jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kartik Sau | Tamio Ikeshoji | Godwill Mbiti Kanyolo | Titus Masese
Source: ChemRxiv (preprint, 2021-01-06)
Field: Chemistry — Inorganic Chemistry

Modelling Cationic Diffusion in Nickel-Based Honeycomb Layered Tellurates using Vashishta-Rahman Interatomic Potential and Relevant Insights &lt;b&gt;Although the fascinatingly rich crystal chemistry of honeycomb layered oxides has been accredited as the propelling force behind their remarkable electrochemistry, the atomistic mechanisms surrounding their operations remain unexplored. Thus, herein, we present an extensive molecular dynamics study performed systematically using a reliable set of inter-atomic potential parameters of &lt;/b&gt;&lt;i&gt;A&lt;/i&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;b&gt;Ni&lt;/b&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;b&gt;TeO&lt;/b&gt;&lt;sub&gt;6&lt;/sub&gt;&lt;b&gt; (where &lt;/b&gt;&lt;i&gt;A&lt;/i&gt;&lt;b&gt; = Li, Na, and K). We demonstrate the effectiveness of the Vashishta-Rahman form of the inter-atomic potential in reproducing various structural and transport properties of this promising class of materials and predict an exponential increase in cationic diffusion with larger inter-layer distances. The simulations demonstrate the correlation between broadened inter-layer (inter-slab) distances associated with the larger ionic radii of K and Na compared to Li and the enhanced cationic conduction exhibited in K&lt;/b&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;b&gt;Ni&lt;/b&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;b&gt;TeO&lt;/b&gt;&lt;sub&gt;6&lt;/sub&gt;&lt;b&gt; and Na&lt;/b&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;b&gt;Ni&lt;/b&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;b&gt;TeO&lt;/b&gt;&lt;sub&gt;6&lt;/sub&gt;&lt;b&gt; relative to Li&lt;/b&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;b&gt;Ni&lt;/b&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;b&gt;TeO&lt;/b&gt;&lt;sub&gt;6&lt;/sub&gt;&lt;b&gt;. Whence, our findings connect lower potential energy barriers, favourable cationic paths and wider bottleneck size along the cationic diffusion channel within frameworks (comprised of larger mobile cations) to the improved cationic diffusion experimentally observed in honeycomb layered oxides. Furthermore, we elucidate the role of inter-layer distance and cationic size in cationic diffusion. Our theoretical studies reveal the dominance of inter-layer distance over cationic size, a crucial insight into the further performance enhancement of honeycomb layered oxides.&lt;/b&gt;&lt;br&gt; Chemistry ; Crystallography Zeolite Catalysis and Synthesis ; Advanced Thermoelectric Materials and Devices ; Ionic liquids properties and applications
Authors: Shiqi Wu | Laura Abad Galán | Margaux Roux | François Riobé | Boris Le Guennic | Y. Guyot | Tangui Le Bahers | Laurent Micouin | Olivier Maury | Erica Benedetti
Source:  (preprint, 2021-07-12)
Field: Chemistry — Spectroscopy

Tuning the excited state properties of [2.2]paracyclophane-based antennas to ensure efficient sensitization of lanthanide ions or singlet oxygen generation. The multi-step synthesis of original antennas incorporating substituted [2.2]paracyclophane (pCp) moieties in the -conjugated skeleton is described. These antennas, functionalized with electron an donor alkoxy fragment (A1) or with a fused coumarin derivative (A2) are incorporated in a triazacyclonane macrocyclic ligand L1 or L2, respectively for the design of Eu(III), Yb(III) and Gd(III) complexes. A combined photophysical/theoretical study reveals that A1 presents a charge transfer character via the through-space paracyclophane conjugation, whereas A2 presents only local excited states centered on the coumarin-paracyclophane moiety, strongly favoring triplet state population via intersystem crossing. The resulting complexes EuL1 and YbL2 are fully emissive in the red and near infrared, respectively whereas the GdL2 complex acts as photosensitizer for the generation of singlet oxygen. Intersystem crossing ; Singlet oxygen ; Photochemistry ; Excited state ; Moiety ; Chromophore ; Chemistry ; Singlet state ; Lanthanide ; Conjugated system ; Alkoxy group ; Triplet state ; Photosensitizer ; Ion ; Stereochemistry ; Molecule ; Oxygen ; Physics ; Atomic physics ; Organic chemistry ; Polymer Molecular Sensors and Ion Detection ; Lanthanide and Transition Metal Complexes ; Luminescence and Fluorescent Materials
Authors: Sichen Mi
Source: Infoscience (Ecole Polytechnique Fédérale de Lausanne) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Shining Diamond: from Micro- and Nano-Engineering to Quantum Photonics The discovery of room-temperature single photon emission from color centers in diamond has in recent years boosted research interest in this renowned material in photonics and quantum community and beyond. Following understanding some basic principles of how the color centers work, new challenges arose: designing and fabricating real devices that leverage the unrivaled properties that diamond possesses. Due to this material's extreme physical hardness and its chemical resistance to etchants, the established semiconductor technologies cannot straightforwardly answer to the call for scalable and reliable fabrication of micro- and nano-photonic devices in single crystal diamond. In this thesis, we first demonstrate an unconventional polishing protocol with ion beam etching, which makes possible wafer-scale fine finishing of diamond substrates, the starting point for any subsequent processing. Following this we present the experimental observation of self-organized nanotextures on diamond induced by ion beam irradiation, a lithography-free approach for nanoengineering periodic structures. Regarding device fabrication, we obtained free-standing micro- and nano-photonic structures using angled reactive ion etching, facilitated by a novel and reproducible Faraday cage design. We also propose possible designs to interconnect individual components in hope of realizing a photonic integrated circuit in the future. Last but not least, we present spectroscopic characterization of single and ensemble color centers either grown by microwave plasma chemical vapor deposition, or fabricated by ion implantation with subsequent high-temperature annealing, which are the fundamental building blocks for quantum applications. Photoluminescence spectra that, to our knowledge, are not seen in literature were recorded for ensemble silicon-vacancy centers at cryogenic temperature, signifying potentially unknown mechanism for tuning the optical transitions, and unexplored possibilities with this exciting material platform for quantum technologies. Photonics ; Diamond ; Nanotechnology ; Nano- ; Engineering physics ; Materials science ; Engineering ; Physics ; Optoelectronics ; Quantum mechanics ; Metallurgy Diamond and Carbon-based Materials Research ; Advanced Surface Polishing Techniques
Authors: Weixuan Zeng | Ouissam El Bakouri | Dariusz W. Szczepanik | Hugo Bronstein | Henrik Ottosson
Source: ChemRxiv (preprint, 2021-01-05)
Field: Chemistry — Organic Chemistry

Excited State Character of Cibalackrot-Type Compounds Interpreted in Terms of Hückel-Aromaticity: A Rational for Singlet Fission Chromophore Design The exact energies of the lowest singlet and triplet excited states in organic chromophores are crucial to their performance in optoelectronic devices. The possibility of utilizing singlet fission to enhance the performance of photovoltaic devices has resulted in a wide demand for tuneable, stable organic chromophores with wide S&lt;sub&gt;1&lt;/sub&gt; – T&lt;sub&gt;1&lt;/sub&gt; energy gaps (&gt;1 eV). Cibalackrot-type compounds were recently considered to have favorably positioned excited state energies for singlet fission, and they were found to have a degree of aromaticity in the lowest triplet excited state (T&lt;sub&gt;1&lt;/sub&gt;). This work reports on a revised and deepened theoretical analysis taking into account the excited state Hückel-aromatic (instead of Baird-aromatic) as well as diradical characters, with the aim to design new organic chromophores based on this scaffold in a rational way starting from qualitative theory. We demonstrate that the substituent strategy can effectively adjust the spin populations on the chromophore moieties and thereby manipulate the excited state energy levels. Additionally, the improved understanding of the aromatic characters enables us to demonstrate a feasible design strategy to vary the excited state energy levels by tuning the number and nature of Hückel-aromatic units in the excited state. Finally, our study elucidates the complications and pitfalls of the excited state aromaticity and antiaromaticity concepts, highlighting that quantitative results from quantum chemical calculations of various aromaticity indices must be linked with qualitative theoretical analysis of the character of the excited states. Excited state ; Aromaticity ; Singlet fission ; Chromophore ; Diradical ; Singlet state ; Antiaromaticity ; Chemistry ; Computational chemistry ; Chemical physics ; Photochemistry ; Atomic physics ; Physics ; Molecule ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Organic Electronics and Photovoltaics ; Fullerene Chemistry and Applications
Authors: Chengyun Zhang | Xiang Cai | Haoran Qiao | Yun Zhang | Yejian Wu | Xinqiao Wang | Haiying Xie | Feng Luo | Hongliang Duan
Source: ChemRxiv (preprint, 2021-07-27)
Field: Materials Science — Materials Chemistry

Self-supervised molecular pretraining strategy for low-resource reaction prediction scenarios In the face of low-resource reaction training samples, we construct a chemical platform for addressing small-scale reaction prediction problem. By using a self-supervised pretraining strategy called MASS, the transformer model can absorb the chemical information about 1 billion molecules and then finetunes on small-scale reaction prediction, which is different from previous works that only rely on reaction samples. To demonstrate the broad applicability of our approach, we adopt three dif-ferent name reactions in our work. In the Baeyer-Villiger, Heck and Sharpless asymmetric epoxidation reaction prediction tasks, the average accuracies increase by 5.7%, 10.8%, 4.8% respectively, marking an important step to low-resource reaction prediction. Construct (python library) ; Transformer ; Scale (ratio) ; Computer science ; Resource (disambiguation) ; Reaction conditions ; Artificial intelligence ; Machine learning ; Chemistry ; Engineering ; Organic chemistry ; Catalysis ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Umit Volkan Ucak | Islambek Ashyrmamatov | Junsu Ko | Juyong Lee
Source:  (preprint, 2021-08-04)
Field: Materials Science — Materials Chemistry

RetroTRAE: retrosynthetic translation of atomic environments with Transformer We present the new retrosynthesis prediction method RetroTRAE using fragment-based tokenization combined with the Transformer architecture. RetroTRAE represents chemical reactions by using the changes of fragment sets of molecules using the atomic environment fragmentation scheme. Atom environments stand as an ideal, chemically meaningful building blocks together producing a high resolution molecular representation. Describing a molecule with a set of atom environments establishes a clear relationship between translated product-reactant pairs due to conservation of atoms in reactions. Our model achieved a top-1 accuracy of 67.1% within the bioactively similar range for USPTO test dataset, outperforming the other state of the art, translation methods. We investigated the effect of different encoding scenarios on predicting the reactant candidates. We also critically assessed the retrieval process that converts a set of fragments into a molecule with respect to coverage, degeneracy and resolution. Our new template-free model for retrosynthetic prediction provides fast and reliable retrosynthetic route planning for substances whose fragmentation patterns are revealed. Retrosynthetic analysis ; Fragmentation (computing) ; Computer science ; Fragment (logic) ; Machine translation ; Transformer ; Artificial intelligence ; Chemistry ; Algorithm ; Programming language ; Engineering ; Stereochemistry ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Björn Siekmeyer | Dennis Lübken | Kevin Bajerke | Markus Kalesse
Source:  (preprint, 2021-08-20)
Field: Chemistry — Organic Chemistry

Bioinspired Total Synthesis of (±)-Antroalbocin A enabled by a Photochemical 1,3-Acyl Shift The first bioinspired and protecting group free total synthesis of the antibacterial sesquiterpenoid antroalbocin A has been achieved in five linear steps from a literature-known intermediate with an overall yield of 6.7%. An intramolecular Robinson annulation gave rapid access to the tricyclic enone as starting material for a photochemical domino process of deconjugation and sigmatropic 1,3 acyl shift. Herein we further describe studies towards the use of photolytic sigmatropic 1,3-acyl shifts in the synthesis of bridged terpenoid building blocks. Annulation ; Intramolecular force ; Chemistry ; Sigmatropic reaction ; Enone ; Total synthesis ; Yield (engineering) ; Photochemistry ; Stereochemistry ; Organic chemistry ; Catalysis ; Physics Synthetic Organic Chemistry Methods ; Traditional and Medicinal Uses of Annonaceae ; Marine Sponges and Natural Products
Authors: Lucas Foppa | Luca M. Ghiringhelli | Frank Girgsdies | Maike Hashagen | Pierre Kube | Michael Hävecker | Spencer J. Carey | Andrey Tarasov | Peter A. Kraus | Frank Rosowski | Robert Schlögl | Annette Trunschke | Matthias Scheffler
Source: arXiv (Cornell University) (preprint, 2021-02-16)
Field: Materials Science — Materials Chemistry

Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence Heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes, e.g., the different surface chemical reactions, and the dynamic re-structuring of the catalyst material at reaction conditions. Modelling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters ("materials genes") reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of "clean data", containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design. Catalysis ; Structuring ; Heterogeneous catalysis ; Biochemical engineering ; Vanadium ; Computer science ; Function (biology) ; Set (abstract data type) ; Biological system ; Materials science ; Nanotechnology ; Process engineering ; Chemistry ; Engineering ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Lucas Foppa | Luca M. Ghiringhelli | Frank Girgsdies | Maike Hashagen | Pierre Kube | Michael Hävecker | Spencer J. Carey | Andrey Tarasov | Peter Kraus | Frank Rosowski | Robert Schlögl | Annette Trunschke | Matthias Scheffler
Source: ChemRxiv (preprint, 2021-02-17)
Field: Materials Science — Materials Chemistry

Materials Genes of Heterogeneous Catalysis from Clean Experiments and Artificial Intelligence Heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes, e.g., the different surface chemical reactions, and the dynamic re-structuring of the catalyst material at reaction conditions. Modelling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters ("materials genes") reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of "clean data", containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design.&lt;br&gt; Catalysis ; Structuring ; Heterogeneous catalysis ; Vanadium ; Biochemical engineering ; Function (biology) ; Computer science ; Set (abstract data type) ; Biological system ; Materials science ; Nanotechnology ; Process engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Lucas Foppa | Christopher Sutton | Luca M. Ghiringhelli | Sandip De | Patricia Löser | Stephan A. Schunk | Ansgar Schaefer | Matthias Scheffler
Source: ChemRxiv (preprint, 2021-10-20)
Field: Materials Science — Materials Chemistry

Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence The design of heterogeneous catalysts is challenged by the complexity of materials and processes that govern reactivity and by the fact that the number of good catalysts is very small compared to the number of possible materials. Here, we show how the subgroup-discovery (SGD) artificial-intelligence approach can be applied to an experimental plus theoretical data set to identify constraints on key physicochemical parameters, the so-called SG rules, which exclusively describe materials and reaction conditions with outstanding catalytic performance. By using high-hroughput experimentation, 120 SiO 2 -supported catalysts containing ruthenium, tungsten and phosphorus were synthesized and tested in the catalytic oxidation of propylene. As candidate descriptive parameters, the temperature and ten parameters related to the composition and chemical nature of the catalyst materials, derived from calculated free-atom properties, were offered. The temperature, the phosphorus content, and the composition-weighted electronegativity are identified as key parameters describing high yields towards the value-added oxygenate products acrolein and acrylic acid. The SG rules not only reflect the underlying processes particularly associated to high performance but also guide the design of more complex catalysts containing up to five elements in their composition. Catalysis ; Electronegativity ; Reactivity (psychology) ; Ruthenium ; Chemistry ; Acrolein ; Chemical engineering ; Materials science ; Computer science ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Luigi Sbailò | Matthias Scheffler | Luca M. Ghiringhelli
Source: Bulletin of the American Physical Society (article, 2021-03-19)
Field: Materials Science — Materials Chemistry

The NOMAD Artificial-Intelligence Toolkit: Web-Based FAIR-Data-Driven Materials Science Computer science ; Web application ; World Wide Web ; Data science Machine Learning in Materials Science ; Advanced X-ray and CT Imaging
Authors: Christian Wagner | Klaus‐Robert Müller | F. Stefan Tautz
Source: TheScienceBreaker (article, 2021-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

How an artificial intelligence bends a single molecule Nature often inspires scientists to engineer molecules for cutting-edge technologies like quantum computers. However, it has been challenging to handle molecular-scale motions within our hands. Our study proposes that it may be time for us to hand the ‘controller’ to artificial intelligence that can perform such a complex task very precisely. - submission by Christian Wagner, Klaus-Robert Müller, F. Stefan Tautz Artificial intelligence ; Computer science ; Psychology Various Chemistry Research Topics
Authors: Lucas Foppa | Luca M. Ghiringhelli | Robert Schlögl | Annette Trunschke | Matthias Scheffler
Source: Bulletin of the American Physical Society (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Identification of "materials genes" governing heterogeneous catalysis using "clean experiments" and artificial intelligence Identification (biology) ; Materials science ; Artificial intelligence ; Nanotechnology ; Biochemical engineering ; Computer science ; Engineering ; Biology ; Ecology Machine Learning in Materials Science
Authors: Dil K. Limbu
Source: Aquila Digital Community (University of Southern Mississippi) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Approaches to Complex Materials: Applications to Amorphous Solids While conventional approaches to materials modeling made significant contributions and advanced our understanding of materials properties in the past decades, these approaches often cannot be applied to disordered materials (e.g., glasses) for which accurate total-energy functionals or forces are either not available or it is infeasible to employ due to computational complexities associated with modeling disordered solids in the absence of translational symmetry. In this dissertation, a number of information-driven probabilistic methods were developed for the structural determination of a range of materials including disordered solids to transition metal clusters. The ground-state structures of transition-metal clusters of iron, nickel, and copper were determined by a force-biased Monte Carlo method and their structural and electronic properties were studied comparatively via force-biased Monte Carlo and ab initio simulations. The force-biased Monte Carlo approach has shown unambiguously that it can effectively determine the putative ground-state structures of a number of small transition-metal clusters. For complex amorphous materials, an information-driven probabilistic viewpoint was adopted by posing structural determination of disordered solids as an inferential program and the problem of materials design was addressed as an optimization program, jointly supported by experimental data and information. The hallmark of this new approach is that it can produce atomistic configurations of amorphous solids, which are thermodynamically stable and close to a stable local minimum of a quantum-mechanical total-energy functional. The models have structural, topological, electronic, and vibrational properties comparable to experiments. The data-driven approach presented here for amorphous solids not only can produce overall structural and electronic properties but also the microstructural properties of realistic samples from experiments, such as voids and vacancy-type defects, which cannot be addressed directly using currently available computational methods. Ab initio hydrogen dynamics were simulated inside nanometer-size voids in a-Si within the framework of the density-functional theory and the study revealed that the microstructure of the hydrogen distribution and the morphology of the voids were characterized by the presence of a significant number of monohydride Si–H bonds, along with a few dihydride Si–H2 configurations but not any isolated hydrogen. The study also revealed that a considerable number of total H atoms inside voids can appear as H2 molecules. The densities of the bonded and nonbonded hydrogens are observed to be consistent with those from the infrared and Rutherford backscattering spectrometry measurements. Amorphous solid ; Computer science ; Materials science ; Chemistry ; Crystallography Machine Learning in Materials Science