jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Johannes Sandberg | Thomas Voigtmann | Émilie Devijver | N. Jakse
Source: arXiv (Cornell University) (preprint, 2023-12-26)
Field: Materials Science — Materials Chemistry

Feature Selection for High-Dimensional Neural Network Potentials with the Adaptive Group Lasso Neural network potentials are a powerful tool for atomistic simulations, allowing to accurately reproduce \textit{ab initio} potential energy surfaces with computational performance approaching classical force fields. A central component of such potentials is the transformation of atomic positions into a set of atomic features in a most efficient and informative way.In this work, a feature selection method is introduced for high dimensional neural network potentials, based on the Adaptive Group Lasso (AGL) approach. It is shown that the use of an embedded method, taking into account the interplay between features and their action in the estimator, is necessary to optimize the number of features. The method's efficiency is tested on three different monoatomic systems, including Lennard-Jones as a simple test case, Aluminium as a system characterized by predominantly radial interactions, and Boron as representative of a system with strongly directional interactions. The AGL is compared with unsupervised filter methods and found to perform consistently better in reducing the number of features needed to reproduce the reference simulation data. {In particular, our results show the importance of taking into account model predictions in feature selection for interatomic potentials. Artificial neural network ; Computer science ; Estimator ; Filter (signal processing) ; Feature selection ; Feature (linguistics) ; Artificial intelligence ; Set (abstract data type) ; Potential energy ; Algorithm ; Physics ; Mathematics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Che Chen Tho | San‐Dong Guo | Shi‐Jun Liang | Wee‐Liat Ong | Chit Siong Lau | Liemao Cao | Guangzhao Wang | Yee Sin Ang
Source: arXiv (Cornell University) (preprint, 2023-04-06)
Field: Materials Science — Materials Chemistry

MA$_2$Z$_4$ Family Heteorstructures: Promises and Prospects Recent experimental synthesis of ambient-stable MoSi2N4 monolayer have garnered enormous research interests. The intercalation morphology of MoSi2N4 - composed of a transition metal nitride (Mo-N) inner sub-monolayer sandwiched by two silicon nitride (Si-N) outer sub-monolayers - have motivated the computational discovery of an expansive family of synthetic MA2Z4 monolayers with no bulk (3D) material counterpart (where M = transition metals or alkaline earth metals; A = Si, Ge; and N = N, P, As). MA2Z4 monolayers exhibit interesting electronic, magnetic, optical, spintronic, valleytronic and topological properties, making them a compelling material platform for next-generation device technologies. Furthermore, heterostructure engineering enormously expands the opportunities of MA2Z4. In this review, we summarize the recent rapid progress in the computational design of MA2Z4-based heterostructures based on first-principle density functional theory (DFT) simulations - a central \emph{work horse} widely used to understand the physics, chemistry and general design rules for specific targeted functions. We systematically classify the MA2Z4-based heterostructures based on their contact types, and review their physical properties, with a focus on their performances in electronics, optoelectronics and energy conversion applications. We review the performance and promises of MA2Z4-based heterostructures for device applications that include electrical contacts, transistors, spintronic devices, photodetectors, solar cells, and photocatalytic water splitting. This review unveils the vast device application potential of MA2Z4-based heterostructures, and paves a roadmap for the future experimental and theoretical development of MA2Z4-based functional heterostructures and devices. Heterojunction ; Spintronics ; Monolayer ; Materials science ; Nanotechnology ; Density functional theory ; Photodetector ; Electronics ; Optoelectronics ; Engineering physics ; Physics ; Condensed matter physics ; Electrical engineering ; Engineering 2D Materials and Applications ; MXene and MAX Phase Materials ; Machine Learning in Materials Science
Authors: Linnéa Andersson | Chao Zhang
Source: arXiv (Cornell University) (preprint, 2023-08-03)
Field: Chemistry — Electrochemistry

Molecular dynamics simulations of metal-electrolyte interfaces under potential control The interfaces between metal electrodes and liquid electrolytes are prototypical in electrochemistry. That is why it is crucial to have a molecular and dynamical understating of such interfaces for both electrical properties and chemical reactivities under potential control. In this short review, we will categorize different schemes for modelling electrified metal-electrolyte interfaces used in molecular dynamics simulations. Our focus is on the similarities between seemingly different methods and their conceptual connections in terms of relevant electrochemical quantities. Therefore, it can be used as a guideline for developing new methods and building modularized computational protocols for simulating electrified interfaces. Electrolyte ; Molecular dynamics ; Electrochemistry ; Computer science ; Focus (optics) ; Nanotechnology ; Categorization ; Chemical physics ; Electrode ; Chemistry ; Materials science ; Computational chemistry ; Physics ; Physical chemistry ; Artificial intelligence Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: Khalid El-Awady
Source: arXiv (Cornell University) (preprint, 2023-12-25)
Field: Materials Science — Materials Chemistry

VAE for Modified 1-Hot Generative Materials Modeling, A Step Towards Inverse Material Design We investigate the construction of generative models capable of encoding physical constraints that can be hard to express explicitly. For the problem of inverse material design, where one seeks to design a material with a prescribed set of properties, a significant challenge is ensuring synthetic viability of a proposed new material. We encode an implicit dataset relationships, namely that certain materials can be decomposed into other ones in the dataset, and present a VAE model capable of preserving this property in the latent space and generating new samples with the same. This is particularly useful in sequential inverse material design, an emergent research area that seeks to design a material with specific properties by sequentially adding (or removing) elements using policies trained through deep reinforcement learning. Generative grammar ; Inverse ; Computer science ; Set (abstract data type) ; Encoding (memory) ; Space (punctuation) ; ENCODE ; Property (philosophy) ; Material Design ; Generative model ; Generative Design ; Artificial intelligence ; Mathematics ; Engineering ; Programming language Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence
Authors: Yubai Shi | Ri He | Bingwen Zhang | Zhicheng Zhong
Source: arXiv (Cornell University) (preprint, 2023-12-21)
Field: Materials Science — Materials Chemistry

Revisit the phase diagram and piezoelectricity of lead zirconate titanate from first principles Lead zirconate titanate (PbZr1-xTixO3, PZT) exhibits excellent piezoelectric properties in the morphotropic phase boundary (MPB) region of its temperature-composition phase diagram. However, the microscopic origin of its high piezoelectric response remains controversial. Here, we develop a machine-learning-based deep potential (DP) model of PZT using the training dataset from first principles density functional theory calculations. Based on DP-assisted large-scale atomic simulations, we reproduce the temperature-composition phase diagram of PZT, in good agreement with the experiment except the absence of structural transition from R3c to R3m. We find that the rhombohedral phase maintains R3c symmetry with slight oxygen octahedral tilting as increase of temperature, instead of appearing R3m symmetry. This discrepancy can trace back to the lack of experimental measurements to identify such slight octahedral tilting. More importantly, we clarify the atomic-level feature of PZT at the MPB, exhibiting the competing coupling of ferroelectric nanodomains with various polarization orientations. The high piezoelectric response is driven by polarization rotation of nanodomains induced by an external electric field. Piezoelectricity ; Phase boundary ; Phase diagram ; Ferroelectricity ; Lead zirconate titanate ; Materials science ; Condensed matter physics ; Polarization (electrochemistry) ; Phase transition ; Piezoelectric coefficient ; Phase (matter) ; Induced polarization ; Electric field ; Symmetry (geometry) ; Octahedron ; Dielectric ; Physics ; Crystal structure ; Chemistry ; Crystallography ; Electrical resistivity and conductivity ; Composite material ; Geometry ; Mathematics ; Optoelectronics Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials
Authors: Nihad Omer Hassan | Gasim Ibrahim | Dhallia Mamoun Beshir | Nimir O. Elbashir
Source: Processes (article, 2023-02-07)
Field: Chemistry — Analytical Chemistry

A Techno-Economic Study of Catalytic Decarboxylation Process for Naphthenic Acids Utilizing Protonic Zeolite Socony Mobil Type 5 (HZSM-5) Catalyst This paper represents a detailed techno-economic analysis of a typical commercial-scale catalytic decarboxylation process of naphthenic acids over HZSM-5 zeolite. Simulation of the process has been performed in ASPEN Plus®. The performance of the modeled unit was compared to experimental results data from a similar plant. Two models were developed for the proposed industrial plant based on continuous flow reactors; the first is based on a fluidized bed reactor, and it was modeled as a continuous stirred tank reactor (CSTR) unit, and the second is a semi-regenerative process that consists of three fixed-bed reactors with intermediate preheaters and are modeled as three plug flow reactors (PFR). The outcome of the economic analysis of the two proposed commercial scale reactors of a decarboxylation process of a capacity of 11,000 bbl/day showed that the CAPEX, including the total equipment cost for the fluidized bed reactor plant and semi-regenerative process plant, was $44,319,362 and $4,447,919, respectively. The annual operating cost for the fluidized bed plant and semi-regenerative process plant is 45,269,180 $/year and 1,771,839 $/year, respectively. Our results demonstrated that catalytic decarboxylation over HZSM-5 zeolite is economically feasible using a semi-regenerative process, and is a promising method for removing naphthenic acid. The insight obtained from this work can be used as a basis for more comprehensive future financial and risk modeling of the process. The cost estimated in this work was compared to the Khartoum refinery cost for the naphthenic acid corrosion mitigation system, with a saving of $29,459,528. Pilot plant ; Fluidized bed ; Refinery ; Continuous stirred-tank reactor ; Waste management ; Naphtha ; Process engineering ; Decarboxylation ; Plug flow reactor model ; Process (computing) ; Catalysis ; Environmental science ; Chemistry ; Chemical engineering ; Engineering ; Computer science ; Organic chemistry Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Process Optimization and Integration
Authors: Ibrahim Abdulwahhab Atiyah | Ismail Ibrahim Marhoon | Raed K. Mohammed Jawad
Source: Journal of the Mechanical Behavior of Materials (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

An atomistic study on the strain rate and temperature dependences of the plastic deformation Cu–Au core–shell nanowires: On the role of dislocations Abstract Recently, Cu–Au core–shell nanowires have been extensively used as conductors, nanocatalysts, and aerospace instruments due to their excellent thermal and electrical conductivity. In experimental studies, various methods have been presented for producing, characterizing, and strengthening these structures. However, the mechanical behavior and plastic deformation mechanisms of these materials have not been investigated at the atomic scale. Consequently, in the present study, we carried out uniaxial tensile tests on Cu–Au nanowires at various tension rates and temperatures by means of the molecular dynamics approach. The Cu–Au interface was found to be the main site for nucleation of perfect dislocations, Shockley partials, and stacking faults due to the stress concentration and high potential energy arising from the atomic mismatch between shell and core layers. It was observed that an increase in the strain rate from 108 to 1,011 s −1 shortened the time required for the nucleation of dislocations, decreasing the dislocation density. This emphasizes that dislocation nucleation and slip mechanisms are time-dependent. Moreover, it was found that the interaction of Shockley partials can lead to the creation of lock dislocations, such as Hirth, Frank, and Stair-rod dislocations, imposing obstacles for the slip of other dislocations. However, as the tension temperature rose from 300 to 600 K, opposite-sign dislocations removed each other due to thermally activated mechanisms such as dislocation climb and dislocation recovery. Furthermore, the combination of Shockley partial dislocations decreased the stacking fault density, facilitating the plastic deformation of these structures. The yield strength and elastic modulus of the samples increased with the strain rate and substantially decreased as the temperature rose. Materials science ; Nucleation ; Partial dislocations ; Dislocation ; Nanowire ; Condensed matter physics ; Stacking-fault energy ; Slip (aerodynamics) ; Dislocation creep ; Composite material ; Deformation (meteorology) ; Crystallography ; Nanotechnology ; Thermodynamics Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Advanced materials and composites
Authors: Kianoosh Sattari | Dawei Li | Yunchao Xie | Olexandr Isayev | Jian Lin
Source: ChemRxiv (preprint, 2023-04-07)
Field: Materials Science — Materials Chemistry

De Novo Design of Molecules Towards Biased Properties via a Deep Generative Framework and Iterative Transfer Learning De Novo design of molecules with targeted properties represents a new frontier in molecule development. Despite enormous progress, two main challenges remain, i.e., (i) generation of novel molecules with targeted and quantifiable properties; (ii) generated molecules having property values beyond the range in the training dataset. To tackle these challenges, we propose a novel reinforced regressional and conditional generative adversarial network (RRCGAN) to generate chemically valid, small molecules with targeted heat capacity (Cv) values as a proof-of-concept study. As validated by DFT, ~80% of the generated samples have a relative error (RE) of < 20% of the targeted Cv values. To bias the generation of molecules with the Cv values beyond the range of the original training molecules, transfer learning was applied to iteratively retrain the RRCGAN model. After only two iterations of transfer learning, the mean Cv of the generated molecules increases to 44.0 cal/(mol·K) from the mean value of 31.6 cal/(mol·K) shown in the initial training dataset. This demonstrated computation methodology paves a new avenue to discovering small molecules with biased properties. Molecule ; Generative grammar ; Range (aeronautics) ; Computation ; Transfer of learning ; Computer science ; Small molecule ; Artificial intelligence ; Mathematics ; Algorithm ; Machine learning ; Physics ; Chemistry ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Lijin Rajan | Madari Palliyalil Sidheekha | Aranhikundan Shabeeba | Ayana Rajan | Thamburan Vathukkal Krishnendu | Yahya A. Ismail
Source: Research Square (preprint, 2023-11-30)
Field: Materials Science — Polymers and Plastics

Bio-mimetic sensing characteristics of differently synthesized polyanilines towards the surrounding electrical and chemical energetic conditions Abstract Conducting polymers are recognized as responsive gels capable of responding to the changes in their surrounding environment through their unique electrochemical response. Various polyanilines at different reaction time were synthesized chemically and their properties were examined using TGA, UV-VIS spectroscopy, FTIR spectroscopy, cyclic voltammetry (CV) and coulovoltammetry (QV). To investigate their electrochemical sensing capabilities towards both electrical and chemical stimuli, the chronopotentiometric responses in HCl solutions were monitored by varying the working variables: the applied current and electrolyte concentration, at a constant charge obtained from respective QV. The consumed electrical energy during the electrochemical reaction was observed to change linearly with the driving current, while a logarithmic relationship was established with the electrolyte concentration. The electrical energy served as the sensing parameter, and the sensitivity was found to be associated with the reaction time during synthesis of the polymers, with longer chains exhibiting greater sensitivity. The experimental findings were validated using a theoretical equation. Applicability of polyaniline to act as a model material for designing bio-mimetic sensing devices using only two connecting wires is verified here as they mimic the electrochemical reactions of biological muscles comprising of natural polymeric chain. Polyaniline ; Electrochemistry ; Cyclic voltammetry ; Electrolyte ; Materials science ; Fourier transform infrared spectroscopy ; Polymer ; Electric potential energy ; Conductive polymer ; Chemical engineering ; Electrode ; Analytical Chemistry (journal) ; Chemistry ; Organic chemistry ; Physical chemistry ; Energy (signal processing) ; Composite material Conducting polymers and applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Shubhajit Das | Rubén Laplaza | J. Terence Blaskovits | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2023-03-23)
Field: Chemistry — Inorganic Chemistry

Engineering Frustrated Lewis Pair Active Sites in Porous Organic Scaffolds for Catalytic CO2 Hydrogenation Frustrated Lewis pairs (FLPs), featuring reactive combinations of Lewis acids and Lewis bases, have been utilized for myriad homogeneous catalytic processes. Immobilizing the active Lewis sites to a solid support, especially to porous scaffolds, has shown great potential to ameliorate FLP catalysis by circumventing some of its inherent drawbacks, such as product separation and catalyst recyclability. Nevertheless, designing immobilized Lewis pair active sites (LPASs) is challenging due to the requirement of placing the donor and acceptor centers in appropriate geometric arrangements while maintaining the necessary chemical environment to perform catalysis, and clear design rules have not yet been established. In this work, we formulate simple guidelines to build highly active LPASs for direct catalytic hydrogenation of CO2 through a large-scale screening of a diverse library of 25,000 immobilized FLPs. The library is built by introducing boron-containing acidic sites in the vicinity of the existing basic nitrogen sites of the organic linkers of metal-organic frameworks collected in a ``top-down" fashion from an experimental database. The chemical and geometrical appropriateness of these LPASs for CO2 hydrogenation is determined by evaluating a series of simple descriptors representing the intrinsic strength (acidity and basicity) of the components and their spatial arrangement in the active sites. Analysis of the leading candidates enables the formulation of pragmatic and experimentally-relevant design principles and the leading candidates constitute the starting point for further exploration of FLP-based catalysts for the reduction of CO2. Frustrated Lewis pair ; Catalysis ; Lewis acids and bases ; Chemistry ; Homogeneous ; Active site ; Combinatorial chemistry ; Porosity ; Nanotechnology ; Organic chemistry ; Materials science ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Chuhong Wang | Muratahan Aykol | Tim Mueller
Source: ChemRxiv (preprint, 2023-04-24)
Field: Materials Science — Materials Chemistry

Nature of the amorphous-amorphous interfaces in solid-state batteries revealed using machine-learned interatomic potentials Non-crystalline solid materials have attracted growing attention in energy storage for their desirable properties such as ionic conductivity, stability and processability. However, compared to bulk crystalline materials, fundamental understanding of these highly complex metastable systems is hindered by the scale limitations of density functional theory (DFT) calculations and resolution limitations of experimental methods. To fill the knowledge gap and guide the rational design of amorphous battery materials and interfaces, we present a molecular dynamics (MD) framework based on machine-learned interatomic potentials trained on the fly to study the amorphous solid electrolyte Li3PS4 and its protective coating, amorphous Li3B11O18. The use of machine-learned potentials allows us to simulate the materials at time and length scales that are not accessible to DFT while maintaining a near-DFT level of accuracy. This approach allows us to calculate amorphization energies, amorphous-amorphous interface energies, and the impact of the interface on lithium ion conductivity. This study demonstrates the promising role of actively-learned interatomic potentials in extending the application of ab-initio modeling to more complex and realistic systems such as amorphous materials and interfaces. Amorphous solid ; Materials science ; Metastability ; Interatomic potential ; Density functional theory ; Molecular dynamics ; Ab initio ; Ionic bonding ; Chemical physics ; Ionic conductivity ; Conductivity ; Amorphous carbon ; Electrolyte ; Nanotechnology ; Ion ; Computational chemistry ; Physical chemistry ; Chemistry ; Crystallography Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: Sirwan Ahmad | Mohammad Kareem
Source: Journal of Zankoy Sulaimani - Part A (article, 2023-12-20)
Field: Chemistry — Physical and Theoretical Chemistry

Kinetics, thermodynamics, and DFT studies of phenols oxidative coupling reactions with 4-amino-N, N-dimethylaniline A spectrophotometric method has been established for the reaction kinetics of phenol oxidative coupling with 4-amino-N, N-dimethylaniline in the presence of potassium dichromate as an oxidant to form C14H14N2O, known as phenol blue. Optimum conditions for the reaction were investigated: maximum wavelength (λ max), linear range, molar absorptivity, and stability constant. The kinetic models were applied to the reaction, indicating that it is a first-order reaction. The activation energy (Ea) and Arrhenius constant (A) were calculated from the Arrhenius equation as 14.89 kJ·mol−1 and 3.75 × 105 s-1 respectively. Thermodynamic parameters were ΔH* 12.37 Kj·mol−1, ΔG* 5.47 kJ·mol−1 and ΔS* 22.77 J·mol−1·K−1 at 30oC.. Results indicate that the formation of the product phenol blue is non-spontaneous and endothermic in nature. Density function theory (DFT) has been done for the comparison between experimental and theoretical results using the common 6-311G(d,p) basis set. The results are in good agreement with each other, proving the reliability of the method. Finally, some preliminary test has been performed to check possibility of the product as dye. Dimethylaniline ; Kinetics ; Oxidative coupling of methane ; Chemistry ; Phenols ; Oxidative phosphorylation ; Thermodynamics ; Computational chemistry ; Organic chemistry ; Catalysis ; Physics ; Biochemistry Photochemistry and Electron Transfer Studies ; Chemical Reaction Mechanisms ; Free Radicals and Antioxidants