jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ilia Igashov | Nikita Pavlichenko | Sergei Grudinin
Source: Machine Learning Science and Technology (preprint, 2021-04-16)
Field: Materials Science — Materials Chemistry

Spherical convolutions on molecular graphs for protein model quality assessment Abstract Processing information on three-dimensional (3D) objects requires methods stable to rigid-body transformations, in particular rotations, of the input data. In image processing tasks, convolutional neural networks achieve this property using rotation-equivariant operations. However, contrary to images, graphs generally have irregular topology. This makes it challenging to define a rotation-equivariant convolution operation on these structures. In this work, we propose spherical graph convolutional network that processes 3D models of proteins represented as molecular graphs. In a protein molecule, individual amino acids have common topological elements. This allows us to unambiguously associate each amino acid with a local coordinate system and construct rotation-equivariant spherical filters that operate on angular information between graph nodes. Within the framework of the protein model quality assessment problem, we demonstrate that the proposed spherical convolution method significantly improves the quality of model assessment compared to the standard message-passing approach. It is also comparable to state-of-the-art methods, as we demonstrate on critical assessment of structure prediction benchmarks. The proposed technique operates only on geometric features of protein 3D models. This makes it universal and applicable to any other geometric-learning task where the graph structure allows constructing local coordinate systems. The method is available at https://team.inria.fr/nano-d/software/s-gcn/ . Computer science ; Rotation (mathematics) ; Graph ; Topology (electrical circuits) ; Equivariant map ; Convolutional neural network ; Algorithm ; Convolution (computer science) ; Geometric networks ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Artificial neural network ; Pure mathematics ; Combinatorics ; Complex network Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Nguyen Thi Han | Vo Khuong Dien | Ming‐Fa Lin
Source: Research Square (preprint, 2021-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Excitonic Effects in The Optical Spectra of Li2SiO3 Compound Abstract Li 2 SiO 3 compound exhibits unique electronic and optical properties. The state-of-the-art analyses, which based on first-principle calculations, have successfully confirmed the concise physical/chemical picture and the orbital bonding in Li-O and Si-O bonds. Especially, the unusual optical response behavior includes a large red shift of the onset frequency due to the extremely strong excitonic effect, the polarization of optical properties along three-directions, various optical excitations structures and the most prominent plasmon mode in terms of the dielectric functions, energy loss functions, absorption coefficients and reflectance spectra. The close connections of electronic and optical properties can identify a specific orbital hybridization for each distinct excitation channel. The presented theoretical framework will be fully comprehending the diverse phenomena and widen the potential application of other emerging materials. Plasmon ; Excitation ; Orbital hybridisation ; Polarization (electrochemistry) ; Optical spectra ; Spectral line ; Dielectric ; Molecular orbital ; Chemical bond ; Materials science ; Molecular physics ; Chemistry ; Optoelectronics ; Physics ; Molecule ; Physical chemistry ; Molecular orbital theory ; Quantum mechanics Crystal Structures and Properties ; Semiconductor materials and devices ; Semiconductor materials and interfaces
Authors: Sizhe Liu | Kyle C. Smith
Source: Physical Review Materials (article, 2021-03-08)
Field: Materials Science — Materials Chemistry

Linking the polyatomic arrangements of interstitial <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi mathvariant="normal">H</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math> and cations to bonding within Prussian blue analogues <i>ab initio</i> using gradient-boosted machine learning Prussian blue analogues (PBAs) are model host compounds for the intercalation of monovalent cations for electrochemical energy storage and separations. However, the interactions among interstitial species and their effects on atomic arrangements therein are understood mainly at a phenomenological level. Analyzing correlations between electronic interactions and polyatomic arrangements in hydrated Prussian blue analogues is complicated by the nonlocal hydrogen-bonding interactions between zeolitic water and framework lattices. Here, we train machine-learning (ML) models to learn DFT-calculated energy landscapes of nickel hexacyanoferrate PBA lattices with various lattice hydration degrees, oxidation states, and types of intercalated alkali cations based on various three-particle feature parameters. This ML approach is enabled by using gradient-boosted regression trees with features that are rotationally invariant geometric parameters. ML model accuracy is shown to be a cation-specific indicator of correlations between energy and polyatomic arrangements. Overlap population analysis among correlated atoms further confirms that such correlations are caused by the competition for dative bonding between Lewis-acid intercalated cations and Lewis bases (cyanide and oxygen in ${\mathrm{H}}_{2}\mathrm{O}$). Examination of lowest-energy structures reveals that cation hydrophilicity and bare ionic radius determine dative-bonding strength, resulting in cation-${\mathrm{H}}_{2}\mathrm{O}$ ordering in interstitial space. The projected energy landscapes of hydrated PBA lattices is also explored in subspaces spanned by certain many-particle feature parameters inspired by ML analysis. The downhill traces in such landscapes indicate that lattice distortion is accompanied by two kinds of collective movements: (1) rearrangements in the hydration shells around small and hydrophilic cations and (2) collective attack of ${\mathrm{H}}_{2}\mathrm{O}$ molecules on nickel-cyanide bonds promoted by large, hydrophobic cations. Ionic radius ; Hydration energy ; Materials science ; Dissociation (chemistry) ; Population ; Crystallography ; Ion ; Physical chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions
Authors: Jonathan Vandermause | Yu Xie | Jin Soo Lim | Cameron J. Owen | Boris Kozinsky
Source: Research Square (preprint, 2021-12-22)
Field: Materials Science — Materials Chemistry

Active learning of reactive Bayesian force fields: Application to heterogeneous catalysis dynamics of H/Pt Bayesian probability ; Dynamics (music) ; Catalysis ; Computer science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Materials science ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Michael Craig | Max García‐Melchor
Source: ChemRxiv (preprint, 2021-02-22)
Field: Chemistry — Electrochemistry

High-Throughput Screening and Rational Design to Drive Discovery in Molecular Water Oxidation Catalysis In this work we study a library of 444 hypothetical complexes for the OER composed of distinct metals (Cr, Mn, Fe, Ru, Co &amp; Ni) and ligand skeletons. These were analysed using density functional theory via different functionals to drive interesting insights and suggestions for future research of this reaction. Catalysis ; Rational design ; Throughput ; Density functional theory ; Work (physics) ; Ligand (biochemistry) ; Combinatorial chemistry ; Chemistry ; High-throughput screening ; Nanotechnology ; Computer science ; Computational chemistry ; Materials science ; Physics ; Organic chemistry ; Thermodynamics ; Receptor ; Biochemistry Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Kobi Felton | Daniel Wigh | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2021-04-19)
Field: Materials Science — Materials Chemistry

Multi-task Bayesian Optimization of Chemical Reactions Recent work has shown how Bayesian optimization (BO) is an efficient method for optimizing expensive experiments such as chemical reactions. However, in previous studies, each optimization has been started from scratch with no information about previous or similar chemical optimization studies. Therefore, BO can still require more iterations than many experimental budgets provide. Here, we overcome this challenge using multi-task BO. Through in silico benchmarking studies, we show how past experimental data can be leveraged to improve the quality and speed of reaction optimization. Bayesian optimization ; Computer science ; Benchmarking ; Task (project management) ; Bayesian probability ; Machine learning ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Multi-Objective Optimization Algorithms
Authors: Sarah Saïdi | Giuseppe Portale | Wim Bras | Alessandro Longo | José Manuel Amigo | D. Chapron | P. Bourson | Daniel Hermida‐Merino
Source: Polymers (article, 2021-11-30)
Field: Chemistry — Analytical Chemistry

Data Mining of Polymer Phase Transitions upon Temperature Changes by Small and Wide-Angle X-ray Scattering Combined with Raman Spectroscopy The complex physical transformations of polymers upon external thermodynamic changes are related to the molecular length of the polymer and its associated multifaceted energetic balance. The understanding of subtle transitions or multistep phase transformation requires real-time phenomenological studies using a multi-technique approach that covers several length-scales and chemical states. A combination of X-ray scattering techniques with Raman spectroscopy and Differential Scanning Calorimetry was conducted to correlate the structural changes from the conformational chain to the polymer crystal and mesoscale organization. Current research applications and the experimental combination of Raman spectroscopy with simultaneous SAXS/WAXS measurements coupled to a DSC is discussed. In particular, we show that in order to obtain the maximum benefit from simultaneously obtained high-quality data sets from different techniques, one should look beyond traditional analysis techniques and instead apply multivariate analysis. Data mining strategies can be applied to develop methods to control polymer processing in an industrial context. Crystallization studies of a PVDF blend with a fluoroelastomer, known to feature complex phase transitions, were used to validate the combined approach and further analyzed by MVA. Raman spectroscopy ; Differential scanning calorimetry ; Context (archaeology) ; Materials science ; Small-angle X-ray scattering ; Polymer ; Phase transition ; Raman scattering ; Phase (matter) ; Crystallization ; Spectroscopy ; Scattering ; Chemical physics ; Thermodynamics ; Optics ; Chemistry ; Physics ; Organic chemistry ; Composite material Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Sukriti Manna | Alberto Hernández | Yunzhe Wang | Peter Lile | Shanping Liu | Tim Mueller
Source: ChemRxiv (preprint, 2021-12-01)
Field: Materials Science — Materials Chemistry

A Database of Low-Energy Atomically Precise Nanoclusters The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined using ab-initio methods to date. We report the methodologies used to discover low-energy clusters as well as the energies, relaxed structures, and physical properties (such as relative stability, HOMO-LUMO gap among others) for over 50,000 clusters across 55 elements. We have identified 589 structures which have energies lower than any previously reported in the literature by at least 1 meV/atom, and we have identified 1340 new structures for clusters that were previously unexplored in the literature. Patterns in the data reveal insights into the chemical and structural relationships among the elements at the nanoscale. We describe how the database can be accessed for future studies and the development of nanocluster-based technologies. Nanoclusters ; Cluster (spacecraft) ; Atom (system on chip) ; Ab initio ; Chemical physics ; Materials science ; Nanotechnology ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Xuanming Lu | Kazuki Nakanishi
Source: Frontiers in Chemical Engineering (review, 2021-12-16)
Field: Materials Science — Materials Chemistry

Synthesis of Hierarchically Porous Metal Oxide Monoliths via Sol–Gel Process Accompanied by Phase Separation From Divalent Metal Salts: A Short Review The sol–gel process accompanied by phase separation is one of the methods to prepare hierarchically porous monoliths, hierarchically porous monolith, which is applicable not only to oxides but also to various materials compositions such as metal phosphates, organic-polymers/carbons, metal-organic frameworks. It is not until recently, however, that progress has been made in the preparation of low-valence metal oxide HPMs, such as those of magnesium, manganese, cobalt, nickel, etc. Due to the difficulty of divalent metal precursors to form homogeneous gels, different approaches from those established for trivalent and tetravalent counterparts have been attempted. This short review introduces the methods and trials in the preparation of metal oxide HPMs from divalent metal salts. Metal ; Oxide ; Materials science ; Inorganic chemistry ; Cobalt ; Porosity ; Divalent metal ; Magnesium ; Chemical engineering ; Nickel ; Divalent ; Monolith ; Manganese ; Sol-gel ; Polymer ; Chemistry ; Metallurgy ; Organic chemistry ; Nanotechnology ; Catalysis ; Composite material Mesoporous Materials and Catalysis ; Layered Double Hydroxides Synthesis and Applications ; Catalytic Processes in Materials Science
Authors: Alexander Scheinker | Frederick Cropp | Sergio Paiagua | D. Filippetto
Source: Research Square (preprint, 2021-04-06)
Field: Materials Science — Materials Chemistry

Adaptive Deep Learning for Time-Varying Systems With Hidden Parameters: Predicting Changing Input Beam Distributions of Compact Particle Accelerators Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems which does not require re-training. Our approach is to include adaptive feedback in the architecture of deep generative convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. Our approach is inspired by biological systems in which separate groups of neurons interact and are controlled and synchronized by external feedbacks. We demonstrate this approach by developing an inverse model of a complex charged particle accelerator system, mapping output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We demonstrate our methods on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics. We also demonstrate our method for automatically tracking the time varying quantum efficiency map of a particle accelerator’s photocathode. Computer science ; Convolutional neural network ; Deep learning ; Beam (structure) ; Artificial intelligence ; Artificial neural network ; Physics ; Optics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: M. Carmen Ortega‐Liebana | Nicola J. Porter | Catherine Adam | Teresa Valero | Lloyd Hamilton | Dirk Sieger | Catherina G. Becker | Asier Unciti‐Broceta
Source: Angewandte Chemie (article, 2021-11-03)
Field: Chemistry — Organic Chemistry

Truly‐Biocompatible Gold Catalysis Enables Vivo‐Orthogonal Intra‐CNS Release of Anxiolytics Being recognized as the best-tolerated of all metals, the catalytic potential of gold (Au) has thus far been hindered by the ubiquitous presence of thiols in organisms. Herein we report the development of a truly-catalytic Au-polymer composite by assembling ultrasmall Au-nanoparticles at the protein-repelling outer layer of a co-polymer scaffold via electrostatic loading. Illustrating the in vivo-compatibility of the novel catalysts, we show their capacity to uncage the anxiolytic agent fluoxetine at the central nervous system (CNS) of developing zebrafish, influencing their swim pattern. This bioorthogonal strategy has enabled -for the first time- modification of cognitive activity by releasing a neuroactive agent directly in the brain of an animal. Biocompatible material ; Chemistry ; Catalysis ; In vivo ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Organic chemistry ; Biomedical engineering Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Rocco Peter Fornari | Piotr de Silva
Source: ChemRxiv (preprint, 2021-06-09)
Field: Chemistry — Organic Chemistry

A Computational Protocol Combining DFT and Cheminformatics for Prediction of pH-Dependent Redox Potentials &lt;p&gt;We present and validate a robust procedure to calculate the redox potentials of organic molecules at any pH value, using widely available quantum chemistry and cheminformatics methods. Using a consistent experimental data set for validation, we explore and compare a few different methods for calculating reaction free energies, the treatment of solvation, and the effect of pH on redox potentials. We find that the B3LYP hybrid functional with COSMO solvation method, in conjunction with thermal contributions evaluated from BLYP gas-phase harmonic frequencies, yields a good prediction of pH=0 redox potentials at a moderate computational cost. To predict how the potentials are affected by pH, we propose an improved version of the Alberty-Legendre transform that allows the construction of a more realistic Pourbaix diagram by taking into account how the protonation state changes with pH.&lt;/p&gt; Cheminformatics ; Solvation ; Pourbaix diagram ; Chemistry ; Redox ; Protonation ; Computational chemistry ; Basis set ; Molecule ; Thermodynamics ; Physical chemistry ; Density functional theory ; Electrochemistry ; Inorganic chemistry ; Physics ; Organic chemistry Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Wan Nazri Wan Busu | Shan Chen | Dalila Shahdan | Muhammad Jefri Mohd Yusof | Mohamad Jani Saad | Sahrim Ahmad
Source: International Journal of Integrated Engineering (article, 2021-08-31)
Field: Materials Science — Polymers and Plastics

Statistical Optimization Using Response Surface Methodology for Enhanced Tensile Strength of Polyethylene/Graphene Nanocomposites Despite having remarkable features such as low density, ease of fabrication and recyclability, linear low-density polyethylene (LLDPE) has several drawbacks like poor stiffness and low creep resistance which fortunately can be improved by incorporating with other suitable nanofillers. In this study, graphene nanoplatelets (GNPs) that are well-known for its high surface area and superior stability were selected to reinforce the polymer network of LLDPE via melt blending. During mixing processing, the rotor speed, mixing temperature and mixing time parameters are manipulated with the aids of 5-level-3-factor central composite rotatable design (CCRD) in order to determine the optimization of processing parameters in preparing LLDPE/GNPs nanocomposites. The experimental data is fitted with the statistically significant quadratic model with R2 value of 0.8601. The results showed that the tensile strength of LLDPE/GNPs nanocomposites could be extended to 24.80 MPa. The optimum processing parameters for preparation of LLDPE/GNPs nanocomposites were found to be at 101 rpm rotor speed, 139.8oC of mixing temperature and 13.2 min of mixing time, resulting in LLDPE/GNPs nanocomposites with tensile strength of 24.11 MPa. Conclusively, our study has provided a novel statistical design of experiment to obtain the optimum processing parameters in preparing LLDPE/GNPs nanocomposites. Linear low-density polyethylene ; Materials science ; Ultimate tensile strength ; Composite material ; Nanocomposite ; Mixing (physics) ; Response surface methodology ; Polyethylene ; Composite number ; Mathematics Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Carbon Nanotubes in Composites
Authors: Yuanheng Wang | Jiajun Ren | Zhigang Shuai
Source: ChemRxiv (preprint, 2021-05-28)
Field: Chemistry — Physical and Theoretical Chemistry

Evaluating the Anharmonicity Contributions to the Molecular Excited State Internal Conversion Rates with Finite Temperature TD-DMRG In this work, we propose a new method to calculate the molecular nonradiative electronic relaxation rates based on the numerically exact time-dependent density matrix renormalization group theory (TD-DMRG). This method could go beyond the existing frameworks under the harmonic approximation (HA) of the potential energy surface (PES) so that the anharmonic effect could be considered, which is of vital importance when the electronic energy gap is much larger than the vibrational frequency. We calculate the internal conversion (IC) rates in a two-mode model with Morse potential to investigate the validity of HA. We find that HA is unsatisfactory unless only the lowest several vibrational states of the lower electronic state are involved in the transition process when the adiabatic excitation energy is relatively low. As the excitation energy increases, HA first underestimates and then overestimates the IC rates when the excited state PES shifts towards the dissociative side of the ground state PES. On the contrary, HA slightly overestimates the IC rates when the excited state PES shifts towards the repulsive side. In both cases, higher temperature enlarges the error of HA. As a real example to demonstrate the effectiveness and scalability of the method, we calculate the IC rates of azulene from $S_1$ to $S_0$ on the ab initio anharmonic PES approximated by 1-mode representation. The calculated IC rates of azulene under HA are consistent with the analytically exact results. The rates on anharmonic PES are 30%-40% higher than the rates under HA. Anharmonicity ; Excited state ; Azulene ; Density matrix renormalization group ; Ground state ; Adiabatic process ; Ab initio ; Excitation ; Physics ; Atomic physics ; Potential energy ; Chemistry ; Quantum mechanics ; Renormalization group Photochemistry and Electron Transfer Studies ; Free Radicals and Antioxidants ; Synthesis and Properties of Aromatic Compounds