jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexandre Boucher | Glenn Jones | Alberto Roldán
Source: Physical Chemistry Chemical Physics (article, 2022-12-12)
Field: Materials Science — Materials Chemistry

Toward a new definition of surface energy for late transition metals Surface energy is a top-importance stability descriptor of transition metal-based catalysts. Here, we combined density functional theory (DFT) calculations and a tiling scheme measuring surface areas of metal structures to develop a simple computational model predicting the average surface energy of metal structures independently of their shape. The metals considered are W, Ru, Co, Ir, Ni, Pd, Pt, Cu, Ag and Au. Lorentzian trends derived from the DFT data proved effective at predicting the surface energy of metallic surfaces but not for metal clusters. We used machine-learning protocols to build an algorithm that improves the Lorentzian trend's accuracy and is able to predict the surface energies of metal surfaces of any crystal structure, <i>i.e.</i>, face-centred cubic, hexagonal close-packed, and body-centred cubic, but also of nanostructures and sub-nanometer clusters. The machine-learning neural network takes easy-to-compute geometric features to predict metallic moieties surface energies with a mean absolute error of 0.091 J m<sup>-2</sup> and an <i>R</i><sup>2</sup> score of 0.97. Transition metal ; Surface (topology) ; Transition (genetics) ; Energy transition ; Energy (signal processing) ; Materials science ; Chemical physics ; Chemistry ; Mathematics ; Statistics ; Geometry ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Lenz Fiedler | Nils Hoffmann | Parvez Mohammed | Gabriel A. Popoola | Tamar Yovell | Vladyslav Oles | John Ellis | Sivasankaran Rajamanickam | Attila Cangi
Source: Machine Learning Science and Technology (article, 2022-10-11)
Field: Materials Science — Materials Chemistry

Training-free hyperparameter optimization of neural networks for electronic structures in matter Abstract A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations—this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of neural network models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn–Sham density functional theory, the most popular computational method in materials science and chemistry. Hyperparameter ; Workflow ; Computer science ; Overhead (engineering) ; Artificial neural network ; Machine learning ; Artificial intelligence ; Electronic structure ; Process (computing) ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electron and X-Ray Spectroscopy Techniques
Authors: Yong Hu | Jennifer L. Gottfried | Rose A. Pesce‐Rodriguez | Chi‐Chin Wu | Scott D. Walck | Zhiyu Liu | Sangeeth Balakrishnan | Scott Broderick | Zipeng Guo | Qiang Zhang | Lu An | Revant Adlakha | Mostafa Nouh | Chi Zhou | Peter W. Chung | Shenqiang Ren
Source: Nature Communications (article, 2022-11-15)
Field: Materials Science — Materials Chemistry

Releasing chemical energy in spatially programmed ferroelectrics Chemical energy ferroelectrics are generally solid macromolecules showing spontaneous polarization and chemical bonding energy. These materials still suffer drawbacks, including the limited control of energy release rate, and thermal decomposition energy well below total chemical energy. To overcome these drawbacks, we report the integrated molecular ferroelectric and energetic material from machine learning-directed additive manufacturing coupled with the ice-templating assembly. The resultant aligned porous architecture shows a low density of 0.35 g cm<sup>-3</sup>, polarization-controlled energy release, and an anisotropic thermal conductivity ratio of 15. Thermal analysis suggests that the chlorine radicals react with macromolecules enabling a large exothermic enthalpy of reaction (6180 kJ kg<sup>-1</sup>). In addition, the estimated detonation velocity of molecular ferroelectrics can be tuned from 6.69 ± 0.21 to 7.79 ± 0.25 km s<sup>-1</sup> by switching the polarization state. These results provide a pathway toward spatially programmed energetic ferroelectrics for controlled energy release rates. Exothermic reaction ; Chemical energy ; Detonation ; Chemical physics ; Materials science ; Polarization (electrochemistry) ; Chemical reaction ; Thermal energy ; Enthalpy ; Ferroelectricity ; Anisotropy ; Nanotechnology ; Explosive material ; Chemical engineering ; Thermodynamics ; Chemistry ; Optoelectronics ; Physical chemistry ; Organic chemistry ; Optics ; Physics Thermal properties of materials ; Perovskite Materials and Applications ; Catalytic Processes in Materials Science
Authors: Daniel Hewitt | Tom Pope | Misbah Sarwar | Alessandro Turrina | Ben Slater
Source: Chemical Science (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

Machine learning accelerated high-throughput screening of zeolites for the selective adsorption of xylene isomers The production of widely used polymers such as polyester currently relies upon the chemical separation of and transformation of xylene isomers. The least valuable but most prevalent isomer is <i>meta</i>-xylene which can be selectively transformed into the more useful and expensive <i>para</i>-xylene isomer using a zeolite catalyst but at a high energy cost. In this work, high-throughput screening of existing and hypothetical zeolite databases containing more than two million structures was performed, using a combination of classical simulation and deep neural network methods to identify promising materials for selective adsorption of <i>meta</i>-xylene. Novel anomaly detection techniques were applied to the heavily biased classification task of identifying structures with a selectivity greater than that of the best performing existing zeolite, ZSM-5 (MFI topology). Eight hypothetical zeolite topologies are found to be several orders of magnitude more selective towards <i>meta</i>-xylene than ZSM-5 which may provide an impetus for synthetic efforts to realise these promising materials. Moreover, the leading hypothetical frameworks identified from the screening procedure require a markedly lower operating temperature to achieve the diffusion seen in existing materials, suggesting significant energetic savings if the frameworks can be realised. Adsorption ; Throughput ; Chemistry ; High-throughput screening ; Xylene ; p-Xylene ; Combinatorial chemistry ; Organic chemistry ; Catalysis ; Computer science ; Toluene ; Biochemistry Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Yaroslav Balytskyi | Justin Bendesky | Tristan C. Paul | Guy M. Hagen | Kelly McNear
Source: Analytical Chemistry (article, 2022-10-24)
Field: Chemistry — Analytical Chemistry

Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach Raman spectroscopy, combined with machine learning techniques, holds great promise for many applications as a rapid, sensitive, and label-free identification method. Such approaches perform well when classifying spectra of chemical species that were encountered during the training phase. That is, species that are known to the neural network. However, in real-world settings, such as in clinical applications, there will always be substances whose spectra have not yet been taken. When the neural network encounters these new species during the testing phase, the number of false positives becomes uncontrollable, limiting the usefulness of these techniques, especially in public safety applications. To overcome these barriers, we implemented the recently introduced Entropic Open Set and Objectosphere loss functions. To demonstrate the efficacy and efficiency of this approach, we compiled a database of hyperspectral Raman images of 40 chemical species separating them into three class categorizations. The known class consisted of 20 biologically relevant species comprising amino acids, the ignored class was 10 "irrelevant" species comprising bio-related chemicals, and the never seen before class was 10 various chemical species that the neural network had not seen before. We show that this approach not only enables the network to effectively separate the unknown species while preserving high accuracy on the known ones and reducing false positives but also performs better than the current gold standards in machine learning techniques. This opens the door to using Raman spectroscopy, combined with our novel machine learning algorithm, in a variety of practical applications. Availability and implementation: freely available on the web at https://github.com/BalytskyiJaroslaw/RamanOpenSet.git. False positive paradox ; Artificial intelligence ; Limiting ; Machine learning ; Artificial neural network ; Raman spectroscopy ; Identification (biology) ; Class (philosophy) ; Chemistry ; Computer science ; Set (abstract data type) ; Chemical species ; Hyperspectral imaging ; Pattern recognition (psychology) ; Ecology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Fancy Qian Wang | Kamal Choudhary | Yu Liu | Jianjun Hu | Ming Hu
Source: Scientific Data (article, 2022-02-21)
Field: Materials Science — Materials Chemistry

Large scale dataset of real space electronic charge density of cubic inorganic materials from density functional theory (DFT) calculations Driven by the big data science, material informatics has attracted enormous research interests recently along with many recognized achievements. To acquire knowledge of materials by previous experience, both feature descriptors and databases are essential for training machine learning (ML) models with high accuracy. In this regard, the electronic charge density ρ(r), which in principle determines the properties of materials at their ground state, can be considered as one of the most appropriate descriptors. However, the systematic electronic charge density ρ(r) database of inorganic materials is still in its infancy due to the difficulties in collecting raw data in experiment and the expensive first-principles based computational cost in theory. Herein, a real space electronic charge density ρ(r) database of 17,418 cubic inorganic materials is constructed by performing high-throughput density functional theory calculations. The displayed ρ(r) patterns show good agreements with those reported in previous studies, which validates our computations. Further statistical analysis reveals that it possesses abundant and diverse data, which could accelerate ρ(r) related machine learning studies. Moreover, the electronic charge density database will also assists chemical bonding identifications and promotes new crystal discovery in experiments. Density functional theory ; Charge (physics) ; Computer science ; Chemical space ; Materials informatics ; Space (punctuation) ; Electronic structure ; Charge density ; Computation ; Electronic density ; Database ; Data mining ; Machine learning ; Physics ; Chemistry ; Computational chemistry ; Algorithm ; Health informatics ; Quantum mechanics ; Drug discovery Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Dan Feng Zhang | Jayendran Srinivasan | Jenifer Locke
Source: CORROSION (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Quantitative Evaluation of the Ability of Accelerated Tests to Simulate On-Road Corrosion Morphology of Automotive Aluminum Alloys Three wrought automotive Al-Mg-Si alloys (6xxx-series) were exposed to an on-road service environment and in six standard laboratory-accelerated exposure tests. Optical micrographs of all these exposures were obtained and the resulting corrosion morphology was quantified using fractal dimension analysis and corrosion boundary length-to-area ratios. Additionally, the images were also used to train a convolutional neural network (CNN)-based pattern recognition algorithm, which was then used to quantitatively identify which accelerated test was the closest match to the field exposures. Overall, no single accelerated test could fully capture the on-road results or ubiquitously be the most appropriate test regardless of alloy and temper. However, results from fractal dimension and length-to-area ratio analyses identified that among the tests studied, those with acidified electrolytes are more appropriate for matching on-road corrosion morphology. The CNN algorithm output also agreed with this finding, indicating that the results from tests with acidified electrolytes correlated to field morphology with a confidence &amp;gt;70% for most of the images tested, thus showing the utility of these methods in providing quantitative bases for morphology comparison. Assessed in the context of literature evidence for localized corrosion mechanisms in 6xxx-series alloys, these results also indicated that pH may play an influential role in how corrosion morphology develops in these alloys upon exposure to a complex on-road environment. Corrosion ; Context (archaeology) ; Materials science ; Fractal dimension ; Alloy ; Morphology (biology) ; Aluminium ; Metallurgy ; Convolutional neural network ; Computer science ; Fractal ; Artificial intelligence ; Mathematics ; Geology Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Maicon Pierre Lourenço | Lizandra Barrios Herrera | Jiří Hostaš | Patrizia Calaminici | Andreas M. Köster | Alain Tchagang | Dennis R. Salahub
Source: Journal of Molecular Modeling (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

A new active learning approach for adsorbate–substrate structural elucidation in silico Artificial neural network ; Computer science ; Energy minimization ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Software Engineering Research ; Computational Drug Discovery Methods
Authors: Haotian Chen | Enno Kätelhön | Richard G. Compton
Source: The Analyst (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

The application of physics-informed neural networks to hydrodynamic voltammetry Electrochemical problems are widely studied in flowing systems since the latter offer improved sensitivity notably for electro-analysis and the possibility of steady-state measurements for fundamental studies even with macro-electrodes. We report the exploratory use of Physics-Informed Neural Networks (PINNs) as potentially simpler, and easier way to implement alternatives to finite difference or finite element simulations to predict the effect of flow and electrode geometry on the currents observed in channel electrodes where the flow is constrained to a rectangular duct with the electrode embedded flush with the wall of the cell. Several problems are addressed including the evaluation of the transport limited current at a micro channel electrode, the transport of material between two adjacent electrodes in a channel flow and the response of an electrode where the electrode reaction follows a preceding chemical reaction. The approach is shown to give quantitative agreement in the limits for which existing solutions are known whilst offering predictions for the case of the previously unexplored CE reaction at a micro channel electrode. Voltammetry ; Artificial neural network ; Physics ; Statistical physics ; Computer science ; Artificial intelligence ; Electrochemistry ; Quantum mechanics ; Electrode Machine Learning in Materials Science ; Neural Networks and Applications ; Electrochemical Analysis and Applications
Authors: Aishwaryo Ghosh | Soumendu Datta | Tanusri Saha‐Dasgupta
Source: The Journal of Physical Chemistry C (article, 2022-04-08)
Field: Materials Science — Materials Chemistry

Understanding the Trend in Core–Shell Preferences for Bimetallic Nanoclusters: A Machine Learning Approach Finding out the driving factors in core–shell preference of nanoscale binary metal alloys is important due to their ubiquitous presence in applications ranging from catalysis to biomedical. We consider binary-alloyed metallic nanoparticles encompassing a vast range of alkali, alkaline, basic, 3d, 4d, and 5d transition metals, and p-block metals and determine the core–shell preference by calculating the segregation energies of single-atom alloy clusters by density functional theory. Application of machine learning to this large database, built on features characterizing the constituents, leads to the identification of four key factors: (i) cohesive energy difference, (ii) atomic radius difference, (iii) coordination number difference, and (iv) magnetism, providing the core-to-shell preference of a given constituent. Interestingly, the relative importance of one key feature over another is found to be decided by the metal type. Our analysis also predicts that, for very small and very large differences of cohesive energy of the constituents, instead of core–shell structure, mixed and Janus structures are stabilized, respectively. Our exhaustive study will be useful in designing bimetallic nanoalloys with specific chemical ordering of the constituent species. Bimetallic strip ; Nanoclusters ; Shell (structure) ; Atom (system on chip) ; Materials science ; Density functional theory ; Chemical physics ; Preference ; Binary number ; Core (optical fiber) ; Nanotechnology ; Metal ; Chemistry ; Computer science ; Computational chemistry ; Metallurgy ; Mathematics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Electrocatalysts for Energy Conversion
Authors: Pedro Borlido | Jonathan Schmidt | Hai‐Chen Wang | Silvana Botti | Miguel A. L. Marques
Source: npj Computational Materials (article, 2022-07-20)
Field: Materials Science — Materials Chemistry

Computational screening of materials with extreme gap deformation potentials Abstract In this work, we present a large-scale study of gap deformation potentials based on density-functional theory calculations for over 5000 semiconductors. As expected, in most cases the band gap decreases for increasing volume with deformation potentials that can reach values of almost −15 eV. We find, however, also a sizeable number of materials with positive deformation potentials. Notorious members of this group are halide perovskites, known for their applications in photovoltaics. We then focus on understanding the physical reasons for so different values of the deformation potentials by investigating the correlations between this property and a large number of other material and compositional properties. We also train explainable machine learning models as well as graph convolutional networks to predict deformation potentials and establish simple rules to understand predicted values. Finally, we analyze in more detail a series of materials that have record positive and negative deformation potentials. Deformation (meteorology) ; Band gap ; Photovoltaics ; Materials science ; Semiconductor ; Statistical physics ; Physics ; Photovoltaic system ; Optoelectronics ; Composite material ; Engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Bohayra Mortazavi | Fazel Shojaei | Masoud Shahrokhi | Timon Rabczuk | Alexander V. Shapeev | Xiaoying Zhuang
Source: Batteries (article, 2022-10-19)
Field: Materials Science — Materials Chemistry

Electronic, Optical, Mechanical and Li-Ion Storage Properties of Novel Benzotrithiophene-Based Graphdiyne Monolayers Explored by First Principles and Machine Learning Recently, benzotrithiophene graphdiyne (BTT-GDY), a novel two-dimensional (2D) carbon-based material, was grown via a bottom-up synthesis strategy. Using the BTT-GDY lattice and by replacing the S atoms with N, NH and O, we designed three novel GDY lattices, which we named BTHP-, BTP- and BTF-GDY, respectively. Next, we explored structural, electronic, mechanical, optical, photocatalytic and Li-ion storage properties, as well as carrier mobilities, of novel GDY monolayers. Phonon dispersion relations, mechanical and failure behavior were explored using the machine learning interatomic potentials (MLIPs). The obtained HSE06 results reveal that BTX-GDYs (X = P, F, T) are direct gap semiconductors with band gaps in the range of 2.49–2.65 eV, whereas the BTHP-GDY shows a narrow indirect band gap of 0.06 eV. With appropriate band offsets, good carrier mobilities and a strong capability for the absorption of visible and ultraviolet range of light, BTF- and BTT-GDYs were predicted to be promising candidates for overall photocatalytic water splitting. The BTHP-GDY nanosheet, noticeably, was found to yield an ultrahigh Li-ion storage capacity of over 2400 mAh/g. The obtained findings provide a comprehensive vision of the critical physical properties of the novel BTT-based GDY nanosheets and highlight their potential for applications in nanoelectronics and energy storage and conversion systems. Materials science ; Band gap ; Nanosheet ; Semiconductor ; Ion ; Monolayer ; Photocatalysis ; Optoelectronics ; Nanotechnology ; Chemistry ; Organic chemistry Graphene research and applications ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Xiaoxun Gong | He Li | Nianlong Zou | Runzhang Xu | Wenhui Duan | Yong Xu
Source: arXiv (Cornell University) (preprint, 2022-10-25)
Field: Materials Science — Materials Chemistry

General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian Combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating a priori knowledge and symmetry requirements is a key challenging subject. Here we propose an E(3)-equivariant deep-learning framework to represent density functional theory (DFT) Hamiltonian as a function of material structure, which can naturally preserve the Euclidean symmetry even in the presence of spin-orbit coupling. Our DeepH-E3 method enables very efficient electronic-structure calculation at ab initio accuracy by learning from DFT data of small-sized structures, making routine study of large-scale supercells ($&gt; 10^4$ atoms) feasible. Remarkably, the method can reach sub-meV prediction accuracy at high training efficiency, showing state-of-the-art performance in our experiments. The work is not only of general significance to deep-learning method development, but also creates new opportunities for materials research, such as building Moiré-twisted material database. Ab initio ; Density functional theory ; Hamiltonian (control theory) ; Equivariant map ; Computer science ; Artificial neural network ; Deep learning ; Electronic structure ; Artificial intelligence ; A priori and a posteriori ; Euclidean geometry ; Quantum mechanics ; Physics ; Mathematics ; Mathematical optimization ; Geometry ; Pure mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography