jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Zhang | Jinhui Yu | Hongwei Song | Minghui Yang
Source: Journal of Chemical Information and Modeling (article, 2023-08-10)
Field: Materials Science — Materials Chemistry

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH <sub>3</sub> /H/O Radicals Accurate determination of the thermal rate constants for combustion reactions is a highly challenging task, both experimentally and theoretically. Machine learning has been proven to be a powerful tool to predict reaction rate constants in recent years. In this work, three supervised machine learning algorithms, including XGB, FNN, and XGB-FNN, are used to develop quantitative structure-property relationship models for the estimation of the rate constants of hydrogen abstraction reactions from alkanes by the free radicals CH<sub>3</sub>, H, and O. The molecular similarity based on Morgan molecular fingerprints combined with the topological indices are proposed to represent chemical reactions in the machine learning models. Using the newly constructed descriptors, the hybrid XGB-FNN algorithm yields average deviations of 65.4%, 12.1%, and 64.5% on the prediction sets of alkanes + CH<sub>3</sub>, H, and O, respectively, whose performance is comparable and even superior to the corresponding one using the activation energy as a descriptor. The use of activation energy as a descriptor has previously been shown to significantly improve prediction accuracy ( <i>Fuel</i> 2022, 322, 124150) but typically requires cumbersome ab initio calculations. In addition, the XGB-FNN models could reasonably predict reaction rate constants of hydrogen abstractions from different sites of alkanes and their isomers, indicating a good generalization ability. It is expected that the reaction descriptors proposed in this work can be applied to build machine learning models for other reactions. Reaction rate constant ; Hydrogen atom abstraction ; Abstraction ; Chemistry ; Radical ; Generalization ; Artificial intelligence ; Computer science ; Ab initio ; Machine learning ; Mathematics ; Organic chemistry ; Physics ; Kinetics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: Yanzhang Zhao | Huan Li | Jieqiong Shan | Zhen Zhang | Xinyu Li | Qinfeng Shi | Yan Jiao | Haobo Li
Source: The Journal of Physical Chemistry Letters (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

Machine Learning Confirms the Formation Mechanism of a Single-Atom Catalyst via Infrared Spectroscopic Analysis Single-atom catalysts (SACs) offer significant potential across various applications, yet our understanding of their formation mechanism remains limited. Notably, the pyrolysis of zeolitic imidazolate frameworks (ZIFs) stands as a pivotal avenue for SAC synthesis, of which the mechanism can be assessed through infrared (IR) spectroscopy. However, the prevailing analysis techniques still rely on manual interpretation. Here, we report a machine learning (ML)-driven analysis of the IR spectroscopy to unravel the pyrolysis process of Pt-doped ZIF-67 to synthesize Pt–Co3O4 SAC. Demonstrating a total Pearson correlation exceeding 0.7 with experimental data, the algorithm provides correlation coefficients for the selected structures, thereby confirming crucial structural changes with time and temperature, including the decomposition of ZIF and formation of Pt–O bonds. These findings reveal and confirm the formation mechanism of SACs. As demonstrated, the integration of ML algorithms, theoretical simulations, and experimental spectral analysis introduces an approach to deciphering experimental characterization data, implying its potential for broader adoption. Mechanism (biology) ; Infrared ; Catalysis ; Atom (system on chip) ; Infrared spectroscopy ; Chemistry ; Chemical physics ; Materials science ; Photochemistry ; Computer science ; Physics ; Organic chemistry ; Optics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Haonan Fan | Yanwen Zhang | Fakhre Alam | Jing Ma | Biaobiao Hao | Yanhui Chen | Yating Wang | Jingming Huang | Tao Jiang
Source: Journal of Catalysis (article, 2023-12-09)
Field: Materials Science — Materials Chemistry

Highly efficient PCCP/Cr(III) ethylene tetramerization catalyst designed using artificial neural network approach Steric effects ; Chemistry ; Artificial neural network ; Selectivity ; Catalysis ; Electronic effect ; Ethylene ; Quantitative structure–activity relationship ; Computational chemistry ; Biological system ; Density functional theory ; Thermodynamics ; Stereochemistry ; Artificial intelligence ; Organic chemistry ; Computer science ; Physics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Organometallic Complex Synthesis and Catalysis
Authors: Seulyoung Park | Noki Lee | Jun Oh Park | Jin Park | Yu Seong Heo | Jaichan Lee
Source: ACS Materials Letters (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

Exploring the Latent Chemical Space of Oxygen Vacancy Formation Energy by a Machine Learning Ensemble Oxygen vacancy (VO) is a fundamental and intrinsic defect in metal oxides since its formation is subject to various growth and annealing processes and it significantly affects the material properties. Therefore, the formation energy of an oxygen vacancy is of great interest in the fabrication and investigation of metal oxides. Traditional methods for obtaining the formation energy of an oxygen vacancy such as theoretical calculation and experiment require expensive costs, making it difficult to explore a large number of metal oxides. Here, machine learning (ML) models are built for rapid prediction of the formation energy of an oxygen vacancy. We show that an ensemble of multiple ML models allows the prediction of the formation energy of an oxygen vacancy in a wide regime of an unexplored latent chemical space from binary to quinary oxides. Vacancy defect ; Quinary ; Oxygen ; Materials science ; Binary number ; Chemical physics ; Thermodynamics ; Chemistry ; Metallurgy ; Physics ; Crystallography ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science
Authors: Tuo Yao | Jian Liu | Xuxing Wan | Beibei Li | Sohrab Rohani | Zhenguo Gao | Junbo Gong
Source: AIChE Journal (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

Deep‐learning based <i>in situ</i> image monitoring crystal polymorph and size distribution: Modeling and validation Abstract In situ monitoring and closed‐loop control of the solution crystallization process are the modern trends for pharmaceutical development, in which the critical process parameters (CPPs) as well as the product critical quality attributes (CQAs) can be regulated and guaranteed during the manufacturing process. In this study, an in situ image monitoring methodology based on a state‐of‐the‐art deep‐learning model was developed to track the CQAs such as polymorph ratio, two‐dimensional crystal size, and crystal shape in a solvent‐mediated polymorphic transformation (SMPT) process. Coupled with the multidimensional process information, a 2D population balance model (PBM) was developed and validated using the results of the in situ image‐based CQAs analysis. The 2D‐PBM was solved using a high‐resolution finite volume method (HR‐FVM) which could provide a high dimensional particle‐size distribution. Through the validation between the process image analysis and the 2D‐PBM, the accuracy of image analysis was discussed, and the potential and challenges of in situ image analysis were proposed. This work aims to integrate the crystal polymorphism and two‐dimensional crystal size distribution (2D‐CSD) information in the SMPT process using intelligent microscopic image analysis and then to validate the results of neural network processing by solving the numerical solution of the multidimensional PBM. Critical quality attributes ; In situ ; Process analytical technology ; Biological system ; Crystallization ; Process (computing) ; Particle-size distribution ; Process control ; Materials science ; Algorithm ; Artificial intelligence ; Particle size ; Computer science ; Chemistry ; Work in process ; Engineering ; Chemical engineering Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Emerson Coy | Igor Iatsunskyi | Mikhaël Bechelany
Source: Solar RRL (article, 2023-01-03)
Field: Materials Science — Materials Chemistry

Perspectives and Current Trends on Hybrid Nanocomposite Materials for Photocatalytic Applications Photocatalysis can be understood as the acceleration of chemical reactions by incident light. These reactions typically perform poorly without photoactivation and the presence of catalysts. Photocatalysis could find applications in a wide range of fields, such as renewable energy and environmental remediation. The advance in the photocatalysis field is driven by the development of innovative materials allowing a broad range of absorption and efficient charge separation. The most developed photocatalysts are inorganic semiconductors. An alternative approach to increase the efficiency of these photocatalysts is the design of organic–inorganic hybrid materials. Two classes of hybrid materials are largely investigated: 1) small molecules and 2) organic macromolecules or polymers. The polymers can have protective roles as well as photocatalytic and conductive properties. The last type of material is known as conductive polymer. Different types of conductive polymers have been investigated in the literature. This perspective will focus on the new trend in this field by first describing the use of machine learning in designing and understanding polymeric hybrid systems toward catalytic applications, followed by the description of four types of the most used organic–inorganic hybrid materials as photocatalysts based on chitosan, polydopamine, polyaniline and covalent organic frameworks. Photocatalysis ; Polyaniline ; Materials science ; Nanocomposite ; Hybrid material ; Nanotechnology ; Polymer ; Conductive polymer ; Catalysis ; Chemistry ; Organic chemistry ; Composite material ; Polymerization Covalent Organic Framework Applications ; Conducting polymers and applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Xiangcheng Shi | Dongfang Cheng | Ran Zhao | Gong Zhang | Shican Wu | Shiyu Zhen | Zhi‐Jian Zhao | Jinlong Gong
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Accessing complex reconstructed material structures with hybrid global optimization accelerated <i>via</i> on-the-fly machine learning The complex reconstructed structure of materials can be revealed by global optimization. This paper describes a hybrid evolutionary algorithm (HEA) that combines differential evolution and genetic algorithms with a multi-tribe framework. An on-the-fly machine learning calculator is adopted to expedite the identification of low-lying structures. With a superior performance to other well-established methods, we further demonstrate its efficacy by optimizing the complex oxidized surface of Pt/Pd/Cu with different facets under (4 × 4) periodicity. The obtained structures are consistent with experimental results and are energetically lower than the previously presented model. Computer science ; Global optimization ; Identification (biology) ; Tribe ; Calculator ; Genetic algorithm ; Artificial intelligence ; Differential evolution ; Machine learning ; Algorithm ; Biology ; Ecology Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Bernhard M. Kriesche | Laura E. Kronenberg | Felix R. S. Purtscher | Thomas S. Hofer
Source: Frontiers in Chemistry (article, 2023-03-09)
Field: Materials Science — Materials Chemistry

Storage and diffusion of CO2 in covalent organic frameworks—A neural network-based molecular dynamics simulation approach As a consequence of the accelerated climate change, solutions to capture, store and potentially activate carbon dioxide received increased interest in recent years. Herein, it is demonstrated, that the neural network potential ANI-2x is able to describe nanoporous organic materials at approx. density functional theory accuracy and force field cost, using the example of the recently published two- and three-dimensional covalent organic frameworks HEX-COF1 and 3D-HNU5 and their interaction with CO<sub>2</sub> guest molecules. Along with the investigation of the diffusion behaviour, a wide range of properties of interest is analyzed, such as the structure, pore size distribution and host-guest distribution functions. The workflow developed herein facilitates the estimation of the maximum CO<sub>2</sub> adsorption capacity and is easily generalizable to other systems. Additionally, this work illustrates, that minimum distance distribution functions can be a highly useful tool in understanding the nature of interactions in host-gas systems at the atomic level. Molecular dynamics ; Covalent bond ; Diffusion ; Artificial neural network ; Dynamics (music) ; Computer science ; Chemical physics ; Chemistry ; Materials science ; Nanotechnology ; Biological system ; Statistical physics ; Computational chemistry ; Physics ; Artificial intelligence ; Organic chemistry ; Thermodynamics ; Biology Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Po-Yen Chen | Kiyou Shibata | Katsumi Hagita | Tomohiro Miyata | Teruyasu Mizoguchi
Source: The Journal of Physical Chemistry Letters (article, 2023-05-18)
Field: Materials Science — Materials Chemistry

Prediction of the Ground-State Electronic Structure from Core-Loss Spectra of Organic Molecules by Machine Learning The core-loss spectrum reflects the partial density of states (PDOS) of the unoccupied states at the excited state and is a powerful analytical technique to investigate local atomic and electronic structures of materials. However, various molecular properties governed by the ground-state electronic structure of the occupied orbital cannot be directly obtained from the core-loss spectra. Here, we constructed a machine learning model to predict the ground-state carbon s- and p-orbital PDOS in both occupied and unoccupied states from the C K-edge spectra. We also attempted an extrapolation prediction of the PDOS of larger molecules using a model trained by smaller molecules and found that the extrapolation prediction performance can be improved by excluding tiny molecules. Besides, we found that using smoothing preprocess and training by specific noise data can improve the PDOS prediction for noise-contained spectra, which pave a way for the application of the prediction model to the experimental data. Extrapolation ; Excited state ; Ground state ; Spectral line ; Electronic structure ; Smoothing ; Molecule ; Chemistry ; Homonuclear molecule ; Molecular physics ; Core (optical fiber) ; Chemical physics ; Atomic physics ; Statistical physics ; Computational chemistry ; Physics ; Computer science ; Quantum mechanics ; Optics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Thomas Pigeon | Gabriel Stoltz | Manuel Corral Valero | Ani Anciaux‐Sedrakian | Maxime Moreaud | Tony Lelièvre | Pascal Raybaud
Source: Journal of Chemical Theory and Computation (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Computing Surface Reaction Rates by Adaptive Multilevel Splitting Combined with Machine Learning and <i>Ab Initio</i> Molecular Dynamics Computing accurate rate constants for catalytic events occurring at the surface of a given material represents a challenging task with multiple potential applications in chemistry. To address this question, we propose an approach based on a combination of the rare event sampling method called adaptive multilevel splitting (AMS) and <i>ab initio</i> molecular dynamics. The AMS method requires a one-dimensional reaction coordinate to index the progress of the transition. Identifying a good reaction coordinate is difficult, especially for high dimensional problems such as those encountered in catalysis. We probe various approaches to build reaction coordinates such as support vector machine and path collective variables. The AMS is implemented so as to communicate with a density functional theory-plane wave code. A relevant case study in catalysis, the change of conformation and the dissociation of a water molecule chemisorbed on the (100) γ-alumina surface, is used to evaluate our approach. The calculated rate constants and transition mechanisms are discussed and compared to those obtained by a conventional static approach based on the Eyring-Polanyi equation with harmonic approximation. It is revealed that the AMS method may provide rate constants that are smaller than those provided by the static approach by up to 2 orders of magnitude due to entropic effects involved in the chemisorbed water molecule. Reaction coordinate ; Ab initio ; Reaction rate constant ; Dissociation (chemistry) ; Molecular dynamics ; Transition state ; Computer science ; Chemistry ; Density functional theory ; Transition state theory ; Reaction rate ; Statistical physics ; Catalysis ; Physics ; Computational chemistry ; Physical chemistry ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Nahoko Kuroki | Yuji Mochizuki | Hirotoshi Mori
Source: Journal of Chemical Education (article, 2023-01-04)
Field: Chemistry — Physical and Theoretical Chemistry

Practical Computational Chemistry Course for a Comprehensive Understanding of Organic, Inorganic, and Physical Chemistry: From Molecular Interactions to Chemical Reactions In all fields of chemistry, it has become essential to use quantum chemical calculations and machine learning for explaining and predicting chemical phenomena. However, it is challenging to apply textbook knowledge to practical research. In this study, we developed teaching material based on computational chemistry to promote the integration of knowledge from all fields of chemistry, including organic, inorganic, and physical chemistry, to bridge the gap between classroom learning and chemistry research. This experimental material, which comprehensively covers weak intermolecular interactions and chemical reactions, is expected to help students conduct purposeful research activities after being assigned to a laboratory. Chemistry ; Chemistry education ; Physical organic chemistry ; Quantum chemistry ; Nanotechnology ; Biochemical engineering ; Physical chemistry ; Organic chemistry ; Molecule ; Physics ; Materials science ; Engineering ; Supramolecular chemistry ; Quality (philosophy) Various Chemistry Research Topics ; Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: Wenjie Du | Fenfen Ma | Baicheng Zhang | Shuai Zhang | Di Wu | Edward Sharman | Jun Jiang | Yang Wang
Source: Journal of the American Chemical Society (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

Spectroscopy-Guided Deep Learning Predicts Solid–Liquid Surface Adsorbate Properties in Unseen Solvents Accurately and rapidly acquiring the microscopic properties of a material is crucial for catalysis and electrochemistry. Characterization tools, such as spectroscopy, can be a valuable tool to infer these properties, and when combined with machine learning tools, they can theoretically achieve fast and accurate prediction results. However, on the path to practical applications, training a reliable machine learning model is faced with the challenge of uneven data distribution in a vast array of non-negligible solvent types. Herein, we employ a combination of the first-principles-based approach and data-driven model. Specifically, we utilize density functional theory (DFT) to calculate theoretical spectral data of CO-Ag adsorption in 23 different solvent systems as a data source. Subsequently, we propose a hierarchical knowledge extraction multiexpert neural network (HMNN) to bridge the knowledge gaps among different solvent systems. HMNN undergoes two training tiers: in tier I, it learns fundamental quantitative spectra-property relationships (QSPRs), and in tier II, it inherits the fundamental QSPR knowledge from previous steps through a dynamic integration of expert modules and subsequently captures the solvent differences. The results demonstrate HMNN's superiority in estimating a range of molecular adsorbate properties, with an error range of less than 0.008 eV for zero-shot predictions on unseen solvents. The findings underscore the usability, reliability, and convenience of HMNN and could pave the way for real-time access to microscopic properties by exploiting QSPR. Chemistry ; Artificial neural network ; Range (aeronautics) ; Quantitative structure–activity relationship ; Characterization (materials science) ; Usability ; Spectroscopy ; Biological system ; Machine learning ; Artificial intelligence ; Computer science ; Nanotechnology ; Materials science ; Human–computer interaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Maithreyi Ramakrishnan | Alexander van Teijlingen | Tell Tuttle | Rein V. Ulijn
Source: Angewandte Chemie (article, 2023-02-02)
Field: Materials Science — Biomaterials

Integrating Computation, Experiment, and Machine Learning in the Design of Peptide‐Based Supramolecular Materials and Systems Abstract Interest in peptide‐based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screen and navigate these very large design spaces. Increasingly, the use of artificial intelligence is employed to screen far more candidates than traditional methods. Based on a brief history of computational and experimentally integrated investigations of peptide structures, we explore recent impactful examples of computationally driven investigation into peptide self‐assembly, focusing on recent advances in methodology development. It is clear that the integration between experiment and computation to understand and design new systems is becoming near seamless in this growing field. Supramolecular chemistry ; Computer science ; Computation ; Field (mathematics) ; Nanotechnology ; Artificial intelligence ; Chemistry ; Materials science ; Algorithm ; Mathematics Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Polydiacetylene-based materials and applications