jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shingo Harada | Hiroki Takenaka | Tsubasa Ito | Haruki Kanda | Tetsuhiro Nemoto
Source: Nature Communications (article, 2024-03-14)
Field: Chemistry — Organic Chemistry

Valence-isomer selective cycloaddition reaction of cycloheptatrienes-norcaradienes The rapid and precise creation of complex molecules while controlling multiple selectivities is the principal objective in synthetic chemistry. Combining data science and organic synthesis to achieve this goal is an emerging trend, but few examples of successful reaction designs are reported. We develop an artificial neural network regression model using bond orbital data to predict chemical reactivities. Actual experimental verification confirms cycloheptatriene-selective [6 + 2]-cycloaddition utilizing nitroso compounds and norcaradiene-selective [4 + 2]-cycloaddition reactions employing benzynes. Additionally, a one-pot asymmetric synthesis is achieved by telescoping the enantioselective dearomatization of non-activated benzenes and cycloadditions. Computational studies provide a rational explanation for the seemingly anomalous occurrence of thermally prohibited suprafacial [6 + 2]-cycloaddition without photoirradiation. Cycloaddition ; Valence (chemistry) ; Enantioselective synthesis ; Chemistry ; Computational chemistry ; Combinatorial chemistry ; Valence bond theory ; Molecule ; Molecular orbital ; Organic chemistry ; Catalysis Oxidative Organic Chemistry Reactions ; Organic Chemistry Cycloaddition Reactions ; Asymmetric Synthesis and Catalysis
Authors: Jie Zhou | Chaolin Wu | Xi He | Lingjun Zhang | Xiangnan Gong | Wei Ren | Shiming Lv | Xin Zhang | Anping Liu | Yingzhou Huang
Source: Surfaces and Interfaces (article, 2024-03-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Single AgNF@ZIF-8 nanoparticle for deep learning assisted SERS detection of gaseous molecule Materials science ; Nanoparticle ; Plasmon ; Nanotechnology ; Molecule ; Surface-enhanced Raman spectroscopy ; Nanoflower ; Zeolitic imidazolate framework ; Raman spectroscopy ; Surface plasmon resonance ; Nanostructure ; Raman scattering ; Metal-organic framework ; Adsorption ; Optoelectronics ; Organic chemistry ; Chemistry ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning and ELM ; Advanced Chemical Sensor Technologies
Authors: Zirui Bian | Tianzhe Bao | Xuequan Sun | Ning Wang | Qian Mu | Ting Jiang | Zhongxiang Yu | Junhang Ding | Ting Wang | Qihui Zhou
Source: International Journal of Nanomedicine (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots Thus, our results demonstrate that ML-based high-throughput theoretical calculation could be used to predict and decode the relationship between CD properties and the anti-bacterial effect, accelerating the development of high-performance nanoparticles and potential clinical translation. Support vector machine ; Machine learning ; Nanoparticle ; Antimicrobial ; Computer science ; Minimum inhibitory concentration ; Artificial intelligence ; Biochemical engineering ; Nanotechnology ; Materials science ; Biology ; Microbiology Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Advanced Nanomaterials in Catalysis
Authors: Nabil Khossossi | Poulumi Dey
Source: Journal of Energy Chemistry (article, 2024-09-16)
Field: Materials Science — Materials Chemistry

Few-shot learning for screening 2D Ga2CoS4− supported single-atom catalysts for hydrogen production A machine learning-driven high-throughput screening reveals efficient single-atom catalysts on 2D Ga 2 CoS 4− x surfaces, optimizing hydrogen evolution reaction catalysis and providing alternatives to platinum-based catalysts. Hydrogen generation and related energy applications heavily rely on the hydrogen evolution reaction (HER), which faces challenges of slow kinetics and high overpotential. Efficient electrocatalysts, particularly single-atom catalysts (SACs) on two-dimensional (2D) materials, are essential. This study presents a few-shot machine learning (ML) assisted high-throughput screening of 2D septuple-atomic-layer Ga 2 CoS 4− x supported SACs to predict HER catalytic activity. Initially, density functional theory (DFT) calculations showed that 2D Ga 2 CoS 4 is inactive for HER. However, defective Ga 2 CoS 4− x ( x = 0–0.25) monolayers exhibit excellent HER activity due to surface sulfur vacancies (SVs), with predicted overpotentials (0–60 mV) comparable to or lower than commercial Pt/C, which typically exhibits an overpotential of around 50 mV in the acidic electrolyte, when the concentration of surface SV is lower than 8.3%. SVs generate spin-polarized states near the Fermi level, making them effective HER sites. We demonstrate ML-accelerated HER overpotential predictions for all transition metal SACs on 2D Ga 2 CoS 4− x . Using DFT data from 18 SACs, an ML model with high prediction accuracy and reduced computation time was developed. An intrinsic descriptor linking SAC atomic properties to HER overpotential was identified. This study thus provides a framework for screening SACs on 2D materials, enhancing catalyst design. Single shot ; Shot (pellet) ; Catalysis ; Hydrogen production ; Production (economics) ; Hydrogen atom ; Atom (system on chip) ; Hydrogen ; Materials science ; Nanotechnology ; Chemistry ; Computer science ; Physics ; Metallurgy ; Optics ; Economics ; Organic chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; MXene and MAX Phase Materials
Authors: Jin‐Hoon Yang | Ji‐Young Lee | Hajin Kwon | Eun‐Ho Sohn | Hyunju Chang | Seunghun Jang
Source: Macromolecular Rapid Communications (article, 2024-05-25)
Field: Materials Science — Materials Chemistry

High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data Machine learning can be used to predict the properties of polymers and explore vast chemical spaces. However, the limited number of available experimental datasets hinders the enhancement of the predictive performance of a model. This study proposes a machine learning approach that leverages transfer learning and ensemble modeling to efficiently predict the glass transition temperature (T<sub>g</sub>) of fluorinated polymers and guide the design of high T<sub>g</sub> copolymers. Initially, the quantum machine 9 (QM9) dataset is employed for model pretraining, thus providing robust molecular representations for the subsequent fine-tuning of a specialized copolymer dataset. Ensemble modeling is used to further enhance prediction robustness and reliability, effectively addressing the problems owing to the limited and unevenly distributed nature of the copolymer dataset. Finally, a fine-tuned ensemble model is used to navigate a vast chemical space comprising 61 monomers and identify promising candidates for high T<sub>g</sub> fluorinated polymers. The model predicts 247 entries capable of achieving a T<sub>g</sub> over 390 K, of which 14 are experimentally validated. This study demonstrates the potential of machine learning in material design and discovery, highlighting the effectiveness of transfer learning and ensemble modeling strategies for overcoming the challenges posed by small datasets in complex copolymer systems. Copolymer ; Robustness (evolution) ; Polymer ; Computer science ; Ensemble forecasting ; Ensemble learning ; Glass transition ; Chemical space ; Materials science ; Machine learning ; Transfer of learning ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Yuchen Gao | Yu‐Hang Yuan | Suozhi Huang | Nan Yao | Legeng Yu | Yao‐Peng Chen | Qiang Zhang | Xiang Chen
Source: Angewandte Chemie (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes Abstract Developing rechargeable batteries that operate within a wide temperature range and possess high safety has become necessary with increasing demands. Rapid and accurate assessment of the melting points (MPs), boiling points (BPs), and flash points (FPs) of electrolyte molecules is essential for expediting battery development. Herein, we introduce Knowledge‐based electrolyte Property prediction Integration (KPI), a knowledge–data dual‐driven framework for molecular property prediction of electrolytes. Initially, the KPI collects molecular structures and properties, and then automatically organizes them into structured datasets. Subsequently, interpretable machine learning further explores the structure–property relationships of molecules from a microscopic perspective. Finally, by embedding the discovered knowledge into property prediction models, the KPI achieved very low mean absolute errors of 10.4, 4.6, and 4.8 K for MP, BP, and FP predictions, respectively. The KPI reached state‐of‐the‐art results in 18 out of 20 datasets. Utilizing molecular neighbor search and high‐throughput screening, 15 and 14 promising molecules, with and without Chemical Abstracts Service Registry Number, respectively, were predicted for wide‐temperature‐range and high‐safety batteries. The KPI not only accurately predicts molecular properties and deepens the understanding of structure–property relationships but also serves as an efficient framework for integrating artificial intelligence and domain knowledge. Battery (electricity) ; Dual (grammatical number) ; Electrolyte ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Physics ; Electrode ; Physical chemistry ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Jiawei Guo | Tyler Sours | Sam Holton | Chenghan Sun | Ambarish Kulkarni
Source: ACS Catalysis (article, 2024-01-10)
Field: Materials Science — Materials Chemistry

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]<sup>2+</sup> sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]<sup>2+</sup>-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community. Catalysis ; Zeolite ; Methane ; Chemistry ; Biochemical engineering ; Computer science ; Anaerobic oxidation of methane ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Z B Wang | Yuhang Jing | Yi Sun | Weiqi Li | Jianqun Yang | Xingji Li
Source: Computational Materials Science (article, 2024-01-26)
Field: Materials Science — Materials Chemistry

Hydration induced mechanical degradation in the Y-doped BaZrO3 solid oxide Materials science ; Density functional theory ; Doping ; Oxide ; Enthalpy ; Electrolyte ; Chemical bond ; Bulk modulus ; Thermodynamics ; Elastic modulus ; Lattice constant ; Modulus ; Physical chemistry ; Chemical physics ; Computational chemistry ; Chemistry ; Composite material ; Organic chemistry ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Chemical Looping and Thermochemical Processes
Authors: Andrea Martini | Janis Timoshenko | Philipp Grosse | Clara Rettenmaier | Dorottya Hursán | Gabriele Deplano | Hyo Sang Jeon | Arno Bergmann | Beatriz Roldán Cuenya
Source: Physical Review Letters (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

Adsorbate Configurations in Ni Single-Atom Catalysts during <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>CO</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> Electrocatalytic Reduction Unveiled by <i>Operando</i> XAS, XES, and Machine Learning Nickel and nitrogen co-doped carbon (Ni-N-C) catalysts are attracting attention due to their exceptionally high performance in the electrocatalytic reduction of CO_{2}(CO_{2}RR) to CO. However, the direct experimental insight into the working mechanism of these catalysts is missing, hindering our fundamental understanding and their further improvement. This work sheds light on the nature of adsorbates forming under CO_{2}RR at singly dispersed Ni sites. In particular, operando high energy resolution fluorescence detected x-ray absorption near edge structure (HERFD-XANES) at the Ni K-edge together with valence-to-core x-ray emission spectroscopy (vtc-XES) and x-ray absorption (XAS) at the Ni L_{3}-edge were employed to unveil the structure and electronic properties of the reaction intermediates. These techniques, coupled with unsupervised and supervised machine learning methodologies and density functional theory, enabled a comprehensive characterization of the local atomistic and electronic structure of the working Ni-N-C catalysts. Specifically, we were able to distinguish between the structural and electronic changes of the Ni sites associated with the CO_{2}RR functionality from the effect of radiation-induced damage, providing direct insight into the bond formation between the Ni centers and CO_{2}RR intermediates such as CO adsorbates. Atom (system on chip) ; Computer science ; Operating system Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Amir Hossein Sheikhshoaei | Ali Khoshsima | Davood Zabihzadeh
Source: Chemical Thermodynamics and Thermal Analysis (article, 2024-12-10)
Field: Materials Science — Materials Chemistry

Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models • ML/DL models accurately predict the specific heat capacity of SrPr 4 (SiO 4 ) 3 O. • A Hybrid model combining RF and DBN models outperforms other ones in accuracy. • The application of machine/deep learning in thermophysical property prediction. • The Hybrid model gives an RMSE of 0.47 and R 2 of 0.9999 in heat capacity prediction. • The Leverage method validated the integrity of the data used and hybrid model A thorough examination of the specific heat capacity of strontium-praseodymium oxysilicate, which serves as a vital thermophysical parameter was investigated. This parameter is important in enhancing the performance and efficiency of heat transfer-based equipment, as well as applications in catalysis, insulation materials, and advanced ceramics. Machine learning (ML) offers a potent solution for forecasting diverse processes using both data-driven and knowledge-based methods. In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. Additionally, we introduce a hybrid model combining these two models, which enhances the accuracy of predicting the heat capacity of strontium-praseodymium oxysilicate. Specifically, the Hybrid model achieved an AAPRE of 0.42213, an RMSE of 0.38914, and a near-perfect R 2 value of 0.9999. The analysis indicated that the Hybrid model could accurately anticipate how the heat capacity of strontium-praseodymium oxysilicate would change. In conclusion, outlier detection was performed using the Leverage method to identify any anomalous data points, thereby illustrating the effective range of the proposed Hybrid model. Praseodymium ; Strontium ; Deep learning ; Artificial intelligence ; Computer science ; Materials science ; Chemistry ; Metallurgy Thermal and Kinetic Analysis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yinan Xu | Yezhi Jin | Jireh S. García Sánchez | Gustavo R. Pérez-Lemus | Pablo F. Zubieta Rico | Massimiliano Delferro | Juan Pablo
Source: The Journal of Physical Chemistry Letters (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

A Molecular View of Methane Activation on Ni(111) through Enhanced Sampling and Machine Learning A combination of machine learned interatomic potentials (MLIPs) and enhanced sampling simulations is used to investigate the activation of methane on a Ni(111) surface. The work entails the development and iterative refinement of MLIPs, initially trained on a data set constructed via <i>ab initio</i> molecular dynamics simulations, supplemented by adaptive biasing forces, to enrich the sampling of catalytically relevant configurations. Our results reveal that upon incorporation of collective variables that capture the behavior of the reactant molecule, as well as additional frames that describe the dynamic response of the catalytic surface, it is possible to enhance considerably the accuracy of predicted energies and forces. By employing enhanced sampling schemes in the refinement of the MLIP, we systematically explore the potential energy surface, leading to a refined MLIP capable of predicting density functional theory-level energies and forces and replicating key geometric characteristics of the catalytic system. The resulting free energy landscapes at several temperatures provide a detailed view of the thermodynamics and dynamics of methane activation. Specifically, as methane approaches and dissociates on the catalytic surface, the process involves the dynamic interplay of CH<sub>4</sub> and the Ni catalyst that includes both enthalpic and entropic contributions. The progression toward the transition state involves a CH<sub>4</sub> moiety that is increasingly restrained in its ability to rotate or translate, while the stage following the transition state is characterized by a notable rise of the Ni atom that interacts with the cleaved C-H bond. This leads to an increase in the mobility of the adsorbed species, a feature that becomes more pronounced at higher temperatures. Methane ; Sampling (signal processing) ; Computer science ; Chemistry ; Chemical engineering ; Materials science ; Engineering ; Computer vision ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Luneng Zhao | Yanhan Ren | Xiaoran Shi | Hongsheng Liu | Zhi Gen Yu | Junfeng Gao | Jijun Zhao
Source: Smart Molecules (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS<sub>2</sub> with active machine learning Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition. They will significantly impact the electronic/magnetic properties. Moreover, surface supported atoms are also widely explored for high active and selecting catalysts. Severe deformation, even dipping into the surface, of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces. However, most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions. Here, we develop an accurate graph neural network machine learning potential (MLP) from ab initio data by active learning architecture through fine-tuning pre-trained models, and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters (1-8 atoms) on perfect and various defective MoS<sub>2</sub> monolayers. Interestingly, Mo clusters can always sink and embed themselves into MoS<sub>2</sub> layers. In contrast, S clusters float on perfect surfaces. On the defective surface, a few S atoms will fill the vacancy and rest S clusters float on the top. Such significant structural reconstructions should be carefully taken into account. Float glass ; Chemical physics ; Materials science ; Vacancy defect ; Ab initio ; Nanostructure ; Branching (polymer chemistry) ; Surface (topology) ; Monte Carlo method ; Molecular physics ; Nanotechnology ; Crystallography ; Chemistry ; Geometry 2D Materials and Applications ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Christopher R. Taylor | Patrick W. V. Butler | Graeme M. Day
Source: Faraday Discussions (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

Predictive crystallography at scale: mapping, validating, and learning from 1000 crystal energy landscapes Computational crystal structure prediction (CSP) is an increasingly powerful technique in materials discovery, due to its ability to reveal trends and permit insight across the possibility space of crystal structures of a candidate molecule, beyond simply the observed structure(s). In this work, we demonstrate the reliability and scalability of CSP methods for small, rigid organic molecules by performing in-depth CSP investigations for over 1000 such compounds, the largest survey of its kind to-date. We show that this highly-efficient force-field-based CSP approach is superbly predictive, locating 99.4% of observed experimental structures, and ranking a large majority of these (74%) as among the most stable possible structures (to within uncertainty due to thermal effects). We present two examples of insights such large predicted datasets can permit, examining the space group preferences of organic molecular crystals and rationalising empirical rules concerning the spontaneous resolution of chiral molecules. Finally, we exploit this large and diverse dataset for developing transferable machine-learned energy potentials for the organic solid state, training a neural network lattice energy correction to force field energies that offers substantial improvements to the already impressive energy rankings, and a MACE equivariant message-passing neural network for crystal structure re-optimisation. We conclude that the excellent performance and reliability of the CSP workflow enables the creation of very large datasets of broad utility and explanatory power in materials design. Scale (ratio) ; Computer science ; Data science ; Cartography ; Geography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies