jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiawei Wu | Zhihong Chen | Zhongwen Si | Xiaoling Lou | Junxian Yun
Source: Chinese Journal of Chemical Engineering (article, 2024-08-31)
Field: Materials Science — Biomaterials

Radial basis function neural network and overlay sampling uniform design toward polylactic acid molecular weight prediction Polylactic acid ; Overlay ; Basis (linear algebra) ; Artificial neural network ; Function (biology) ; Radial basis function ; Sampling (signal processing) ; Materials science ; Computer science ; Biological system ; Mathematics ; Artificial intelligence ; Composite material ; Biology ; Telecommunications ; Geometry biodegradable polymer synthesis and properties ; Collagen: Extraction and Characterization ; Polymer composites and self-healing
Authors: Chunjie Zhai | Xinmeng Wang | Siyu Zhang | Zhaolou Cao
Source: Combustion and Flame (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

Parameter estimation of distributed activation energy models via chemical reaction neural network Artificial neural network ; Biological system ; Estimation ; Activation energy ; Chemical reaction ; Chemistry ; Computer science ; Environmental science ; Artificial intelligence ; Physical chemistry ; Organic chemistry ; Engineering ; Biology Thermal and Kinetic Analysis ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Yuhan Lin | Ushna | Quan Lin | Chongyang Wei | Yue Wang | Shouying Huang | Xing Chen | Xinbin Ma
Source: Journal of Catalysis (article, 2024-12-22)
Field: Materials Science — Materials Chemistry

Machine learning descriptors for CO activation on iron-based Fischer − Tropsch catalysts Fischer–Tropsch process ; Chemistry ; Catalysis ; Chemical engineering ; Organic chemistry ; Selectivity Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Daniel Julian | Rian Koots | Jesús Pérez-Ríos
Source: Physical review. A/Physical review, A (article, 2024-09-16)
Field: Materials Science — Materials Chemistry

Machine-learning models for atom-diatom reactions across isotopologues This work shows that feed-forward neural networks can predict the final rovibrational state distributions of inelastic and reactive processes of the reaction of $\mathrm{Ca}+{\mathrm{H}}_{2}\ensuremath{\rightarrow}\mathrm{CaH}+\mathrm{H}$ in the hyperthermal regime, relevant for buffer gas chemistry. Furthermore, these models can be extended to the isotopologues of the reaction involving deuterium and tritium. In addition, we develop a neural network model that can learn across the chemical space based on the isotopologues of hydrogen. The model can predict the outcome of a reaction whose reactants have never been seen. This is done by training on the $\mathrm{Ca}+{\mathrm{H}}_{2}$ and $\mathrm{Ca}+{\mathrm{T}}_{2}$ reactions and subsequently predicting the $\mathrm{Ca}+{\mathrm{D}}_{2}$ reaction. Isotopologue ; Diatom ; Atom (system on chip) ; Computer science ; Artificial intelligence ; Chemistry ; Geology ; Oceanography ; Parallel computing ; Molecule Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Takamitsu Ishiyama | Takashi Suemasu | Kaoru Toko
Source: Scientific Reports (article, 2024-08-21)
Field: Materials Science — Materials Chemistry

Semantic segmentation in crystal growth process using fake micrograph machine learning Microscopic evaluation is one of the most effective methods in materials research. High-quality images are essential to analyze microscopic images using artificial intelligence. To overcome this challenge, we propose the machine learning of "fake micrographs" in this study. To verify the effectiveness of this method, we chose to analyze the optical microscopic images of the crystal growth process of a Ge thin film, which is a material in which it is difficult to obtain a contrast between the crystal and amorphous states. By learning the automatically generated fake micrographs that mimic the crystal growth process, the machine learning model can now identify the low-resolution real micrographs as crystalline or amorphous. Comparing the three types of machine learning models, it was found that ResUNet ++ exhibited high accuracy, exceeding 90%. The technology developed in this study for the automatic and rapid analysis of low-resolution images is widely helpful in material research. Artificial intelligence ; Micrograph ; Computer science ; Crystal (programming language) ; Process (computing) ; Amorphous solid ; Segmentation ; Materials science ; Resolution (logic) ; Machine vision ; Machine learning ; Computer vision ; Crystallography ; Chemistry ; Scanning electron microscope ; Composite material Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Cell Image Analysis Techniques
Authors: Orhadahwe TA
Source: Physical Science & Biophysics Journal (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Application of Machine Learning Tools to Material Science: A Mini-Review Machine learning has become a global trend in artificial intelligence and computing. Its application cuts across different industrial verticals. The application of machine learning algorithms in material science and related fields is becoming a topic of interest to many researchers. However, unlike other fields such as health, sports, communication, and agriculture, the use of machine learning in material science is still in its ideation stage. This mini-review is designed to explore some of those machine learning tools that have been applied to material science, the importance of machine learning in material science, and the challenges limiting the implementation of machine learning techniques in material science. One of the major findings from this review is that the limited availability of material science data is a major challenge to the implementation of machine learning algorithms. A specialized material science data repository was recommended. Computer science ; Mathematics education ; Engineering ; Psychology Machine Learning in Materials Science ; Mineral Processing and Grinding
Authors: Lanting Xu | Jiazhou Zhu | Xiaodong Shen | Jialei Chai | Lei Shi | Bing Wu | Wei Li | Dawei Ma
Source: Angewandte Chemie (article, 2024-08-27)
Field: Chemistry — Organic Chemistry

6‐Hydroxy Picolinohydrazides Promoted Cu(I)‐Catalyzed Hydroxylation Reaction in Water: Machine‐Learning Accelerated Ligands Design and Reaction Optimization Abstract Hydroxylated (hetero)arenes are privileged motifs in natural products, materials, small‐molecule pharmaceuticals and serve as versatile intermediates in synthetic organic chemistry. Herein, we report an efficient Cu(I)/6‐hydroxy picolinohydrazide‐catalyzed hydroxylation reaction of (hetero)aryl halides (Br, Cl) in water. By establishing machine learning (ML) models, the design of ligands and optimization of reaction conditions were effectively accelerated. The N ‐(1,3‐dimethyl‐9 H ‐ carbazol‐9‐yl)‐6‐hydroxypicolinamide ( L32 , 6‐HPA‐DMCA) demonstrated high efficiency for (hetero)aryl bromides, promoting hydroxylation reactions with a minimal catalyst loading of 0.01 mol % (100 ppm) at 80 °C to reach 10000 TON; for substrates containing sensitive functional groups, the catalyst loading needs to be increased to 3.0 mol % under near‐room temperature conditions. N ‐(2,7‐Di‐ tert ‐butyl‐9 H ‐carbazol‐9‐yl)‐6‐hydroxypicolinamide ( L42 , 6‐HPA‐DTBCA) displayed superior reaction activity for chloride substrates, enabling hydroxylation reactions at 100 °C with 2–3 mol % catalyst loading. These represent the state of art for both lowest catalyst loading and temperature in the copper‐catalyzed hydroxylation reactions. Furthermore, this method features a sustainable and environmentally friendly solvent system, accommodates a wide range of substrates, and shows potential for developing robust and scalable synthesis processes for key pharmaceutical intermediates. Hydroxylation ; Catalysis ; Chemistry ; Combinatorial chemistry ; Reaction conditions ; Organic chemistry ; Enzyme Chemical Synthesis and Reactions ; Oxidative Organic Chemistry Reactions
Authors: Haoke Qiu | Zhao‐Yan Sun
Source: ChemRxiv (preprint, 2024-08-20)
Field: Materials Science — Materials Chemistry

Is BigSMILES the Friend of Polymer Machine Learning? The inherent randomness of polymers has long posed challenges for their representation learning in polymer machine learning (ML). The Simplified Molecular-Input Line-Entry System (SMILES) notation, which has excelled in small molecule research, unfortunately, struggles to flexibly capture the complexity of polymer structures, such as random block copolymers. Recently, BigSMILES and its extensions have paved the way for more accurate descriptions of polymer structures. However, whether BigSMILES outperforms SMILES in polymer ML workflows has yet to be systematically explored and demonstrated. To fill this scientific gap, we conducted extensive experiments investigating this question, encompassing a variety of polymer property prediction and inverse design tasks based on both image and text inputs. Our findings reveal that in 11 tasks involving homopolymer systems, BigSMILES-based ML workflows exhibit performance comparable to or even exceeding that of SMILES, underscoring the utility of BigSMILES in representing polymer structures. Furthermore, BigSMILES offers a more compact textual representation compared to SMILES, significantly reducing the computational cost of model training, particularly for large language models. Through these comprehensive experiments, we demonstrate that BigSMILES can achieve performance on par with SMILES, while also facilitating faster model training and reducing energy consumption, which could have a substantial impact on a wide range of polymer tasks in the future, including property prediction (and classification) and polymer generation across various polymer types. Polymer ; Psychology ; Computer science ; Cognitive science ; Artificial intelligence ; Polymer science ; Materials science ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Haoyu Yang | Juanli Zhao | Qiankun Wang | Bin Liu | Wei Luo | Ziqi Sun | Ting Liao
Source: arXiv (Cornell University) (preprint, 2024-02-06)
Field: Materials Science — Materials Chemistry

Convolutional Neural Networks and Volcano Plots: Screening and Prediction of Two-Dimensional Single-Atom Catalysts Single-atom catalysts (SACs) have emerged as frontiers for catalyzing chemical reactions, yet the diverse combinations of active elements and support materials, the nature of coordination environments, elude traditional methodologies in searching optimal SAC systems with superior catalytic performance. Herein, by integrating multi-branch Convolutional Neural Network (CNN) analysis models to hybrid descriptor based activity volcano plot, 2D SAC system composed of diverse metallic single atoms anchored on six type of 2D supports, including graphitic carbon nitride, nitrogen-doped graphene, graphene with dual-vacancy, black phosphorous, boron nitride, and C2N, are screened for efficient CO2RR. Starting from establishing a correlation map between the adsorption energies of intermediates and diverse electronic and elementary descriptors, sole singular descriptor lost magic to predict catalytic activity. Deep learning method utilizing multi-branch CNN model therefore was employed, using 2D electronic density of states as input to predict adsorption energies. Hybrid-descriptor enveloping both C- and O-types of CO2RR intermediates was introduced to construct volcano plots and limiting potential periodic table, aiming for intuitive screening of catalyst candidates for efficient CO2 reduction to CH4. The eDOS occlusion experiments were performed to unravel individual orbital contribution to adsorption energy. To explore the electronic scale principle governing practical engineering catalytic CO2RR activity, orbitalwise eDOS shifting experiments based on CNN model were employed. The study involves examining the adsorption energy and, consequently, catalytic activities while varying supported single atoms. This work offers a tangible framework to inform both theoretical screening and experimental synthesis, thereby paving the way for systematically designing efficient SACs. Convolutional neural network ; Volcano ; Atom (system on chip) ; Catalysis ; Computer science ; Artificial intelligence ; Chemistry ; Geology ; Parallel computing ; Seismology Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ejaz Hussain | Mudassir Hussain Tahir | Avijit Dalal | Sumaira Naeem | Islam H. El Azab
Source: Physica B Condensed Matter (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

Polymer design with enhanced crystallization tendency aided by machine learning Crystallization ; Materials science ; Polymer ; Crystallization of polymers ; Computer science ; Polymer science ; Chemical engineering ; Process engineering ; Composite material Machine Learning in Materials Science ; Polymer crystallization and properties ; Injection Molding Process and Properties
Authors: Motoki Horibe | Naoto Tanibata | Hayami Takeda | Masanobu Nakayama
Source: ACS Applied Materials & Interfaces (article, 2024-09-25)
Field: Chemistry — Electrochemistry

Universal Neural Network Potential-Driven Molecular Dynamics Study of CO<sub>2</sub>/O<sub>2</sub> Evolution at the Ethylene Carbonate/Charged–Electrode Interface Long-term durability and safety are required to develop Li-ion batteries that can operate at high voltages. However, side reactions, including the release of O<sub>2</sub> from the electrode and CO<sub>2</sub> from the organic electrolyte, occur at the positive-electrode/electrolyte interface during charging at high voltages. In this study, universal neural network potential (UNNP)-driven molecular dynamics (MD) calculations are used to investigate the mechanism of the reaction between Li<sub><i>x</i></sub>CoO<sub>2</sub> (0 ≤ <i>x</i> ≤ 1) or Li<sub><i>x</i></sub>NiO<sub>2</sub> (0 ≤ <i>x</i> ≤ 1), as the positive-electrode material, and an ethylene-carbonate-based electrolyte, with a solid-liquid interface composed of ∼1700 atoms. Molecular CO<sub>2</sub> and O<sub>2</sub> evolve from the partially or fully Li-deintercalated Li<sub><i>x</i></sub>NiO<sub>2</sub>, while no gas-evolution reactions are observed for Li<sub><i>x</i></sub>CoO<sub>2</sub>. Hence, compared Li<sub><i>x</i></sub>NiO<sub>2</sub>, the LiCoO<sub>2</sub> electrode is more stable toward the decomposition of ethylene carbonate in the charged state. The decomposition reactions at the solid-liquid interface during charging are also analyzed using a NN force field. This study provides a robust approach involving MD simulations using UNNP to better understand the side reactions in electrochemical devices, which can guide manufacturers in selecting appropriate materials. Materials science ; Ethylene carbonate ; Electrode ; Carbonate ; Molecular dynamics ; Ethylene ; Interface (matter) ; Chemical physics ; Nanotechnology ; Chemical engineering ; Physical chemistry ; Electrolyte ; Computational chemistry ; Organic chemistry ; Catalysis ; Metallurgy ; Wetting ; Composite material ; Physics Electrochemical Analysis and Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Zhen Liu | Jessica L. Vinskus | Yue Fu | Peng Liu | Kevin J. T. Noonan | Olexandr Isayev
Source: ChemRxiv (preprint, 2024-11-19)
Field: Materials Science — Materials Chemistry

Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions Ring strain energy (RSE) is crucial for understanding molecular reactivity. However, quantitatively determining RSE through experiments or quantum mechanics is resource-intensive, limiting its application on a large scale. We developed a physics-based workflow and a data-driven graph neural network (GNN) capable of reliably predicting RSE in minutes or milliseconds, respectively. For each molecule, the workflow first identifies low-energy conformers, then computes the RSE using the AIMNet2 machine learning interatomic potentials. We validated the approach both computationally and experimentally. Compared to the ωB97M-D4/Def2-TZVPP method, the workflow achieved an R² value of 0.997 and a mean absolute error (MAE) of 0.896 kcal/mol. Using this workflow, we distinguished reactive from non-reactive molecules in copper-free click chemistry and ring-opening metathesis polymerization, demonstrating the workflow's generalizability to diverse molecules. Furthermore, we compiled "RSE Atlas," a computational database of 16,905 single-ring molecules, providing a rich resource for examining factors influencing RSE. Employing this dataset, we trained a GNN that predicts RSE in milliseconds using only 2D molecular information. Our methods render RSE a readily computable property for on-the-fly applications in experimental and computational work. Strain (injury) ; Ring (chemistry) ; Computer science ; Ring strain ; Artificial intelligence ; Chemistry ; Biology Machine Learning in Materials Science
Authors: Xianpeng Wang | Yanxia Ma | Youyong Li | Lu Wang | Lifeng Chi
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Discovery of highly efficient dual-atom catalysts for propane dehydrogenation assisted by machine learning Propane dehydrogenation (PDH) is a highly efficient approach for industrial production of propylene, and the dual-atom catalysts (DACs) provide new pathways in advancing atomic catalysis for PDH with dual active sites. In this work, we have developed an efficient strategy to identify promising DACs for PDH reaction by combining high-throughput density functional theory (DFT) calculations and the machine-learning (ML) technique. By choosing the γ-Al<sub>2</sub>O<sub>3</sub>(100) surface as the substrate to anchor dual metal atoms, 435 kinds of DACs have been considered to evaluate their PDH catalytic activity. Four ML algorithms are employed to predict the PDH activity and determine the relationship between the intrinsic characteristics of DACs and the catalytic activity. The promising catalysts of CuFe, CuCo and CoZn DACs are finally screened out, which are further validated by the whole kinetic reaction calculations, and the highly efficient performance of DACs is attributed to the synergistic effects and interactions between the paired active sites. Dehydrogenation ; Propane ; Catalysis ; Dual (grammatical number) ; Density functional theory ; Atom (system on chip) ; Throughput ; Combinatorial chemistry ; Chemistry ; Work (physics) ; Nanotechnology ; Materials science ; Computer science ; Computational chemistry ; Organic chemistry ; Physics ; Thermodynamics ; Parallel computing ; Telecommunications Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Chunbo Lin | Han Zheng
Source: Advances in Engineering Technology Research (article, 2024-04-11)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Strategies for Accurate Prediction of Thermal Conductivity in Polymeric Systems Polymers, integral to advancements in high-tech fields, necessitate the study of their thermal conductivity (TC) to enhance material attributes and energy efficiency. The TC of polymers obtained by molecular dynamics (MD) calculations and experimental measurements is slow, and it is difficult to screen polymers with specific TC in a wide range. Existing machine learning (ML) techniques for determining polymer TC suffer from the problems of too large feature space and cannot guarantee very high accuracy. In this work, we leverage TCs from accessible datasets to decode the Simplified Molecular Input Line Entry System (SMILES) of polymers into ten features of distinct physical significance. A novel evaluation model for polymer TC is formulated, employing four ML strategies. The Gradient Boosting Decision Tree (GBDT)-based model, a focal point of our design, achieved a prediction accuracy of R2=0.88 on a dataset containing 400 polymers. Furthermore, we used an interpretable ML approach to discover the significant contribution of quantitative estimate of drug-likeness and number of rotatable bonds features to TC, and analyzed the physical mechanisms involved. The ML method we developed provides a new idea for physical modeling of polymers, which is expected to be generalized and applied widely in constructing polymers with specific TCs and predicting all other properties of polymers. Leverage (statistics) ; Polymer ; Computer science ; Boosting (machine learning) ; Artificial intelligence ; Gradient boosting ; Biological system ; Machine learning ; Materials science ; Random forest ; Composite material Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Shaoju Cao | M. Hong | Junyuan Hu | Zhenjiang Li | Jin Huang | Kai Guo
Source: Polymer Chemistry (article, 2024-01-01)
Field: Materials Science — Biomaterials

Interpretable machining learning assisted insights into bifunctional squaramide catalyzed ring-opening polymerization of lactide A bifunctional squaramide catalyst with tunable activity enables the controlled ring-opening polymerization of lactide. Combining bench experiments and interpretable machine learning offer insights into catalyst structures, promoting catalyst design. Squaramide ; Bifunctional ; Polymerization ; Ring-opening polymerization ; Ring (chemistry) ; Catalysis ; Polymer chemistry ; Chemistry ; Polymer ; Organic chemistry ; Organocatalysis biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis
Authors: Khushboo Yadava | Shrey Srivastava | Ashutosh Yadav
Source: The Canadian Journal of Chemical Engineering (article, 2024-08-20)
Field: Chemistry — Inorganic Chemistry

Molecular simulations and deep neural networks‐based interpretable machine learning modelling of reverse adsorptive <scp>MOFs</scp> for ethane/ethylene separation Abstract The thermal decomposition of ethane (C 2 H 6 ) and the steam cracking of fossil fuels are the main sources of ethylene (C 2 H 4 ). However, it usually contains 5%–9% of C 2 H 6 residue, which must be reduced to ensure its utilization during polymerization. C 2 H 6 and C 2 H 4 have comparable kinetic diameters and boiling points (C 2 H 6 : 4.44, 184.55 K; C 2 H 4 : 4.16, 169.42 K), which makes the separation process very difficult. This contribution employs a methodology that integrates machine learning (ML) with Monte Carlo simulations to evaluate the ddmof database to develop a predictive model for separating ethane (C 2 H 6 ) and ethylene (C 2 H 4 ). The ML model's input is the metal–organic frameworks (MOFs) chemical and structural descriptors. The grand canonical Monte Carlo (GCMC) simulations in RASPA software were carried out to calculate the equilibrium adsorption of ethane and ethylene. Different ML models such as random forest, decision tree, and deep neural network models have been tested to estimate the selectivity and ethane uptake from the MOF data being generated. Interpretable ML model using SHapley Additive exPlanations (SHAP) is developed for the better understanding of the impact of the parameters on selectivity and ethane uptake. A user‐friendly graphical user interface ( GUI ) is presented, allowing users to predict the ethane uptake and selectivity of MOFs simply by entering the values of chemical and structural descriptors. Artificial neural network ; Ethylene ; Computer science ; Chemistry ; Artificial intelligence ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science