chemistry ; Catalysis ; Palladium Sulfur-Based Synthesis Techniques ; Catalytic C–H Functionalization Methods ; Multicomponent Synthesis of Heterocycles
Authors: Andrew Falkowski | Taylor D. Sparks
Source: ChemRxiv (preprint, 2024-12-17)
Field: Materials Science — Materials Chemistry

Mutual Information Informed Novelty Estimation of Materials Along Chemical and Structural Axes This work presents a parameter-free method for estimating materials novelty along chemical and structural axes using mutual information informed density functions. The approach quantifies novelty by analyzing how MI changes with distance between materials, establishing objective criteria for determining meaningful neighborhoods without requiring predetermined parameters. We demonstrate the method's effectiveness using two case studies: a control dataset of materials with varying degrees of similarity and a practical application analyzing lithium-containing compounds from the GNOME dataset relative to known materials. The method successfully identifies meaningful patterns of novelty in both chemical and structural domains while providing interpretable results that align with materials science intuition. This framework offers researchers a quantitative tool for assessing candidate materials against existing knowledge bases and could support more informed selection of synthesis targets in materials discovery campaigns. Novelty ; Estimation ; Mutual information ; Computer science ; Artificial intelligence ; Psychology ; Engineering ; Social psychology ; Systems engineering Machine Learning in Materials Science
Authors: Xiang Ye
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Advances in metal-organic frameworks for efficient hydrogen storage This article addresses environmental and energy issues by proposing the use of hydrogen as an alternative energy source. However, metal-organic frameworks (MOFs) were chosen as a medium for hydrogen storage and transport because of their large capacity, favourable reversibility, suitable reaction conditions, and relatively low density. The study also introduces the hydrogen storage mechanism of MOFs, and factors affecting their hydrogen storage capacity, and discusses some ways to improve the storage. also introduces the hydrogen storage mechanism of MOFs, factors affecting its hydrogen storage capacity, and discusses some ways to improve the storage The study also introduces the hydrogen storage mechanism of MOFs, factors affecting its hydrogen storage capacity, and discusses some ways to improve the storage features of MOFs. Furthermore, a few of the newest technologies that aid in the creation of MOFs are discussed. This makes it possible to find potential MOFs fast, which·h saves money and effort.1 Introduction. Hydrogen storage ; Energy storage ; Hydrogen ; Metal-organic framework ; Computer science ; Mechanism (biology) ; Process engineering ; Nanotechnology ; Materials science ; Chemistry ; Engineering ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Carbon dioxide utilization in catalysis
Authors: Xiangxiang Shen | Zheng Wan | Lingfeng Wen | Licheng Sun | Ou Yang Ming Jie | Xuan Tang | Lin, Shing-Ho J. | Xiao He | Mingsong Chen | Xian Wei
Source: arXiv (Cornell University) (preprint, 2024-08-23)
Field: Materials Science — Materials Chemistry

PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction Crystal structures can be simplified as a periodic point set that repeats across three-dimensional space along an underlying lattice. Traditionally, crystal representation methods characterize the structure using descriptors such as lattice parameters, symmetry, and space groups. However, in reality, atoms in materials always vibrate above absolute zero, causing their positions to fluctuate continuously. This dynamic behavior disrupts the fundamental periodicity of the lattice, making crystal graphs based on static lattice parameters and conventional descriptors discontinuous under slight perturbations. Chemists proposed the pairwise distance distribution (PDD) method to address this problem. However, the completeness of PDD requires defining a large number of neighboring atoms, leading to high computational costs. Additionally, PDD does not account for atomic information, making it challenging to apply it directly to crystal material property prediction tasks. To tackle these challenges, we introduce the atom-Weighted Pairwise Distance Distribution (WPDD) and Unit cell Pairwise Distance Distribution (UPDD) and apply them to the construction of multi-edge crystal graphs. We demonstrate the continuity and general completeness of crystal graphs under slight atomic position perturbations. Moreover, by modeling PDD as global information and integrating it into matrix-based message passing, we significantly reduce computational costs. Comprehensive evaluation results show that WPDDFormer achieves state-of-the-art predictive accuracy across tasks on benchmark datasets such as the Materials Project and JARVIS-DFT. Zeolite ; Transformer ; Graph ; Materials science ; Topology (electrical circuits) ; Mathematics ; Electrical engineering ; Discrete mathematics ; Combinatorics ; Engineering ; Chemistry ; Voltage Machine Learning in Materials Science ; Gene expression and cancer classification ; Bioinformatics and Genomic Networks
Authors: Adithya N Sreenivasan | C. Levi Petix | Zachary M. Sherman | Michael P. Howard
Source: arXiv (Cornell University) (preprint, 2024-08-06)
Field: Materials Science — Materials Chemistry

relentless: Transparent, reproducible molecular dynamics simulations for optimization relentless is an open-source Python package that enables the optimization of objective functions computed using molecular dynamics simulations. It has a high-level, extensible interface for model parametrization; setting up, running, and analyzing simulations natively in established software packages; and gradient-based optimization. We describe the design and implementation of relentless in the context of relative entropy minimization, and we demonstrate its abilities to design pairwise interactions between particles that form targeted structures. relentless aims to streamline the development of computational materials design methodologies and promote the transparency and reproducibility of complex workflows integrating molecular dynamics simulations. Molecular dynamics ; Dynamics (music) ; Computer science ; Statistical physics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Hang Yang | Zhijiang Yang | Teng-Xin Huang | Pan Li | Xin-Miao Wei | Yan-Fei Hu | Yuquan Yuan | Liang-Liang Wang | Junjie Ding
Source: ACS Omega (article, 2024-06-05)
Field: Chemistry — Spectroscopy

Accurate Density Prediction of Sesquiterpenoid HEDFs and the Multiproperty Computing Server SesquiterPre Accurate and rapid evaluation of density is crucial for evaluating the packing and combustion characteristics of high-energy-density fuels (HEDFs). This parameter is pivotal in the selection of high-performance HEDFs. Our study leveraged a polycyclic compound density data set and quantum chemical (QC) descriptors to establish a correlation with the target properties using the XGBoost algorithm. We utilized a recursive feature elimination method to simplify the model and developed a concise and interpretable density prediction model incorporating only six QC descriptors. The model demonstrated robust performance, achieving coefficients of determination (<i>R</i> <sup>2</sup>) of 0.967 and 0.971 for internal and external test sets, respectively, and root-mean-square errors (RMSE) of 0.031 and 0.027 g/cm<sup>3</sup>, respectively. Compared to the other two mainstream methods, the marginal discrepancy between the predicted and actual molecular densities underscores the model's superior predictive ability and more usefulness for energy density calculation. Furthermore, we developed a web server (SesquiterPre, https://sespre.cmdrg.com/#/) that can simultaneously calculate the density, enthalpy of combustion, and energy density of sesquiterpenoid HEDFs, which greatly facilitates the use of researchers and is of great significance for accelerating the design and screening of novel sesquiterpenoid HEDFs. Computer science ; Operating system Molecular spectroscopy and chirality ; Thermal and Kinetic Analysis ; Analytical Chemistry and Chromatography
Authors: Nikita Rybin | Ivan S. Novikov | Alexander V. Shapeev
Source: arXiv (Cornell University) (preprint, 2024-10-04)
Field: Chemistry — Spectroscopy

Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor Potentials and active learning (based on maxvol algorithm) to accelerate structure prediction of molecular crystals. Benzene and glycine are used as test systems. Interestingly, among obtained low energy structures of benzene we have found a peculiar polymeric benzene structure. Moment (physics) ; Tensor (intrinsic definition) ; Moment tensor ; Statistical physics ; Physics ; Computer science ; Materials science ; Mathematics ; Classical mechanics ; Pure mathematics Advanced NMR Techniques and Applications
Authors: Mohammed Yaseen Abdullah | Laith Baqir Salman | Ahmed Sadoon Obaed | Aqeel Mahmood Jawad | Halal Azam Sbhui
Source: Radioelectronics Nanosystems Information Technologies (article, 2024-09-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Innovations and Applications of Nanostructured Energy Storage Materials Background: As the world shifts towards renewable energy, there is a growing demand for efficient energy storage systems. Because of their unique features, nanostructured materials have emerged as key to improving the performance and efficiency of these storage systems. Objective: This research aims to assess the most recent discoveries and uses of nanostructured materials in energy storage technologies, emphasizing its disruption in the industry. Methods: A detailed assessment of recent advances was undertaken, focusing on diverse nanostructured materials utilized in batteries, supercapacitors, and other energy storage systems. Synthesis and characterization techniques for these materials and their incorporation into energy storage devices were investigated. Results: The nanostructured materials considerably increase energy density, charge/discharge speeds, and durability of energy storage devices. Graphene, carbon nanotubes, and metal-oxide nanostructures have significantly improved performance metrics across various applications. Conclusion: Nanostructured materials have to revolutionize energy storage systems. Continued research and development in this sector are critical for developing high-performance, long-term energy storage technologies capable of meeting the growing demands of contemporary technology and renewable energy systems. Further investigation into these materials' scalability and environmental effects is necessary to ensure their practicality and sustainability. Energy storage ; Supercapacitor ; Renewable energy ; Nanotechnology ; Materials science ; Scalability ; Process engineering ; Computer data storage ; Computer science ; Engineering ; Electrical engineering ; Capacitance Supercapacitor Materials and Fabrication
Authors: Chika Ishii | Kimitaka Asatani | Ichiro Sakata
Source: PLoS ONE (article, 2024-01-26)
Field: Materials Science — Materials Chemistry

Detecting possible pairs of materials for composites using a material word co-occurrence network Composite materials are popular because of their high performance capabilities, but new material development is time-consuming. To accelerate this process, researchers studying material informatics, an academic discipline combining computational science and material science, have developed less time-consuming approaches for predicting possible material combinations. However, these processes remain problematic because some materials are not suited for them. The limitations of specific candidates for new composites may cause potential new material pairs to be overlooked. To solve this problem, we developed a new method to predict possible composite material pairs by considering more materials than previous techniques. We predicted possible material pairs by conducting link predictions of material word co-occurrence networks while assuming that co-occurring material word pairs in scientific papers on composites were reported as composite materials. As a result, we succeeded in predicting the co-occurrence of material words with high specificity. Nodes tended to link to many other words, generating new links in the created co-occurrence material word network; notably, the number of material words co-occurring with graphene increased rapidly. This phenomenon confirmed that graphene is an attractive composite component. We expect our method to contribute to the accelerated development of new composite materials. Word (group theory) ; Materials informatics ; Material properties ; Computer science ; Composite number ; Process (computing) ; Graphene ; Materials science ; Composite material ; Mathematics ; Nanotechnology ; Health informatics Machine Learning in Materials Science ; Graph Theory and Algorithms ; Material Selection and Properties
Authors: Kristin A. Persson
Source: eScholarship (California Digital Library) (article, 2024-04-04)
Field: Chemistry — Spectroscopy

Deconvolution and Analysis of the 1H NMR Spectra of Crude Reaction Mixtures Nuclear magnetic resonance (NMR) spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity of manually analyzing the NMR spectra of new chemical entities.Current efforts to automate the analysis of NMR spectra rely on comparisons to databases of reported spectra for known compounds and, therefore, are incompatible with the exploration of new chemical space.By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, we have used Hamiltonian Monte Carlo Markov Chain and density functional theory to fit the predicted NMR spectra to those of crude reaction mixtures.This approach enables the deconvolution and analysis of the spectra of mixtures of compounds without relying on reported spectra.The utility of our approach to analyze crude reaction mixtures is demonstrated with the experimental spectra of reactions that generate a mixture of isomers, such as Wittig olefination and C-H functionalization reactions.The correct identification of compounds in a reaction mixture and their relative concentrations is achieved with a mean absolute error as low as 1%. Deconvolution ; Chemistry ; NMR spectra database ; Spectral line ; Analytical Chemistry (journal) ; Chromatography ; Mathematics ; Algorithm ; Physics Analytical Chemistry and Chromatography ; NMR spectroscopy and applications
Authors: Tina Torabi | Timon S. Gutleb | Christoph Ortner
Source: arXiv (Cornell University) (preprint, 2024-12-17)
Field: Chemistry — Spectroscopy

Fast automatically differentiable matrix functions and applications in molecular simulations We describe efficient differentiation methods for computing Jacobians and gradients of a large class of matrix functions including the matrix logarithm $\log(A)$ and $p$-th roots $A^{\frac{1}{p}}$. We exploit contour integrals and conformal maps as described by (Hale et al., SIAM J. Numer. Anal. 2008) for evaluation and differentiation and analyze the computational complexity as well as numerical accuracy compared to high accuracy finite difference methods. As a demonstrator application we compute properties of structural defects in silicon crystals at positive temperatures, requiring efficient and accurate gradients of matrix trace-logarithms. Differentiable function ; Matrix (chemical analysis) ; Computer science ; Applied mathematics ; Mathematical optimization ; Pure mathematics ; Mathematics ; Chemistry ; Chromatography Advanced NMR Techniques and Applications ; Advanced Physical and Chemical Molecular Interactions ; Protein Structure and Dynamics
Authors: Michael Kilgour | Mark E. Tuckerman | Jutta Rogal
Source: arXiv (Cornell University) (preprint, 2024-05-22)
Field: Materials Science — Materials Chemistry

Multi-Type Point Cloud Autoencoder: A Complete Equivariant Embedding for Molecule Conformation and Pose Representations are a foundational component of any modelling protocol, including on molecules and molecular solids. For tasks that depend on knowledge of both molecular conformation and 3D orientation, such as the modelling of molecular dimers, clusters, or condensed phases, we desire a rotatable representation that is provably complete in the types and positions of atomic nuclei and roto-inversion equivariant with respect to the input point cloud. In this paper, we develop, train, and evaluate a new type of autoencoder, molecular O(3) encoding net (Mo3ENet), for multi-type point clouds, for which we propose a new reconstruction loss, capitalizing on a Gaussian mixture representation of the input and output point clouds. Mo3ENet is end-to-end equivariant, meaning the learned representation can be manipulated on O(3), a practical bonus. An appropriately trained Mo3ENet latent space comprises a universal embedding for scalar and vector molecule property prediction tasks, as well as other downstream tasks incorporating the 3D molecular pose, and we demonstrate its fitness on several such tasks. Autoencoder ; Equivariant map ; Point cloud ; Embedding ; Type (biology) ; Point (geometry) ; Computer science ; Artificial intelligence ; Mathematics ; Pure mathematics ; Geometry ; Biology ; Deep learning ; Paleontology Machine Learning in Materials Science ; Medical Imaging Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques