jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chandrasekhar Nettem | Gopalan Rajaraman
Source: Inorganic Chemistry Frontiers (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

How do quantum chemical descriptors shape hydrogen atom abstraction reactivity in cupric-superoxo species? A combined DFT and machine learning perspective Employing a fusion of DFT and ML methodologies incorporating fifteen quantum chemical descriptors, we have elucidated the factors governing the reactivity of cupric-superoxo species. Reactivity (psychology) ; Quantum chemical ; Perspective (graphical) ; Abstraction ; Hydrogen atom abstraction ; Computational chemistry ; Chemistry ; Atom (system on chip) ; Quantum ; Hydrogen atom ; Quantum chemistry ; Hydrogen ; Quantum mechanics ; Computer science ; Organic chemistry ; Molecule ; Physics ; Group (periodic table) ; Artificial intelligence ; Reaction mechanism ; Epistemology ; Philosophy ; Catalysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Kaige Ding | Zhinan Zhao | Siyuan Ma | Yanqing Qiu | Tingting Lang | Ting Chen
Source: Physical Communication (article, 2024-07-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerating optimization of terahertz metasurface design using principal component analysis in conjunction with deep learning networks Computer science ; Terahertz radiation ; Principal component analysis ; Conjunction (astronomy) ; Component (thermodynamics) ; Principal (computer security) ; Artificial intelligence ; Deep learning ; Optoelectronics ; Physics ; Computer security Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Millimeter-Wave Propagation and Modeling
Authors: Yu Gu | Zexi Zhang | Tianyi Gao | Rafael Gómez‐Bombarelli | Mao Chen
Source: Angewandte Chemie (article, 2024-07-26)
Field: Chemistry — Organic Chemistry

Low‐Dispersity Polymers via Free Radical Alternating Copolymerization: Effects of Charge‐Transfer‐Complexes Abstract Alternating copolymers are crucial for diverse applications. While dispersity ( Ð , also known as molecular weight distribution, MWD) influences the properties of polymers, achieving low dispersities in alternating copolymers poses a notable challenge via free radical polymerizations (FRPs). In this work, we demonstrated an unexpected discovery that dispersities are affected by the participation of charge transfer complexes (CTCs) formed between monomer pairs during free radical alternating copolymerization, which have inspired the successful synthesis of various alternating copolymers with low dispersities (&gt;30 examples, Ð =1.13–1.39) under visible‐light irradiation. The synthetic method is compatible with binary, ternary and quaternary alternating copolymerizations and is expandable for both fluorinated and non‐fluorinated monomer pairs. DFT calculations combined with model experiments indicated that CTC‐absent reaction exhibits higher propagation rates and affords fewer radical terminations, which could contribute to low dispersities. Based on the integration of Monte Carlo simulation and Bayesian optimization, we established the relationship map between FRP parameter space and dispersity, further suggested the correlation between low dispersities and higher propagation rates. Our research sheds light on dispersity control via FRPs and creates a novel platform to investigate polymer dispersity through machine learning. Copolymer ; Dispersity ; Polymer ; Charge (physics) ; Polymer chemistry ; Chemistry ; Photochemistry ; Materials science ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Innovative Microfluidic and Catalytic Techniques Innovation ; Luminescence and Fluorescent Materials
Authors: Paula Aniceto‐Ocaña | José Marqueses-Rodríguez | José A. Pérez‐Omil | José J. Calvino | Carmen E. Castillo | Miguel López‐Haro
Source: Communications Materials (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Direct quantitative assessment of single-atom metal sites supported on powder catalysts Rational design of effective catalyst demands a profound understanding of active-site structures. Single-atom supported powder catalysts, depicting unique features like enhanced metal dispersion, hold promise in different applications. Here, we present an approach to directly quantify the detailed structural nature of metal sites in single-atom, high surface area, powder catalysts. By combining advanced high-resolution scanning-transmission electron microscopy, deep learning and density functional theory calculations, we determine, with statistical significance, the exact location and coordination environment of Pd single-atoms supported on MgO nanoplates. Our findings reveal a preferential interaction of Pd single-atoms with cationic vacancies (V-centers), followed by occupation of anionic defects on the {001} MgO surface. The former interaction results in stabilization of PdO species within V-centers, while partially embedded Pd states are found in F-defects. This methodology opens a route to the ultimate structural analysis of metal-support interaction effects, key in the design of advanced nanocatalysts for sustainable and energy-efficient processes. Metal active sites are important in catalysts to function in various applications. Here, the detailed structural nature of single-atom metal sites is directly quantified using a combination of high-resolution microscopy, deep learning, and theoretical calculation methods. Catalysis ; Atom (system on chip) ; Metal ; Materials science ; Crystallography ; Chemistry ; Metallurgy ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Materials Characterization Techniques ; Electronic and Structural Properties of Oxides
Authors: Yuanyuan Yuan | Junqiang Ren | Hongtao Xue | Junchen Li | Fuling Tang | Xin Guo | Xuefeng Lu
Source: Small Methods (article, 2024-03-20)
Field: Materials Science — Materials Chemistry

Electronic Properties of CrB/Co<sub>2</sub>CO<sub>2</sub> Superlattices by Multiple Descriptor‐Based Machine Learning Combined with First‐Principles In recent times, newly unveiled 2D materials exhibiting exceptional characteristics, such as MBenes and MXenes, have gained widespread application across diverse domains, encompassing electronic devices, catalysis, energy storage, sensors, and various others. Nonetheless, numerous technical bottlenecks persist in the development of high-performance, structurally flexible, and adjustable electronic device materials. Research investigations have demonstrated that 2D van der Waals superlattices (vdW SLs) structures comprising materials exhibit exceptional electrical, mechanical, and optical properties. In this work, the advantages of both materials are combined and compose the vdW SLs structure of MBenes and MXenes, thus obtaining materials with excellent electronic properties. Furthermore, it integrates machine learning (ML) with first-principles methods to forecast the electrical properties of MBene/MXene superlattice materials. Initially, various configurations of MBene/MXene superlattice materials are explored, revealing that distinct stacking methods exert significant influence on the electronic structure of MBene/MXene materials. Specifically, the BABA-type stacking of CrB (layer A) and Co<sub>2</sub>CO<sub>2</sub> MXene (layer B) is most stable configureation. Subsequently, multiple descriptors of the structure are constructed to predict the density of states of vdW SLs through the employment of ML techniques. The best model achieves a mean absolute error (MAE) as low as 0.147 eV. MXenes ; Superlattice ; Stacking ; van der Waals force ; Materials science ; Electronic structure ; Computer science ; Nanotechnology ; Condensed matter physics ; Optoelectronics ; Physics ; Molecule MXene and MAX Phase Materials ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Christopher D Stubbs | Yeonjoon Kim | Ethan C. Quinn | Raúl Pérez‐Soto | Eugene Y.‐X. Chen | Seonah Kim
Source: Digital Discovery (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

Predicting homopolymer and copolymer solubility through machine learning In this work, we report the development of multiple new machine learning (ML) models to accurately predict homopolymer/copolymer solubility over a diverse set of polymers &amp; solvents, using explainable AI to provide polymer design recommendations. Copolymer ; Solubility ; Polymer science ; Materials science ; Polymer chemistry ; Computer science ; Chemical engineering ; Chemistry ; Polymer ; Organic chemistry ; Composite material ; Engineering Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Junhao Chang | Haoyang Li | Zengtao Chen | James D. Hogan
Source: International Journal of Mechanical Sciences (article, 2024-08-28)
Field: Materials Science — Ceramics and Composites

Decoding ceramic fracture: Atomic defects studies in multiscale simulations Ceramic ; Fracture (geology) ; Materials science ; Decoding methods ; Composite material ; Computer science ; Algorithm Advanced ceramic materials synthesis ; Advanced materials and composites ; Nuclear Materials and Properties
Authors: Meiheng Lv | Yifan Zhang | Runze Liu | Yinhua Ma | Li Liu | Wenze Li | Huaxin Liu | Jianyong Liu
Source: Materials Today Communications (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Exploring the thermal decomposition mechanism of nitromethane via a neural network potential Nitromethane ; Materials science ; Mechanism (biology) ; Thermal decomposition ; Artificial neural network ; Decomposition ; Thermal ; Chemical physics ; Artificial intelligence ; Thermodynamics ; Organic chemistry ; Computer science Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Energetic Materials and Combustion
Authors: Vanessa J. Meraz | Ziyue Zou | Pratyush Tiwary
Source: The Journal of Physical Chemistry B (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck Based Enhanced Sampling We investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations reveal that crystallization in the model colloidal system studied here, with particles interacting through a repulsive screened Coulomb Yukawa potential, proceeds from vapor to dense liquid droplet to crystalline phases across multiple high barriers. Employing a one-dimensional reaction coordinate derived from the State Predictive Information Bottleneck framework, our simulations capture back-and-forth phase transitions across multiple barriers effectively in biased metadynamics simulations. We obtain relative free energy differences between different phases and also quantify the roles of different molecular level features in driving the phase changes. Metadynamics ; Nucleation ; Crystallization ; Molecular dynamics ; Colloid ; Bottleneck ; Chemical physics ; Supersaturation ; Statistical physics ; Yukawa potential ; Materials science ; Phase (matter) ; Computer science ; Physics ; Chemistry ; Thermodynamics ; Computational chemistry ; Physical chemistry Material Dynamics and Properties ; nanoparticles nucleation surface interactions ; Machine Learning in Materials Science
Authors: Yuxinxin Chen | Pavlo O. Dral
Source: ChemRxiv (preprint, 2024-10-08)
Field: Materials Science — Materials Chemistry

AIQM2: Better Reaction Simulations with the 2nd Generation of General-Purpose AI-enhanced Quantum Mechanical Method Reaction simulations provide insight into the mechanisms, guide reaction optimization and catalyst design, and assist in predicting reaction outcomes. Traditional quantum chemical methods for reaction simulation face problems of inferior accuracy and high computational cost. While the rise of the universal machine learning models alleviates the problem of the cost, they mostly remain at the density functional theory level, i.e., typically still far from chemical accuracy. Few of them such as AIQM1 and ANI-1ccx, although targeting coupled cluster level, still reportedly have sub-optimal performance for the reaction barriers. Thus, here we report AIQM2, the general-purpose AI-enhanced Quantum Mechanical Method 2, which utilizes the delta-learning framework to correct the modified GFN2-xTB baseline to the CCSD(T)/CBS level. AIQM2 exhibits overall better performance than its predecessor AIQM1, especially for the description of the transition states and barrier heights, and bypasses common DFT approaches with double-zeta quality basis sets at the cost of semi-empirical methods. The superior speed and accuracy of AIQM2 allow us to propagate a thousand trajectories overnight to revise the product distribution of the bifurcating pericyclic reaction previously investigated by a slower and less accurate B3LYP-D3/6-31G*. AIQM2 is now publicly available in the open-source software MLatom at https://github.com/dralgroup/mlatom and the calculations can be performed online at the https://XACScloud.com. Quantum ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Machine Learning and ELM
Authors: Yang Wang | Yan Huang | Xijun Wang | Jun Jiang
Source: The Journal of Physical Chemistry Letters (article, 2024-06-18)
Field: Chemistry — Inorganic Chemistry

Exploring Enzyme-Mimicking Metal–Organic Frameworks for CO<sub>2</sub> Conversion through Vibrational Spectra-Based Machine Learning In pursuing the benefits of natural enzyme catalysts while overcoming their limitations, we find metal-organic frameworks (MOFs), renowned for their highly tunable functionalities, stand out in biomimetic applications. We used unsupervised machine learning on density functional theory-computed vibrational infrared and Raman spectral features to screen 300 Zn-MOFs for CO<sub>2</sub> conversion, similar to carbonic anhydrase (CA). Our findings confirmed that MOFs with spectroscopic attributes closely resembling those of CA hold the potential for replicating CA's electronic and catalytic properties. Unlike previous studies that relied on heuristic or trial-and-error methods and focused on geometric configurations, our research uses vibrational spectral features to explore structure-property relationships, making them more accessible through spectroscopy. Moreover, we highlight vibrational spectral features as efficient carriers for highly dimensional chemical information, enabling the simultaneous optimization of multiple performance parameters. These findings pave the way for pioneering designs of enzyme-mimetic MOFs and concurrently expand the application scope of spectroscopic tools in biomimetic catalysis. Spectral line ; Metal-organic framework ; Materials science ; Metal ; Chemistry ; Physical chemistry ; Physics ; Metallurgy ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Machine Learning in Materials Science
Authors: Qianjun Yao | Qun Ji | Xiaopeng Li | Yehui Zhang | Xinyu Chen | Ming‐Gang Ju | Jie Liu | Jinlan Wang
Source: The Journal of Physical Chemistry Letters (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerates Precise Excited-State Potential Energy Surface Calculations on a Quantum Computer Electronically excited-state problems represent a crucial research field in quantum chemistry, closely related to numerous practical applications in photophysics and photochemistry. The emerging of quantum computing provides a promising computational paradigm to solve the Schrödinger equation for predicting potential energy surfaces (PESs). Here, we present a deep neural network model to predict parameters of the quantum circuits within the framework of variational quantum deflation and subspace search variational quantum eigensolver, which are two popular excited-state algorithms to implement on a quantum computer. The new machine learning-assisted algorithm is employed to study the excited-state PESs of small molecules, achieving highly accurate predictions. We then apply this algorithm to study the excited-state properties of the ArF system, which is essential to a gas laser. Through this study, we believe that with future advancements in hardware capabilities, quantum computing could be harnessed to solve excited-state problems for a broad range of systems. Excited state ; Quantum computer ; Quantum ; Potential energy surface ; Computer science ; Quantum machine learning ; Quantum chemistry ; Field (mathematics) ; Subspace topology ; Statistical physics ; Quantum mechanics ; Physics ; Artificial intelligence ; Molecule ; Mathematics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum and electron transport phenomena
Authors: Debarshi Banerjee | Khatereh Azizi | Colin K. Egan | Edward Danquah Donkor | Cesare Malosso | Solana Di Pino | Gonzalo Díaz Mirón | Martina Stella | Giulia Sormani | Germaine Neza Hozana | Marta Monti | Uriel N. Morzan | Álex Rodríguez | Giuseppe Cassone | Asja Jelić | Damián A. Scherlis | Ali Hassanali
Source: Chemical Physics Reviews (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

Aqueous solution chemistry<i>in silico</i>and the role of data-driven approaches The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has been a tremendous growth in the use of data-driven approaches to develop more accurate potentials for water as well as to characterize its complexity in chemical and biological contexts. We highlight the progress, giving a historical context, on the path to the development of many-body and reactive potentials to model aqueous chemistry, including the role of machine learning strategies. We focus specifically on conceptual and methodological challenges along the way in performing simulations that seek to tackle problems in modeling the chemistry of aqueous solutions. In conclusion, we summarize our perspectives on the use and integration of advanced data-science techniques to provide chemical insights into physical chemistry and how this will influence computer simulations of aqueous systems in the future. Context (archaeology) ; Aqueous solution ; Computer science ; Chemistry ; Biochemical engineering ; Data science ; Focus (optics) ; Management science ; Nanotechnology ; Materials science ; Engineering ; Physics ; Physical chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Jian Wang | Haitao Hei | Yonggang Zheng | Hongwu Zhang | Hongfei Ye
Source: Journal of Chemical Theory and Computation (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

Five-Site Water Models for Ice and Liquid Water Generated by a Series–Parallel Machine Learning Strategy Icing, a common natural phenomenon, always originates from a molecule. Molecular simulation is crucial for understanding the relevant process but still faces a great challenge in obtaining a uniform and accurate description of ice and liquid water with limited model parameters. Here, we propose a series-parallel machine learning (ML) approach consisting of a classification back-propagation neural network (BPNN), parallel regression BPNNs, and a genetic algorithm to establish conventional TIP5P-BG and temperature-dependent TIP5P-BGT models. The established water models exhibit a comprehensive balance among the crucial physical properties (melting point, density, vaporization enthalpy, self-diffusion coefficient, and viscosity) with mean absolute percentage errors of 2.65 and 2.40%, respectively, and excellent predictive performance on the related properties of liquid water. For ice, the simulation results on the critical nucleus size and growth rate are in good accordance with experiments. This work offers a powerful molecular model for phase transition and icing in nanoconfinement and a construction strategy for a complex molecular model in the extreme case. Series (stratigraphy) ; Liquid water ; Computer science ; Water model ; Ice water ; Data science ; Chemistry ; Molecular dynamics ; Earth science ; Geology ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Phase Equilibria and Thermodynamics
Authors: Lei Zhang | Mu He | Endai Huang | Xiaokang Ma | Jiaxue You | Alex K.‐Y. Jen | Shengzhong Liu
Source: Solar RRL (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Overcoming Language Barrier for Scientific Studies via Unsupervised Literature Learning: Case Study on Solar Cell Materials Prediction Data‐driven materials and chemical studies have been predominantly confined to English‐language databases, posing challenges for researchers in non‐English‐speaking regions to access and comprehend literature and derive scientific insights. Herein, a machine learning approach designed for information extraction and knowledge acquisition in the materials and chemical science realm from non‐English literature databases, requiring minimal human intervention, is presented. The efficacy of language model through a case study centered on the prediction of solar cell materials using Chinese‐language sources is studied. The unsupervised learning model effectively extracts crucial latent chemical and materials data from non‐English literature resources. Subsequently, the language model successfully identifies existing solar cell materials and forecasts potential candidates from this non‐English corpus. To further validate the suitability of the proposed solar cell material candidates, we conduct ab initio density functional theory calculations to evaluate their structural and optoelectronic properties. The results validate both the efficacy of our language model and the predictability of our approach. This study represents a stride toward comprehensive data‐driven machine learning for materials and chemical predictions, transcending the limitations of English literature. Furthermore, it offers a solution to aid researchers in non‐English‐speaking regions in overcoming language barriers and accessing scientific discoveries. Computer science ; Artificial intelligence ; Natural language processing ; Realm ; Predictability ; Data science Machine Learning in Materials Science ; Topic Modeling
Authors: Eleonora Spurio | Giovanni Bertoni | Sergio D’Addato | Francesca Alimonti | P. Luches
Source: Nanoscale (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Mapping the local stoichiometry in Cu nanoparticles during controlled oxidation by STEM-EELS spectral imaging Copper nanoparticles (NPs) can be coupled with cuprous oxide, combining photoelectrocatalytic properties with a broad-range optical absorption. In the present study, we aimed to correlate changes in morphology, electronic structure and plasmonic properties of Cu NPs at different stages of oxidation. We demonstrated the ability to monitor the oxidation of NPs at the nanometric level using STEM-EELS spectral maps, which were analyzed with machine learning algorithms. The oxidation process was explored by exposing Cu NPs to air plasma, revealing systematic changes in their morphology and composition. Initial plasma exposure created a Cu<sub>2</sub>O shell, while prolonged exposure resulted in hollow structures with a CuO shell. This study identified procedures to obtain a material with Cu<sub>2</sub>O surface stoichiometry and absorption extended into the near-infrared range. Moreover, this study introduced a novel application of machine learning clustering techniques to analyze the morphological and chemical evolution of a nanostructured sample. Stoichiometry ; Nanoparticle ; Materials science ; Analytical Chemistry (journal) ; Nanotechnology ; Chemistry ; Physical chemistry ; Environmental chemistry Copper-based nanomaterials and applications ; ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors