jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rui Su | Yu, Jieyi | Pengfei Guan | Wang, Weihua
Source: arXiv (Cornell University) (preprint, 2022-11-07)
Field: Materials Science — Materials Chemistry

Efficient and accurate simulation of vitrification in multi-component metallic liquids with neural-network potentials Constructing accurate interatomic potential and overcoming the exponential growth of structural equilibration time are challenges to the atomistic investigations of the composition-dependent structure and dynamics during the vitrification process of deeply supercooled multi-component metallic liquids. In this work, we describe a state-of-the-art strategy to address these challenges simultaneously. In the case of the representative Zr-Cu-Al system, in combination with a general algorithm for generating the neural-network potentials (NNP) of multi-component metallic glasses effectively and accurately, we propose a highly efficient atom-swapping hybrid Monte Carlo (SHMC) algorithm for accelerating the thermodynamic equilibration of deeply supercooled liquids. Extensive calculations demonstrate that the newly developed NNP faithfully reproduces the phase stabilities and structural characteristics obtained from the ab initio calculations and experiments. In the combined NNP-SHMC algorithm, the structure equilibration time in the deeply supercooled temperatures is accelerated by at least five orders of magnitudes, and the quenched glassy samples exhibit comparable stability to those prepared in the laboratory. Our results pave the way for the next-generation studies of the vitrification process and, thereby the composition-dependent glass-forming ability and physical properties of multi-component metallic glasses. Supercooling ; Vitrification ; Component (thermodynamics) ; Materials science ; Amorphous metal ; Work (physics) ; Molecular dynamics ; Phase (matter) ; Artificial neural network ; Atom (system on chip) ; Process (computing) ; Thermodynamics ; Statistical physics ; Chemical physics ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Metallurgy ; Machine learning Material Dynamics and Properties ; Theoretical and Computational Physics ; Glass properties and applications
Authors: José Eduardo Zamudio Díaz Mirón | Matthias Stein
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A benchmark for non-covalent interactions in organometallic crystals Organometallic complexes are the basis for homogeneous catalysis, have applications in materials science and are also active pharmaceutical ingredients. The interaction between transition metal complexes in the solid state is determining their thermodynamics and bio-availability. Non-covalent interactions such as hydrogen bonding and van der Waals are stabilizing crystals of transition metal complexes. The variation of ligand field, central metal atoms and their oxidation and spin states are determinants of the magnitude of their inter-molecular interactions. A comparison of a set of 43 manually curated experimental heats of sublimation (the new XTMC43 set) and results from periodic DFT calculations shows that an agreement to within 9% can be achieved using GGA or mGGA functionals with atom-centred Gaussian-type basis functions. The need for careful assessments of consistency, calibration and reproducibility of experimental and computational data is discussed. Results regarding the new XTMC43 benchmark set are suggested to serve as a starting point for further method development, systematic screening and crystal engineering. Sublimation (psychology) ; van der Waals force ; Chemistry ; Gaussian ; Transition metal ; Thermodynamics ; Supramolecular chemistry ; Chemical physics ; Computational chemistry ; Molecule ; Materials science ; Physical chemistry ; Crystal structure ; Catalysis ; Crystallography ; Physics ; Organic chemistry Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure ; Crystallography and molecular interactions
Authors: Kirill Zinovjev
Source: ChemRxiv (preprint, 2022-08-30)
Field: Materials Science — Materials Chemistry

Electrostatic Embedding of Machine Learning Potentials This work presents a variant of an electrostatic embedding scheme that allows the embedding of arbitrary machine learned potentials trained on molecular systems in vacuo. The scheme is based on physically motivated models of electronic density and polarizability, resulting in a generic model without relying on an exhaustive training set. The scheme only requires in vacuo single point QM calculations to provide training densities and molecular dipolar polarizabilities. As an example, the scheme is applied to create an embedding model for the QM7 dataset using Gaussian Process Regression with only 445 reference atomic environments. The model was tested on SARS-CoV-2 protease complex with PF-00835231, resulting in predicted embedding energy RMSE of 2 kcal/mol, compared to explicit DFT/MM calculations. Embedding ; Polarizability ; Scheme (mathematics) ; Dipole ; Computer science ; Gaussian ; Statistical physics ; Set (abstract data type) ; Artificial intelligence ; Algorithm ; Machine learning ; Theoretical computer science ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Molecule ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Esther‐Lydia Silva‐Ramírez | Inmaculada Cumbrera‐Conde | Rafael Cano‐Crespo | F.L. Cumbrera
Source: Expert Systems (article, 2022-10-20)
Field: Materials Science — Materials Chemistry

Machine learning techniques for the ab initio Bravais lattice determination Abstract Machine learning‐based algorithms have been widely applied recently in different areas due to its ability to solve problems in all fields. In this research, machine learning techniques classifying the Bravais lattices from a conventional X‐ray diffraction diagram have been applied. Indexing algorithms are an essential tool of the preliminary protocol for the structural determination problem in crystallography. The task of reverting the obtained information in reciprocal lattice to direct space is a complex issue. As an alternative way to afford this problem, different machine learning algorithms have been applied and a comparison between them has been conducted. The obtained accuracy was 95.9% using 10‐fold cross‐validation (while the best result obtained so far has been 84%). A model based on Bragg positions was our unique predictor, allowing us to obtain the set of the interplanar lattice distances. Our model was successfully checked with a complex example. In addition, our procedure incorporates the following advantages: robustness versus imprecision in data acquisition and reduction of the amount of necessary input data. This is the first time so far that such classification has been carried out in true ab initio condition. Bravais lattice ; Computer science ; Algorithm ; Reciprocal lattice ; Artificial intelligence ; Machine learning ; Ab initio ; Lattice (music) ; Diffraction ; Crystal structure ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Ludovic Berthier | David R. Reichman
Source: arXiv (Cornell University) (preprint, 2022-08-03)
Field: Materials Science — Materials Chemistry

Modern computational studies of the glass transition The physics of the glass transition and amorphous materials continues to attract the attention of a wide research community after decades of effort. Supercooled liquids and glasses have been studied numerically since the advent of molecular dynamics and Monte Carlo simulations in the last century. Computer studies have greatly enhanced both experimental discoveries and theoretical developments and constitute an active and continually expanding research field. Our goal in this review is to provide a modern perspective on this area. We describe the need to go beyond canonical methods to attack a problem that is notoriously difficult in terms of time scales, length scales, and physical observables. We first summarise recent algorithmic developments to achieve enhanced sampling and faster equilibration using replica exchange methods, cluster and swap Monte Carlo algorithms, and other techniques. We then review some major recent advances afforded by these novel tools regarding the statistical mechanical description of the liquid-to-glass transition as well as the mechanical, vibrational and thermal properties of the glassy solid. We finally describe some important challenges for future research. Statistical physics ; Replica ; Glass transition ; Monte Carlo method ; Supercooling ; Amorphous solid ; Computer science ; Swap (finance) ; Data science ; Physics ; Chemistry ; Mathematics ; Statistics Material Dynamics and Properties
Authors: S. Kadri | Mohamed Tourab | Mahièddine Berkani | Rabie Amraoui | Zeyneb Bordjiba
Source: East European Journal of Physics (article, 2022-03-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Study of Structural, Elastic, Thermal and Transport Properties of Ternary X(X=Co, Rh and Ir)MnAs Obtained by DFT The Density Functional Theory (DFT) with an approximation of generalised gradient is used for the study of elastic, thermodynamic and transport properties and for that of structural stability of ternary Half-Heuslers compounds X(X=Co, Rh and Ir)MnAs. This first predictive study of this compounds determines the mechanical properties such that the compression, shearing, Young modulla and Poisson coefficient without omitting the checking parameters of the nature of these compounds such that hardness, Zener anisotropic facto rand Cauchy pressure. The Pugh ratio and Poisson coefficient have allowed the identification of ductile nature of these compounds. The speed of sound and Debye temperature of these compounds has also been estimated from the elastic constants. The thermodynamic properties have been calculated as well for a pressure interval from zero to 25 GPa. The effect of chemical potential variation on Seebeck coefficient, electric, thermal and electronic conductivities, the power and merit factors have also been studied for different temperatures (300, 600, 900°K), so that these alloys can be better potential candidates for thermoelectric applications. Seebeck coefficient ; Debye model ; Materials science ; Thermodynamics ; Ternary operation ; Thermal expansion ; Thermoelectric effect ; Shearing (physics) ; Figure of merit ; Poisson's ratio ; Density functional theory ; Condensed matter physics ; Chemistry ; Poisson distribution ; Computational chemistry ; Metallurgy ; Composite material ; Physics Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices ; MXene and MAX Phase Materials
Authors: Zaiyun Lin | Shiqiu Yin | Lei Shi | Wenbiao Zhou | YingSheng Zhang
Source: arXiv (Cornell University) (preprint, 2022-04-19)
Field: Materials Science — Materials Chemistry

G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training Retrosynthesis prediction is one of the fundamental challenges in organic chemistry and related fields. The goal is to find reactants molecules that can synthesize product molecules. To solve this task, we propose a new graph-to-graph transformation model, G2GT, in which the graph encoder and graph decoder are built upon the standard transformer structure. We also show that self-training, a powerful data augmentation method that utilizes unlabeled molecule data, can significantly improve the model's performance. Inspired by the reaction type label and ensemble learning, we proposed a novel weak ensemble method to enhance diversity. We combined beam search, nucleus, and top-k sampling methods to further improve inference diversity and proposed a simple ranking algorithm to retrieve the final top-10 results. We achieved new state-of-the-art results on both the USPTO-50K dataset, with top1 accuracy of 54%, and the larger data set USPTO-full, with top1 accuracy of 50%, and competitive top-10 results. Retrosynthetic analysis ; Computer science ; Graph ; Training set ; Autoencoder ; Artificial intelligence ; Machine learning ; Inference ; Encoder ; Beam search ; Artificial neural network ; Data mining ; Theoretical computer science ; Algorithm ; Search algorithm ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: N. Ian Rinehart | Rakesh K. Saunthwal | Joël Wellauer | Andrew F. Zahrt | Lukas Schlemper | Alexander S. Shved | Raphael Bigler | Serena Fantasia | Scott E. Denmark
Source: ChemRxiv (preprint, 2022-09-13)
Field: Chemistry — Organic Chemistry

Development and Validation of a Chemoinformatic Workflow for Predicting Reaction Yield for Pd-Catalyzed C-N Couplings with Substrate Generalizability A machine learning-based tool that provides conditions and predicted yields for Buchwald-Hartwig couplings from a ChemDraw™ structure input is described. The tool is built on an in-house generated experimental dataset that explores a diverse network of reactant pairings. To minimize the number of experiments necessary to produce models and maximize data value, a workflow based on unsupervised machine leaning tools was created. The workflow enables the construction of models which can successfully generalize—making predictions for reactants which are not represented in the dataset. Workflow ; Generalizability theory ; Yield (engineering) ; Substrate (aquarium) ; Computer science ; Value (mathematics) ; Artificial intelligence ; Data mining ; Machine learning ; Combinatorial chemistry ; Chemistry ; Database ; Mathematics ; Physics ; Thermodynamics Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Alexander Moriarty | Kazuki Morita | Keith T. Butler | Aron Walsh
Source: The Journal of Open Source Software (article, 2022-07-05)
Field: Materials Science — Materials Chemistry

UnlockNN: Uncertainty quantification for neural networkmodels of chemical systems Artificial neural network ; Computer science ; Uncertainty quantification ; Artificial intelligence ; Machine learning ; Data mining Machine Learning in Materials Science ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Adeleke Maradesa | Baptiste Py | Ting Hei Wan | Mohammed B. Effat | Francesco Ciucci
Source: ChemRxiv (preprint, 2022-11-14)
Field: Chemistry — Analytical Chemistry

Selecting the Regularization Parameter in the Distribution of Relaxation Times Electrochemical impedance spectroscopy (EIS) is a characterization technique used widely in electrochemistry. Obtaining EIS data is simple when modern electrochemical workstations are used; however, analyzing EIS spectra is still a considerable quandary. The distribution of relaxation times (DRT) has emerged as a solution to this challenge. Nevertheless, DRT deconvolution underlies an ill-posed optimization problem, which is often solved by ridge regression (RR). RR’s accuracy strongly depends on the choice of the regularization level as encapsulated in the parameter λ. In this article, five cross-validation methods (i.e., generalized cross-validation, modified generalized cross-validation, robust generalized cross-validation, re-im cross-validation, and k-fold cross-validation) and the L-curve method are studied for the selection of λ. A hierarchical Bayesian DRT (hyper-λ) deconvolution method is also analyzed, whereby λ_0, a parameter analogous to λ, is obtained through cross-validation. The analysis of synthetic data suggests that the values of λ selected by generalized and modified generalized cross-validation are the most accurate among the methods studied. Furthermore, synthetic EIS spectra show that the hyper-λ approach outperforms optimal RR when λ_0 is obtained by generalized and modified generalized cross-validation. This research is expected to foster additional research on the central topic of regularization level selection for DRT analysis. This article not only explores various cross-validation methods, but also provides, through pyDRTtools, an implementation that may serve as a starting point for future research. Cross-validation ; Regularization (linguistics) ; Deconvolution ; Computer science ; Cross-correlation ; Algorithm ; Mathematics ; Applied mathematics ; Mathematical optimization ; Statistics ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies
Authors: Nur Amilah Fadlina Basri | Mohd Haniff Wahid | Zulkarnain Zainal | Shahrul Ainliah Alang Ahmad | Mohd Rashidi Abdull Manap
Source: Adsorption Science & Technology (article, 2022-01-01)
Field: Materials Science — Biomaterials

Surface Characteristics of Electrospun <i>p</i>-Sulphonated Calix[4]Arene Functionalized Cellulose Acetate Nanofiber and Its Behaviour towards Methylene Blue Adsorption p-sulphonated calix[4]arene ( p-SOCX) functionalized cellulose acetate/graphene oxide (CA/GO) nanofibers were successfully prepared via the electrospinning technique. The presence of p-SOCX within the fiber matrix was ascertained using Fourier Transform Infrared spectroscopy and Carbon-Hydrogen-Nitrogen-Sulphur elemental analyses. Apparent changes in morphology of the samples were observed under scanning electron microscope where fiber diameters increased with increasing p-SOCX content. Preliminary dye removal test at different pH, dosage, and temperature of functionalized CA/GO nanofiber demonstrates enhanced adsorption capacity of methylene blue in presence of p-SOCX compared to pristine CA nanofiber and CA/GO nanofiber at an optimum pH 8. The highest removal efficiency obtained was 88.84% with initial methylene blue dye concentration of 10 mg/L, adsorbent dosage of 20 mg/10 mL, contact time of 30 min at room temperature ca. 293 K. Nanofiber ; Methylene blue ; Adsorption ; Electrospinning ; Chemistry ; Cellulose acetate ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Nuclear chemistry ; Fiber ; Cellulose ; Contact angle ; Chemical engineering ; Polymer chemistry ; Organic chemistry ; Materials science ; Polymer ; Composite material ; Catalysis Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications ; Dendrimers and Hyperbranched Polymers
Authors: Ziba Torkashvand | Kavoos Mirabbaszadeh | Farzaneh Shayeganfar | Changgu Lee
Source: Scientific Reports (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Magneto-optical Kerr effect in surface engineered 2D hexagonal boron nitride Magnetism in atomically thin functional materials can be an important phenomenon for exploring two-dimensional magneto-optics. Magneto-optical experimental data have revealed significant Kerr signals in insulator thin films. Here, the magneto-optical Kerr effect of oxygen functionalized and doped hexagonal boron nitride (hBN) has been investigated by performing first-principles calculations. We calculated Kerr angle and Kerr ellipticity for functionalized hBN as an attention-drawn material. Moreover, increasing of oxygen doping percentage leads to the introduction of surface plasmon to hBN. Our findings show that the functionalized hBN can tolerate high-temperature conditions, keeping oxygen atoms bridge-bonded. These giant opto/magnetic responses of insulating 2D materials provide a platform for the potential designing of magneto-optical devices. Kerr effect ; Hexagonal boron nitride ; Magneto-optic Kerr effect ; Materials science ; Magnetism ; Doping ; Thin film ; Boron nitride ; Surface plasmon ; Condensed matter physics ; Ferromagnetism ; Optoelectronics ; Plasmon ; Oxygen ; Nanotechnology ; Chemistry ; Graphene ; Physics 2D Materials and Applications ; Plasmonic and Surface Plasmon Research ; Quantum and electron transport phenomena
Authors: Jixing Sun | Kun Zhang | Kaixuan Hu | Jiyong Liu | Yu Tian | Xin Wang | Shengchun Yan
Source: Coatings (article, 2022-12-25)
Field: Materials Science — Materials Chemistry

Evolution of Elements on Electrode Surfaces in Gas-Insulated Systems under Electrical Heating Accidents always occur in gas-insulated switchgears (GIS) and gas-insulated lines (GIL) since filmed joint electrodes are produced when internal gases react with the electrode’s surface when there is a discharge or when internal electricals overheat. To solve the problem, this paper analyzed the evolution of elements on the contact electrode. The reaction of the SF6 and electrode’s surface under breakdown currents and overheating conditions was obtained, and the discharge time and discharge current effects upon the transfer of the element were proposed. It was found that the mobility of the F element on the electrode’s surface typically increases after electrical heating. The number of interruptions and short-circuit currents are important factors affecting the transfer of the F element to the electrode. The flashover current is the essential factor that accelerates the transfer of the F element to insulating materials. Frequent switching is a main factor that accelerates the transfer of the F element to the contact. It was also found that Al has little correlations with the breaking process, and metal fluorides become the main components on the electrode’s surface under discharge heating. The research provides a theoretical basis and data support for GIS/GIL surface optimization treatments and the improvement of fault detection methods. Switchgear ; Electrode ; Overheating (electricity) ; Materials science ; Voltage ; Current (fluid) ; Electrical engineering ; Mechanics ; Composite material ; Chemistry ; Engineering High voltage insulation and dielectric phenomena ; Plasma Diagnostics and Applications ; Power Transformer Diagnostics and Insulation
Authors: Ethan F. Bull-Vulpe | Marc Riera | Sigbjørn Løland Bore | Francesco Paesani
Source: ChemRxiv (preprint, 2022-06-22)
Field: Materials Science — Materials Chemistry

Data-Driven Many-Body Potential Energy Functions for Generic Molecules: Linear Alkanes as a Proof-of-Concept Application We present a generalization of the many-body energy (MB-nrg) theoretical/computational framework that enables the development of data-driven potential energy functions (PEFs) for generic covalently bonded molecules, with arbitrary quantum mechanical accuracy. The “nearsightedness of electronic matter” is exploited to define monomers as “natural building blocks” based on their distinct chemical identity. The energy of generic molecules is then expressed as a sum of individual many-body energies of incrementally larger subsystems. The MB-nrg PEFs represent the low-order n-body energies, with n = 1 − 4, using permutationally invariant polynomials derived from electronic structure data carried out at an arbitrary quantum mechanical level of theory, while all higher-order n-body terms (n > 4) are represented by a classical many-body polarization term. As a proof-of-concept application of the general MB-nrg framework, we present MB-nrg PEFs for linear alkanes. The MB-nrg PEFs are shown to accurately reproduce reference energies, harmonic frequencies, and potential energy scans of alkanes, independently of their length. Since, by construction, the MB-nrg framework introduced here can be applied to generic covalently bonded molecules, we envision future computer simulations of complex molecular systems using data-driven MB-nrg PEFs, with arbitrary quantum mechanical accuracy. Quantum ; Proof of concept ; Molecule ; Physics ; Generalization ; Statistical physics ; Quantum mechanics ; Computer science ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced NMR Techniques and Applications