jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manajit Das | Pooja Sharma | Raghavan B. Sunoj
Source: The Journal of Chemical Physics (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

Machine learning studies on asymmetric relay Heck reaction—Potential avenues for reaction development The integration of machine learning (ML) methods into chemical catalysis is evolving as a new paradigm for cost and time economic reaction development in recent times. Although there have been several successful applications of ML in catalysis, the prediction of enantioselectivity (ee) remains challenging. Herein, we describe a ML workflow to predict ee of an important class of catalytic asymmetric transformation, namely, the relay Heck (RH) reaction. A random forest ML model, built using quantum chemically derived mechanistically relevant physical organic descriptors as features, is found to predict the ee remarkably well with a low root mean square error of 8.0 ± 1.3. Importantly, the model is effective in predicting the unseen variants of an asymmetric RH reaction. Furthermore, we predicted the ee for thousands of unexplored complementary reactions, including those leading to a good number of bioactive frameworks, by engaging different combinations of catalysts and substrates drawn from the original dataset. Our ML model developed on the available examples would be able to assist in exploiting the fuller potential of asymmetric RH reactions through a priori predictions before the actual experimentation, which would thus help surpass the trial and error loop to a larger degree. Catalysis ; Workflow ; Computer science ; A priori and a posteriori ; Enantioselective synthesis ; Transformation (genetics) ; Chemistry ; Artificial intelligence ; Class (philosophy) ; Biochemical engineering ; Machine learning ; Combinatorial chemistry ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Min Lin | Jing-Fang Xiong | Mintao Su | Feng Wang | Xiangsi Liu | Yi-Fan Hou | Riqiang Fu | Yong Yang | Jun Cheng
Source: Chemical Science (article, 2022-01-01)
Field: Chemistry — Spectroscopy

A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials Solid-state nuclear magnetic resonance (ssNMR) provides local environments and dynamic fingerprints of alkali ions in paramagnetic battery materials. Linking the local ionic environments and NMR signals requires expensive first-principles computational tools that have been developed for over a decade. Nevertheless, the assignment of the dynamic NMR spectra of high-rate battery materials is still challenging because the local structures and dynamic information of alkali ions are highly correlated and difficult to acquire. Herein, we develop a novel machine learning (ML) protocol that could not only quickly sample atomic configurations but also predict chemical shifts efficiently, which enables us to calculate dynamic NMR shifts with the accuracy of density functional theory (DFT). Using structurally well-defined P2-type Na<sub>2/3</sub>(Mg<sub>1/3</sub>Mn<sub>2/3</sub>)O<sub>2</sub> as an example, we validate the ML protocol and show the significance of dynamic effects on chemical shifts. Moreover, with the protocol, it is demonstrated that the two experimental <sup>23</sup>Na shifts (1406 and 1493 ppm) of P2-type Na<sub>2/3</sub>(Ni<sub>1/3</sub>Mn<sub>2/3</sub>)O<sub>2</sub> originate from two stacking sequences of transition metal (TM) layers for the first time, which correspond to space groups <i>P</i>6<sub>3</sub>/<i>mcm</i> and <i>P</i>6<sub>3</sub>22, respectively. This ML protocol could help to correlate dynamic ssNMR spectra with the local structures and fast transport of alkali ions and is expected to be applicable to a wide range of fast dynamic systems. Chemical shift ; Ion ; Alkali metal ; Paramagnetism ; Chemistry ; Molecular dynamics ; Solid-state nuclear magnetic resonance ; Battery (electricity) ; Ionic bonding ; NMR spectra database ; Chemical physics ; Density functional theory ; Spectral line ; Materials science ; Nuclear magnetic resonance ; Computational chemistry ; Physical chemistry ; Physics ; Condensed matter physics Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Atomic and Subatomic Physics Research
Authors: Chan Gao | Xiaoyong Yang | Ming Jiang | Lixin Chen | Zhiwen Chen | Chandra Veer Singh
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-enabled band gap prediction of monolayer transition metal chalcogenide alloys Monolayer transition metal dichalcogenide (TMD) alloys with tunable direct band gaps have promising applications in nanoelectronics and optoelectronics. The composition-dependent band gaps of ternary, quaternary and quinary monolayer TMD alloys have been systematically studied combining density functional theory and machine learning models in the present study. The excellent agreement between the DFT-calculated band gaps and the ML-predicted values for the training, validation and test datasets demonstrates the accuracy of our machine learning based on a neural network model. It is found that the band gap bowing parameter is closely related to the difference between the band gaps of the endpoint material compositions of the monolayer TMD alloy and increases with increasing band gap difference. The band gap bowing effects of monolayer TMD alloys obtained by mixing different transition metals are attributed to the conduction band minimum positions, while those of monolayer TMD alloys obtained by mixing different chalcogen atoms are dominated by the valence band maximum positions. This study shows that monolayer TMD alloys with tunable direct band gaps can provide new opportunities for band gap engineering, as well as electronic and optoelectronic applications. Monolayer ; Band gap ; Materials science ; Chalcogenide ; Direct and indirect band gaps ; Ternary operation ; Semimetal ; Chalcogen ; Condensed matter physics ; Electronic band structure ; Alloy ; Bowing ; Lattice constant ; Quinary ; Optoelectronics ; Nanotechnology ; Crystallography ; Chemistry ; Optics ; Computer science ; Metallurgy ; Physics ; Diffraction 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Changsu Kim | Wangyun Won | Jiyong Kim
Source: ACS Sustainable Chemistry & Engineering (article, 2022-10-25)
Field: Materials Science — Materials Chemistry

Early-Stage Evaluation of Catalyst Using Machine Learning Based Modeling and Simulation of Catalytic Systems: Hydrogen Production via Water–Gas Shift over Pt Catalysts The goal of this research is to create a new machine learning (ML)-centric methodology for process modeling, designing, and evaluating hydrogen production based on the water–gas shift reaction (WGSR). The approach evaluates the one-pass conversion of catalyst and process overall conversion, as well as the economics of the catalytic conversion process, without the use of kinetics and a process model that requires much trial and error. To accomplish this, an ML model was developed to predict the catalyst performance based on critical catalysis descriptors like catalyst composition, operating conditions, and feed composition. We developed a surrogate model for the hydrogen production process based on the predicted results to determine the mass and energy information on the process, which includes multiple unit operations and recycling. Finally, we assessed the hydrogen production process using different technical and economic metrics such as hydrogen amount, energy consumption, and unit energy cost. The approach can perform a kinetics-free simulation of hydrogen production processes using predicted catalyst performances and evaluate early-state catalysts from an industrial perspective by identifying the optimal operating conditions and the catalyst structure for economic and energy-efficient hydrogen production. As a result, the processes over Pt/Co(10 wt %)/Al2O3, Pt/Co(20 wt %)/Al2O3, and Pt/Ce(5 wt %)/TiO2 show the best performance to produce high-purity hydrogen. Hydrogen production ; Catalysis ; Process engineering ; Hydrogen ; Process simulation ; Process (computing) ; Production (economics) ; Unit operation ; Water-gas shift reaction ; Chemistry ; Computer science ; Chemical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: George S. Fanourgakis | Konstantinos Gkagkas | George E. Froudakis
Source: The Journal of Chemical Physics (article, 2022-01-13)
Field: Chemistry — Inorganic Chemistry

Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage In the field of materials science, the main objective of predictive models is to provide scientists with reliable tools for fast and accurate identification of new materials with exceptional properties. Over the last few years, machine learning methods have been extensively used for the study of the gas-adsorption in nanoporous materials as an efficient alternative of molecular simulations and experiments. In several cases, the accuracy of the constructed predictive models for unknown materials is extremely high. In this study, we explored the adsorption of methane by metal organic frameworks (MOFs) and concluded that many top-performing materials often deviate significantly from the known materials used for the training of the machine learning algorithms. In such cases, the predictions of the machine learning algorithms may not be adequately accurate. For lack of the required appropriate data, we put forth a simple approach for the construction of artificial MOFs with the desired superior properties. Incorporation of such data during the training phase of the machine learning algorithms improves the predictions outstandingly. In some cases, over 96% of the unknown top-performing materials are successfully identified. Machine learning ; Computer science ; Nanoporous ; Artificial intelligence ; Identification (biology) ; Field (mathematics) ; Adsorption ; Methane ; Process engineering ; Nanotechnology ; Materials science ; Engineering ; Chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Jinhui Yu | Dezun Shan | Hongwei Song | Minghui Yang
Source: Fuel (article, 2022-04-11)
Field: Materials Science — Materials Chemistry

A novel hybrid machine learning model for predicting rate constants of the reactions between alkane and CH3 radical Reaction rate constant ; Arrhenius equation ; Combustion ; Hydrogen atom abstraction ; Alkane ; Thermodynamics ; Artificial neural network ; Chemistry ; Range (aeronautics) ; Radical ; Computer science ; Materials science ; Artificial intelligence ; Physical chemistry ; Organic chemistry ; Physics ; Kinetics ; Activation energy ; Hydrocarbon Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Combustion Engine Technologies
Authors: Eduardo A. Barros de Moraes | Marta D’Elia | Mohsen Zayernouri
Source: Computer Methods in Applied Mechanics and Engineering (article, 2022-11-18)
Field: Materials Science — Materials Chemistry

Machine learning of nonlocal micro-structural defect evolutions in crystalline materials Statistical physics ; Dislocation ; Probabilistic logic ; Mesoscale meteorology ; Nucleation ; Mechanics ; Plasticity ; Classical mechanics ; Physics ; Computer science ; Artificial intelligence ; Condensed matter physics Microstructure and mechanical properties ; Model Reduction and Neural Networks ; High Temperature Alloys and Creep
Authors: Magnus Röding | Piotr Tomaszewski | Shun Yu | Markus Borg | Jerk Rönnols
Source: Frontiers in Materials (article, 2022-09-27)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated small-angle X-ray scattering analysis of disordered two- and three-phase materials Small-angle X-ray scattering (SAXS) is a useful technique for nanoscale structural characterization of materials. In SAXS, structural and spatial information is indirectly obtained from the scattering intensity in the spectral domain, known as the reciprocal space. Therefore, characterizing the structure requires solving the inverse problem of finding a plausible structure model that corresponds to the measured scattering intensity. Both the choice of structure model and the computational workload of parameter estimation are bottlenecks in this process. In this work, we develop a framework for analysis of SAXS data from disordered materials. The materials are modeled using Gaussian Random Fields (GRFs). We study the case of two phases, pore and solid, and three phases, where a third phase is added at the interface between the two other phases. Further, we develop very fast GPU-accelerated, Fourier transform-based numerical methods for both structure generation and SAXS simulation. We demonstrate that length scales and volume fractions can be predicted with good accuracy using our machine learning-based framework. The parameter prediction executes virtually instantaneously and hence the computational burden of conventional model fitting can be avoided. Small-angle X-ray scattering ; Scattering ; Materials science ; Reciprocal lattice ; Phase (matter) ; Statistical physics ; Fourier transform ; Small-angle scattering ; Biological system ; Optics ; Physics ; Mathematics ; Mathematical analysis ; Diffraction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Soil and Unsaturated Flow
Authors: K.R. Balasubramanian | Ravi Kumar Kottala | Sathiya Prabhakaran S.P. | B S Jinshah | Nalluri Abhishek
Source: International Journal of Energy Research (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Thermal degradation studies and hybrid neural network modelling of eutectic phase change material composites Using a thermogravimetric analyzer, the thermal stability of pure eutectic phase change material (PEPCM) (LiNO3 + NaCl) and composite eutectic PCM (CEPCM) mixture (ie, PCM containing 9% expanded graphite [EG]) was examined. PEPCM and CEPCM degradation kinetics were studied using model free kinetics methods. The activation energy of both PCM samples was evaluated using the Kissinger-Akahira-Sunose (KAS), Flynn-Wall-Ozawa (FWO), Starink, Friedman and Vyazovkin kinetic models. The calculated activation energies for Vyazovkin, Frideman, Ozawa, KAS and Starink techniques for PEPCM were 80.62-149.2, 108.1-180.18, 83.74-136.17, 73.55-127.02 and 74.64-149.9 kJ/mol, respectively. Likewise, the activation energy of CEPCM vary between 59.4-161.41, 83.97-188.69, 57.1-147.32, 54.19-137.43 and 55-160.65 kJ/mol. Hybrid neural networks such as ANN-PSO and ANFIS were used to model the degradation of PCM samples. The type of PCM, the heating rate, and the temperature were applied as input parameters, while the sample's mass loss was utilized as an output parameter. The created hybrid models are capable of effectively predicting experimental TGA data. Thermogravimetric analysis ; Eutectic system ; Activation energy ; Composite number ; Materials science ; Degradation (telecommunications) ; Kinetics ; Thermal stability ; Composite material ; Phase (matter) ; Thermal ; Chemical engineering ; Thermodynamics ; Chemistry ; Computer science ; Microstructure ; Physical chemistry ; Physics Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Phase Change Materials Research
Authors: Marko Vasić | Cameron Chalk | Austin Luchsinger | Sarfraz Khurshid | David Soloveichik
Source: Proceedings of the National Academy of Sciences (article, 2022-06-09)
Field: Materials Science — Materials Chemistry

Programming and training rate-independent chemical reaction networks Embedding computation in biochemical environments incompatible with traditional electronics is expected to have a wide-ranging impact in synthetic biology, medicine, nanofabrication, and other fields. Natural biochemical systems are typically modeled by chemical reaction networks (CRNs) which can also be used as a specification language for synthetic chemical computation. In this paper, we identify a syntactically checkable class of CRNs called noncompetitive (NC) whose equilibria are absolutely robust to reaction rates and kinetic rate law, because their behavior is captured solely by their stoichiometric structure. In spite of the inherently parallel nature of chemistry, the robustness property allows for programming as if each reaction applies sequentially. We also present a technique to program NC-CRNs using well-founded deep learning methods, showing a translation procedure from rectified linear unit (ReLU) neural networks to NC-CRNs. In the case of binary weight ReLU networks, our translation procedure is surprisingly tight in the sense that a single bimolecular reaction corresponds to a single ReLU node and vice versa. This compactness argues that neural networks may be a fitting paradigm for programming rate-independent chemical computation. As proof of principle, we demonstrate our scheme with numerical simulations of CRNs translated from neural networks trained on traditional machine learning datasets, as well as tasks better aligned with potential biological applications including virus detection and spatial pattern formation. Computer science ; Artificial neural network ; Robustness (evolution) ; Artificial intelligence ; Embedding ; Computation ; Theoretical computer science ; Algorithm ; Chemistry Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; Molecular Junctions and Nanostructures
Authors: Shinyoung Bae | Hwangho Lee | Jun‐Seop Shin | Hyun Sub Kim | Yeonsoo Kim | Do Heui Kim | Jong Min Lee
Source: Chemistry of Materials (article, 2022-08-29)
Field: Materials Science — Materials Chemistry

Data-Driven Inference of Synthesis Guidelines for High-Performance Zeolite-Based Selective Catalytic Reduction Catalysts at Low Temperatures Numerous zeolite-based selective catalytic reduction (SCR) catalysts have been investigated to improve nitrogen oxide (NOx) removal efficiency at low temperatures of 25–200 °C in diesel vehicles. However, the majority of these studies examined only one of each feature’s effects. The catalysis mechanism consists of complex reactions, and the various features interact, making it difficult to predict their combinatorial effects on the catalytic activity. Recently, machine learning-based models have been widely employed in catalysis science to infer hidden information about catalysts without knowledge of the underlying physical principles. Interpretable machine learning models are particularly useful for catalyst research because they can explain the causal relationship between characteristics and catalytic performance. In this study, we construct a machine learning model utilizing a decision tree, one of the representative interpretable machine learning models. Using this model, we evaluate the causal relationship between features and the NOx removal efficiency of zeolite-based SCR catalysts at low temperatures, which is difficult to deduce due to the high number of features. Additionally, we extract several synthesis guidelines for catalysts that show superior NOx removal performance at low temperatures. New catalysts were synthesized using the proposed rules, and their performance was validated experimentally. Catalysis ; Zeolite ; Reduction (mathematics) ; Inference ; Selective catalytic reduction ; Materials science ; Chemical engineering ; Chemistry ; Computer science ; Organic chemistry ; Engineering ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Machine Learning and Data Classification ; Machine Learning and Algorithms
Authors: Xinming Wang | Yinchang Zhao | Shuming Zeng | Zhuchi Wang | Ying Chen | Jun Ni
Source: Physical review. B./Physical review. B (article, 2022-01-27)
Field: Materials Science — Materials Chemistry

Cubic halide perovskites as potential low thermal conductivity materials: A combined approach of machine learning and first-principles calculations Thermal conductivity is the key factor affecting thermoelectric properties of materials. Here, machine-learning techniques combined with first-principles calculations are used to identify the cubic halide perovskites ${\text{Cs}B\text{Br}}_{3}$ (B = Ca, Cd, and Sn) with ultralow thermal conductivity. Based on the Boltzmann transport equation within the relaxation time approximation, we demonstrate this type of perovskites have remarkably low lattice thermal conductivities ${\ensuremath{\kappa}}_{L}\ensuremath{\sim}\phantom{\rule{4pt}{0ex}}0.4\ensuremath{-}1$ W/mK at 300 K. We employ the self-consistent phonon theory incorporating both cubic and quartic anharmonicity, which is considered from the bubble and loop self-energy diagrams rather than many-body perturbation theory. We show that the approach yields a cubic-tetragonal phase transition of ${\mathrm{CsCaBr}}_{3}$ at temperature ${T}_{c}=226\ensuremath{-}265\phantom{\rule{0.16em}{0ex}}\mathrm{K}$, in good agreement with the experimental value of 239 K. An anomalously temperature dependence of ${\ensuremath{\kappa}}_{L}$ is observed in ${\mathrm{CsCdBr}}_{3}$, where the coherent term account for 26% of the total lattice thermal conductivity. We also demonstrate that the hardening of vibrations in low-lying phonon modes offset the phonon population effect as temperature increases by reducing the available phase space. Phonon ; Thermal conductivity ; Anharmonicity ; Condensed matter physics ; Tetragonal crystal system ; Thermoelectric materials ; Physics ; Boltzmann equation ; Lattice (music) ; Materials science ; Thermodynamics ; Phase (matter) ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Expansion and Ionic Conductivity
Authors: Chi Chen | Shyue Ping Ong
Source: Research Square (preprint, 2022-04-04)
Field: Materials Science — Materials Chemistry

A Universal Graph Deep Learning Interatomic Potential for the Periodic Table Graph ; Table (database) ; Periodic table ; Computer science ; Interatomic potential ; Theoretical computer science ; Statistical physics ; Chemistry ; Physics ; Computational chemistry ; Data mining ; Quantum mechanics ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry