jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sukriti Singh | José Miguel Hernández-Lobato
Source: The Journal of Organic Chemistry (article, 2024-08-16)
Field: Chemistry — Inorganic Chemistry

Data-Driven Insights into the Transition-Metal-Catalyzed Asymmetric Hydrogenation of Olefins The transition-metal-catalyzed asymmetric hydrogenation of olefins is one of the key transformations with great utility in various industrial applications. The field has been dominated by the use of noble metal catalysts, such as iridium and rhodium. The reactions with the earth-abundant cobalt metal have increased only in recent years. In this work, we analyze the large amount of literature data available on iridium- and rhodium-catalyzed asymmetric hydrogenation. The limited data on reactions using Co catalysts are then examined in the context of Ir and Rh to obtain a better understanding of the reactivity pattern. A detailed data-driven study of the types of olefins, ligands, and reaction conditions such as solvent, temperature, and pressure is carried out. Our analysis provides an understanding of the literature trends and demonstrates that only a few olefin-ligand combinations or reaction conditions are frequently used. The knowledge of this bias in the literature data toward a certain group of substrates or reaction conditions can be useful for practitioners to design new reaction data sets that are suitable to obtain meaningful predictions from machine-learning models. Rhodium ; Iridium ; Catalysis ; Olefin fiber ; Context (archaeology) ; Transition metal ; Reactivity (psychology) ; Chemistry ; Ligand (biochemistry) ; Cobalt ; Alkene ; Noble metal ; Combinatorial chemistry ; Materials science ; Organic chemistry ; Geology Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Gabriele Lo Monaco | Marco Bertini | Salvatore Lorenzo | G. Massimo Palma
Source: Machine Learning Science and Technology (preprint, 2024-07-09)
Field: Materials Science — Materials Chemistry

Quantum extreme learning of molecular potential energy surfaces and force fields Abstract Quantum machine learning algorithms are expected to play a pivotal role in quantum chemistry simulations in the immediate future. One such key application is the training of a quantum neural network to learn the potential energy surface and force field of molecular systems. We address this task by using the quantum extreme learning machine paradigm. This particular supervised learning routine allows for resource-efficient training, consisting of a simple linear regression performed on a classical computer. We have tested a setup that can be used to study molecules of any dimension and is optimized for immediate use on NISQ devices with a limited number of native gates. We have applied this setup to three case studies: lithium hydride, water, and formamide, carrying out both noiseless simulations and actual implementation on IBM quantum hardware. Compared to other supervised learning routines, the proposed setup requires minimal quantum resources, making it feasible for direct implementation on quantum platforms, while still achieving a high level of predictive accuracy compared to simulations. Our encouraging results pave the way towards the future application to more complex molecules, being the proposed setup scalable. Force field (fiction) ; Quantum ; Potential energy ; Energy (signal processing) ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Konstantin Stracke | Jack D. Evans
Source: Nanoscale (review, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

The use of collective variables and enhanced sampling in the simulations of existing and emerging microporous materials Microporous materials, including zeolites, metal-organic frameworks, and cage compounds, offer diverse functionalities due to their unique dynamics and guest confinement properties. These materials play a significant role in separation, catalysis, and sensing, but their complexity hinders exploration using traditional atomistic simulations. This review explores collective variables (CVs) paired with enhanced sampling as a powerful approach to enable efficient investigation of key features in microporous materials. We highlight successful applications of CVs in studying adsorption, diffusion, phase transitions, and mechanical properties, demonstrating their crucial role in guiding material design and optimisation. The future of CVs lies in integration with techniques like machine learning, allowing for enhanced efficiency and accuracy. By tailoring CVs to specific materials and developing multi-scale approaches we can further unlock the intricacies of these fascinating materials. Simulations are a cornerstone in unravelling the complexities of microporous materials and are crucial for our future understanding. Microporous material ; Sampling (signal processing) ; Materials science ; Statistical physics ; Computer science ; Composite material ; Physics ; Telecommunications Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: L. F. Zhang | Lili Ye | Fan Wang | Wei Gao | Jinhui Yu | Lidong Zhang
Source: The Journal of Physical Chemistry A (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

Prediction of Hydrogen Abstraction Rate Constants at the Allylic Site between Alkenes and OH with Multiple Machine Learning Models Hydrogen abstraction reactions between hydrocarbons and hydroxyl radicals are important propagation steps in radical chain reactions, playing a crucial role in atmospheric and combustion chemistry. This study focuses on predicting the rate constants of the prototype of the reaction class of hydrogen abstractions, i.e., the primary allylic hydrogen abstraction from alkenes by the OH radical, via utilizing machine learning (ML) methods. Specifically, three distinct models, namely, feedforward neural network (FNN), support vector regression (SVR), and Gaussian process regression (GPR), have been employed to construct robust ML models for prediction. We proposed a novel strategy that seamlessly integrates descriptor preprocessing, a pairwise linear correlation analysis, and a model-specific Wrapper method to enhance the effectiveness of the feature selection procedure. The selected feature subset was then evaluated using two cross-validation techniques, i.e., leave-one-group-out (LOGO) and K-fold cross-validations, for each of the three ML models (FNN, SVR, and GPR) to assess their predictive and stability performance. The results demonstrate that the FNN model, trained with seven representative descriptors, achieves superior performance compared to the other two methods. For the FNN model, the average percentage deviation is 39.06% on the test set by performing LOGO cross-validation, while the repeated 10-fold cross-validation achieves a percentage prediction deviation of 19.1%. Two larger alkenes with 10 carbons were selected to test the prediction performance of the trained FNN model on primary allylic hydrogen abstraction. Results show that the kinetic predictions follow well the modified three-parameter Arrhenius equation, indicating the reliable performance of FNN in predicting hydrogen abstraction rate constants, especially for the primary allylic site. Hopefully, this work can shed useful light on the application of ML in generating chemical kinetic parameters of hydrocarbon combustion chemistry. Hydrogen atom abstraction ; Support vector machine ; Computer science ; Feature selection ; Artificial intelligence ; Abstraction ; Cross-validation ; Machine learning ; Chemistry ; Radical ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Hongbin Pei | Taile Chen | A Chen | Huiqi Deng | Jing Tao | Pinghui Wang | Xiaohong Guan
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2024-03-24)
Field: Chemistry — Physical and Theoretical Chemistry

HAGO-Net: Hierarchical Geometric Message Passing for Molecular Representation Learning Molecular representation learning has emerged as a game-changer at the intersection of AI and chemistry, with great potential in applications such as drug design and materials discovery. A substantial obstacle in successfully applying molecular representation learning is the difficulty of effectively and completely characterizing and learning molecular geometry, which has not been well addressed to date. To overcome this challenge, we propose a novel framework that features a novel geometric graph, termed HAGO-Graph, and a specifically designed geometric graph learning model, HAGO-Net. In the framework, the foundation is HAGO-Graph, which enables a complete characterization of molecular geometry in a hierarchical manner. Specifically, we leverage the concept of n-body in physics to characterize geometric patterns at multiple spatial scales. We then specifically design a message passing scheme, HAGO-MPS, and implement the scheme as a geometric graph neural network, HAGO-Net, to effectively learn the representation of HAGO-Graph by horizontal and vertical aggregation. We further prove DHAGO-Net, the derivative function of HAGO-Net, is an equivariant model. The proposed models are validated by extensive comparisons on four challenging benchmarks. Notably, the models exhibited state-of-the-art performance in molecular chirality identification and property prediction, achieving state-of-the-art performance on five properties of QM9 dataset. The models also achieved competitive results on molecular dynamics prediction task. Net (polyhedron) ; Representation (politics) ; Massage ; Computer science ; Artificial intelligence ; Mathematics ; Medicine ; Geometry ; Alternative medicine ; Pathology Various Chemistry Research Topics
Authors: Thanh Ngoc Pham | Beatriz Andrea Choi Tan | Yuji Hamamoto | Kouji Inagaki | Ikutaro Hamada | Yoshitada Morikawa
Source: ACS Catalysis (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

Stability of Pd<sub><i>x</i></sub>O<sub><i>y</i></sub> Particles Supported on Strontium Titanate Perovskite under Three-Way Catalyst Operating Conditions: Implications for Sintering Resistance Self-regenerative materials are keys to the development of stable catalysts used under high temperature condition, e.g., three-way catalyst converters in automobiles. Among others, metal nanoparticles supported on perovskite oxides are promising ones. However, little is known about their atomistic details, which are crucial for understanding and development of thermally stable catalysts. Herein, we present a machine-learning-enhanced density functional theory study of PdxOy nanoparticles supported on a Sr3Ti2O7(001) surface and demonstrate that supported oxidized Pd particles fulfill the conditions for the self-regenerative catalysts. Under the oxidative condition, the solid-solution reaction of Pd with the support is found to be preferable but is limited to the vicinity of the surface. Furthermore, formation of PdO-like clusters enhances their binding strength to the support, inhibiting the agglomeration (sintering) of the PdxOy nanoparticles. We present detailed thermodynamic and electronic structure analyses and clarify the roles of the oxide support, cluster size of the oxidized metal nanoparticle, and the metal–support interaction. This work may provide a guideline for the rational design of the thermally stable catalyst against sintering Sintering ; Catalysis ; Materials science ; Perovskite (structure) ; Nanoparticle ; Oxide ; Strontium titanate ; Chemical engineering ; Cluster (spacecraft) ; Economies of agglomeration ; Metal ; Nanotechnology ; Thin film ; Metallurgy ; Chemistry ; Organic chemistry ; Computer science Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Perovskite Materials and Applications
Authors: Lois Smith | Hossein Ali Karimi‐Varzaneh | Sebastian Finger | Giuliana Giunta | Alessandro Troisi | Paola Carbone
Source: Macromolecules (article, 2024-05-14)
Field: Materials Science — Polymers and Plastics

Framework for a High-Throughput Screening Method to Assess Polymer/Plasticizer Miscibility: The Case of Hydrocarbons in Polyolefins Polymer composite materials require softening to reduce their glass transition temperature and improve processability. To this end, plasticizers (PLs), which are small organic molecules, are added to the polymer matrix. The miscibility of these PLs has a large impact on their effectiveness and, therefore, their interactions with the polymer matrix must be carefully considered. Many PL characteristics, including their size, topology, and flexibility, can impact their miscibility and, because of the exponentially large number of PLs, the current trial-and-error approach is very ineffective. In this work, we show that using coarse-grained molecular simulations of a small dataset of 48 PLs, it is possible to identify topological and thermodynamic descriptors that are proxy for their miscibility. Using <i>ad-hoc</i> molecular dynamics simulation setups that are relatively computationally inexpensive, we establish correlations between the PLs' topology, internal flexibility, thermodynamics of aggregation, and degree of miscibility, and use these descriptors to classify the molecules as miscible or immiscible. With all available data, we also construct a decision tree model, which achieves a F1 score of 0.86 ± 0.01 with repeated, stratified 5-fold cross-validation, indicating that this machine learning method can be a promising route to fully automate the screening. By evaluating the individual performance of the descriptors, we show this procedure enables a 10-fold reduction of the test space and provides the basis for the development of workflows that can efficiently screen PLs with a variety of topological features. The approach is used here to screen for apolar PLs in polyisoprene melts, but similar proxies would be valid for other polyolefins, while, in cases where polar interactions drive the miscibility, other descriptors are likely to be needed. Miscibility ; Plasticizer ; Polymer ; Materials science ; Polymer science ; Polymer chemistry ; Chemical engineering ; Organic chemistry ; Chemistry ; Engineering Polymer crystallization and properties ; Polymer Science and PVC ; Polymer Nanocomposites and Properties
Authors: Yong Li | Chenchong Wang | Yu Zhang | Yuqi Zhang | Lingyu Wang | Yizhuang Li | Wei Xu
Source: Calphad (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

Thermodynamically informed graph for interpretable and extensible machine learning: Martensite start temperature prediction Computer science ; Graph ; Extensibility ; Machine learning ; Artificial intelligence ; Data science ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yifei Yue | Saad Aldin Mohamed | Jianwen Jiang
Source: Journal of Chemical Information and Modeling (article, 2024-06-26)
Field: Chemistry — Inorganic Chemistry

Classifying and Predicting the Thermal Expansion Properties of Metal–Organic Frameworks: A Data-Driven Approach Metal-organic frameworks (MOFs) are versatile materials for a wide variety of potential applications. Tunable thermal expansion properties promote the application of MOFs in thermally sensitive composite materials; however, they are currently available only in a handful of structures. Herein, we report the first data set for thermal expansion properties of 33,131 diverse MOFs generated from molecular simulations and subsequently develop machine learning (ML) models to (1) classify different thermal expansion behaviors and (2) predict volumetric thermal expansion coefficients (α<sub>V</sub>). The random forest model trained on hybrid descriptors combining geometric, chemical, and topological features exhibits the best performance among different ML models. Based on feature importance analysis, linker chemistry and topological arrangement are revealed to have a dominant impact on thermal expansion. Furthermore, we identify common building blocks in MOFs with exceptional thermal expansion properties. This data-driven study is the first of its kind, not only constructing a useful data set to facilitate future studies on this important topic but also providing design guidelines for advancing new MOFs with desired thermal expansion properties. Thermal expansion ; Thermal ; Variety (cybernetics) ; Metal-organic framework ; Materials science ; Set (abstract data type) ; Computer science ; Artificial intelligence ; Thermodynamics ; Chemistry ; Composite material ; Physical chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Robert J. Appleton | Daniel Klinger | Brian H. Lee | Michael Alan Taylor | Sohee Kim | Samuel Blankenship | Brian C. Barnes | Steven F. Son | Alejandro Strachan
Source: Propellants Explosives Pyrotechnics (article, 2024-11-23)
Field: Materials Science — Materials Chemistry

Multi‐Task Multi‐Fidelity Learning of Properties for Energetic Materials Abstract Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, in the field of energetic materials data scarcity limits the accuracy and even applicability of ML tools. To address data limitations, we compiled multi‐modal data: both experimental and computational results for several properties. We find that multi‐task neural networks can learn from multi‐modal data and outperform single‐task models trained for specific properties. As expected, the improvement is more significant for data‐scarce properties. These models are trained using descriptors built from simple molecular information and can be readily applied for large‐scale materials screening to explore multiple properties simultaneously. This approach is widely applicable to fields outside energetic materials. Task (project management) ; Fidelity ; Energetic material ; Computer science ; High fidelity ; Materials science ; Nanotechnology ; Chemistry ; Physics ; Engineering ; Systems engineering ; Explosive material ; Acoustics Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; X-ray Diffraction in Crystallography
Authors: Jameel Ahmed Bhutto | Bilal Siddique | Ihab Mohamed Moussa | Mohamed A. El‐Sheikh | Zhihua Hu | Yurong Guan
Source: Heliyon (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Machine learning assisted designing of non-fullerene electron acceptors: A quest for lower exciton binding energy The designing of acceptors materials for the organic solar cells is a hot topic. The normal experimental methods are tedious and expensive for large screening. Machine learning guided exploration is more suitable solution. Bagging regression, random forest regression, gradient boosting regression, and linear regression are trained to predict exciton binding energy. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) methodology has utilized for designing of new non-fullerene acceptors (NFAs). The predicted values were used to select the designed NFAs. On the selected NFAs, clustering and chemical similarity analyses are also performed. Chemical fingerprints are used for this purpose, and the synthetic accessibility score of the new NFAs is also investigated.30 NFAs have selected with low exciton binding energy values. This approach will allow for the rapid screening of NFAs for organic solar cells. Our proposed framework stands out as a valuable tool for strategically selecting the most effective NFAs for organic solar cells and offers a streamlined approach for material discovery. Random forest ; Organic solar cell ; Exciton ; Fullerene ; Cluster analysis ; Gradient boosting ; Regression ; Binding energy ; Computer science ; Linear regression ; Boosting (machine learning) ; Artificial intelligence ; Machine learning ; Chemistry ; Physics ; Mathematics ; Statistics ; Organic chemistry Machine Learning in Materials Science ; Fullerene Chemistry and Applications ; Organic Electronics and Photovoltaics
Authors: Lucien Dupuy | Neepa T. Maitra
Source: The Journal of Chemical Physics (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings We extend the DeePMD neural network architecture to predict electronic structure properties necessary to perform non-adiabatic dynamics simulations. While learning the excited state energies and forces follows a straightforward extension of the DeePMD approach for ground-state energies and forces, how to learn the map between the non-adiabatic coupling vectors (NACV) and the local chemical environment descriptors of DeePMD is less trivial. Most implementations of machine-learning-based non-adiabatic dynamics inherently approximate the NACVs, with an underlying assumption that the energy-difference-scaled NACVs are conservative fields. We overcome this approximation, implementing the method recently introduced by Richardson [J. Chem. Phys. 158, 011102 (2023)], which learns the symmetric dyad of the energy-difference-scaled NACV. The efficiency and accuracy of our neural network architecture are demonstrated through the example of the methaniminium cation CH2NH2+. Adiabatic process ; Excited state ; Artificial neural network ; Coupling (piping) ; Statistical physics ; Physics ; Computer science ; Energy (signal processing) ; Ground state ; Quantum mechanics ; Machine learning ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Xuan Zhang | Liang Zhang | Yuxuan Wan | Yasushi Shibuta | Xiaoxu Huang
Source: Materials Today Communications (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Predicting the grain boundary segregation energy of solute atoms in aluminum by first-principles calculation and machine learning Materials science ; Grain boundary ; Aluminium ; Energy (signal processing) ; Boundary (topology) ; Engineering physics ; Metallurgy ; Chemical physics ; Microstructure Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Ion-surface interactions and analysis
Authors: Lucas Lang | Henrique Musseli Cezar | Ludwik Adamowicz | Thomas Bondo Pedersen
Source: Journal of the American Chemical Society (article, 2024-01-10)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum Definition of Molecular Structure Molecular structure, a key concept of chemistry, has remained elusive from the perspective of all-particle quantum mechanics, despite many efforts. Viewing molecular structure as a manifestation of strong statistical correlation between nuclear positions, we propose a practical method based on Markov chain Monte Carlo sampling and unsupervised machine learning. Application to the D<sub>3</sub><sup>+</sup> molecule unambiguously shows that it possesses an equilateral triangular structure. These results provide a major step forward in our understanding of the molecular structure from fundamental quantum principles. Chemistry ; Equilateral triangle ; Statistical physics ; Quantum chemistry ; Molecule ; Physics History and advancements in chemistry ; Molecular spectroscopy and chirality ; Chemical Thermodynamics and Molecular Structure
Authors: Carles Corbella | Asim Aijaz | Tomáš Kubart | Li Lin | Sabine Portal | Michael Keidar
Source: Carbon (article, 2024-11-02)
Field: Materials Science — Materials Chemistry

Pulsed plasma vapour deposition of carbon materials: Advantages and challenges Materials science ; Plasma ; Deposition (geology) ; Carbon fibers ; Nanotechnology ; Chemical vapor deposition ; Chemical engineering ; Composite material ; Composite number ; Geology ; Engineering ; Physics Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Ion-surface interactions and analysis