jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kangming Li | Edward Kim | Yao Fehlis | Daniel Persaud | Brian DeCost | Michael T. Greenwood | Jason Hattrick‐Simpers
Source: Matter (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

A call for caution in the era of AI-accelerated materials science Computer science ; Artificial intelligence ; Overhead (engineering) ; Field (mathematics) ; Machine learning ; State (computer science) ; Artificial neural network ; Applications of artificial intelligence ; Algorithm Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electronic and Structural Properties of Oxides
Authors: Yu-Chuan Gu | Lu-Sheng Wang | Xia Huang | Kun Song | Shiqing Lu | Jun Ding
Source: Archive of Applied Mechanics (article, 2023-05-26)
Field: Materials Science — Materials Chemistry

Data-driven constitutive model of GH4169 alloy within a synergistic high strain rate and elevated temperature Constitutive equation ; Materials science ; Strain rate ; Alloy ; Split-Hopkinson pressure bar ; Deformation (meteorology) ; Hardening (computing) ; Softening ; Composite material ; Structural engineering ; Metallurgy ; Finite element method ; Engineering High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming ; Metal and Thin Film Mechanics
Authors: André Felipe Vale Fonseca | Cintia Ellen Giarola | Thaís Adriany de Souza Carvalho | Fernanda Sumika Hojo de Souza | Marco Antônio Schiavon
Source: The Journal of Chemical Physics (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Machine learning predicted emission of water-stable CdTe quantum dots Quantum dots (QDs) have attracted much attention and exhibit many attractive properties, including high absorption coefficient, adjustable bandgap, high brightness, long-term stability, and size-dependent emission. It is known that to obtain high-quality luminescent properties (i.e. emission color, color purity, quantum yield, and stability), the synthesis parameters must be precisely controlled. In this work, we have constructed a database with CdTe aqueous synthesis parameters and spectroscopic results and applied machine learning algorithms to better understand the influence of the main synthesis parameters of CdTe QDs on their final emission properties. A strong dependence of the final emission wavelength with the reaction time and surface ligands and precursors concentrations was demonstrated. These parameters adjusted synchronously were shown to be very useful for provide ideal synthesis conditions for the preparation of CdTe QDs with desirable emission wavelengths. Moreover, applying the algorithms correctly allows for obtaining information and insights into the growth kinetics of QDs under different synthetic conditions. Quantum dot ; Cadmium telluride photovoltaics ; Quantum yield ; Materials science ; Absorption (acoustics) ; Stability (learning theory) ; Aqueous solution ; Wavelength ; Brightness ; Luminescence ; Optoelectronics ; Nanotechnology ; Chemistry ; Computer science ; Physics ; Physical chemistry ; Optics ; Fluorescence ; Machine learning Quantum Dots Synthesis And Properties ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Martin Amezcua | Jeffry Setiadi | David L. Mobley
Source: ChemRxiv (preprint, 2023-11-02)
Field: Materials Science — Materials Chemistry

The SAMPL9 Host-Guest Blind Challenge: An Overview of Binding Free Energy Predictive Accuracy We report the results of the SAMPL9 host-guest blind challenge for predicting binding free energies. The challenge focused on macrocycles from pillar[n]-arene and cyclodextrin host families, including WP6, and bCD and HbCD. A variety of methods were used by participants to submit binding free energy predictions. A machine learning approach based on molecular descriptors achieved the highest accuracy (RMSE of 2.04 kcal/mol) among ranked methods in the WP6 dataset. Interestingly, predictions for WP6 obtained via docking tended to outperform all methods (RMSE of 1.70 kcal/mol), most of which are MD based and computationally more expensive. In general, methods applying force fields achieved better correlation with experiments for WP6 opposed to the machine learning and docking models. In the cyclodextrin-phenothiazine challenge, the ATM approach emerged as the top performing method with RMSE less than 1.86 kcal/mol. Correlation metrics of ranked methods in this dataset was relatively poor compared to WP6. We also highlight several lessons learned to guide future work and help improve studies on the systems discussed. For example, WP6 may be present in other microstates other than its -12 state in the presence of certain guests. Machine learning approaches can be used to fine tune or help train force fields for certain chemistry (i.e WP6-G4). Certain phenothiazines occupy distinct primary and secondary orientations, some of which were considered individually for accurate binding free energies. The accuracy of predictions from certain methods while starting from a single binding pose/orientation demonstrate the sensitivity of calculated binding free energies to the orientation, and in some cases the likely dominant orientation for the system. Computational and experimental results suggest that guests phenothiazine core traverses both secondary and primary faces of the cyclodextrin hosts, bulky catioinic side chain will primarily occupy the primary face, and the phenothiazine core substituent resides at the larger secondary face. Mean squared error ; Machine learning ; Artificial intelligence ; Pillar ; Docking (animal) ; Computer science ; Binding energy ; Chemistry ; Computational chemistry ; Biological system ; Mathematics ; Statistics ; Engineering ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Siyan Deng | Chao Chen | Ke Li | Xi Chen | Kelin Xia | Shuzhou Li
Source: The Journal of Physical Chemistry B (article, 2023-11-09)
Field: Materials Science — Materials Chemistry

Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers To discover new materials, high-throughput screening (HTS) with machine learning (ML) requires universally available descriptors that can accurately predict the desired properties. For elastomers, experimental and simulation data in current descriptors may not be available for all candidates of interest, hindering elastomer discovery through HTS. To address this challenge, we introduce structure-based multilevel (SM) descriptors of elastomers derived solely from molecular structure that is universally available. Our SM descriptors are hierarchically organized to capture both local soft and hard segment structures as well as the global structures of elastomers. With the SM-Morgan Fingerprint (SM-MF) descriptor, one of our SM descriptors, a machine learning model accurately predicts elastomer toughness with a remarkable accuracy of 0.91. Furthermore, an HTS pipeline is established to swiftly screen elastomers with targeted toughness. We also demonstrate the generality and applicability of SM descriptors by using them to construct HTS pipelines for screening elastomers with a targeted critical strain or Young's modulus. The user-friendliness and low computational cost of SM descriptors make them a promising tool to significantly enhance HTS in the search for novel materials. Elastomer ; Pipeline (software) ; Computer science ; Artificial intelligence ; Fingerprint (computing) ; Throughput ; Toughness ; Materials science ; Pattern recognition (psychology) ; Machine learning ; Composite material Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Polymer Synthesis and Characterization
Authors: Yuanhao Wang | Jia Zhang | Guangren Qian | Tong‐Yi Zhang
Source: Materials Letters (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

Data-driven discovery of low temperature denitration catalysts with strong anti-sulfur and anti-water property Catalysis ; Bayesian optimization ; Sulfur ; NOx ; Materials science ; Work (physics) ; Function (biology) ; Kriging ; Gaussian ; Chemical engineering ; Computer science ; Chemistry ; Thermodynamics ; Physical chemistry ; Machine learning ; Organic chemistry ; Metallurgy ; Computational chemistry ; Physics ; Engineering Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Advanced Photocatalysis Techniques
Authors: Mausumi Sarma | Sourav Chakraborty | Radhakrishnan Kesavan | Kshirod Kumar Dash | Prakash Kumar Nayak
Source: Starch - Stärke (article, 2023-10-25)
Field: Materials Science — Biomaterials

Starch‐Based Biodegradable Film from Fruit and Vegetable Waste and Its Standardization Modules Based on Neural Networks and Response Surface Methodology Abstract Fruits and vegetable waste‐based starch has numerous applications for use as a biodegradable film in food packaging materials. This study reviews fruit and vegetable waste‐based non‐commercial starches that can be utilized as an alternatives for commercial starches in biodegradable film growth. Circular economy, sustainable manufacturing goals, recycling waste and by‐products, and new basic concepts drive the hunt for alternative starch sources. Starches from unusual and abandoned fruits and vegetables offer stronger research potential. The characteristics of starch extracted from these sources and their use as a biodegradable film are emerging trends in the field of packaging technology. Further, millet starch, for example, is made from the waste of underused crops or other fruits and vegetables and presents a wealth of new avenues for biodegradable film study. In order to cease throwing away valuable carbohydrates, especially starch, these sources must incorporate into the concept of “circularity” and work toward more sustainable manufacturing practices. Besides, optimizing the biodegradable film composition to improve barrier and shelf life is also crucial. Thus, an additional study may apply response surface‐based hybrid optimization, neural networks, or deep learning‐oriented models to optimize biodegradable film composition and intelligent monitoring of the materials under the packing systems. Starch ; Standardization ; Food packaging ; Food waste ; Shelf life ; Waste management ; Environmental science ; Biochemical engineering ; Engineering ; Food science ; Computer science ; Chemistry ; Mechanical engineering Nanocomposite Films for Food Packaging ; Food composition and properties ; biodegradable polymer synthesis and properties
Authors: Kayode Yomi Raheem | Jairus Olumasai Nandwa
Source: ESMO Open (article, 2023-03-01)
Field: Chemistry — Organic Chemistry

66P Targeting allosteric sites on PDK-1 and PLK-1 with bioactive compounds from Daucus carota as a potential therapy for triple-negative breast cancer Triple-negative breast cancer (TNBC) is a devastating subtype of breast cancer that lacks expression of estrogen receptors, progesterone receptors, and HER2. Targeting TNBC has been challenging due to the lack of specific therapeutic targets. However, recent studies have identified allosteric sites on the enzymes PDK-1 and PLK-1 as potential drug targets for TNBC. New medications for TNBC have considerable adverse effects, highlighting the need for more targeted and effective therapy. In this study, we used computer-aided drug design (CADD) and theoretical chemistry techniques, including molecular docking, MM-GBSA calculation, molecular dynamic (MD) simulation, and a pharmacokinetic study, to identify bioactive compounds from Daucus carota that could bind to these allosteric sites and inhibit the activity of PDK-1 and PLK-1. DFT calculations confirmed the binding affinity and stability of the molecules. Machine-learning predictive models(QSAR) were used to find potential compounds against the two targets. Ten (10) lead compounds from Daucus carota's bioactive compounds are discovered; eight have more potential as PDK-1 and PLK-1 inhibitors than docetaxel and doxorubicin. Astragalin and Scolimoside showed substantial binding affinity and persistent interaction in the allosteric region of the two proteins. These compounds are stable and fluctuate little after 50ns of MD simulation. DFT analysis confirms enhanced bioactivity and chemical reactivity of hit compounds with favourable intramolecular charge transfer between electron-donor and electron-acceptor groups. The predicted QSAR model showed the efficiency of certain compounds as PDK-1 and PLK-1 inhibitors (pIC50). This study provides evidence that targeting PDK-1 and PLK-1 with bioactive compounds from Daucus carota may be an effective method for the treatment of TNBC. Allosteric regulation ; Triple-negative breast cancer ; Chemistry ; Docking (animal) ; Cancer research ; Pharmacology ; Stereochemistry ; Breast cancer ; Biochemistry ; Receptor ; Cancer ; Biology ; Medicine ; Internal medicine Synthesis and biological activity ; Tannin, Tannase and Anticancer Activities ; Computational Drug Discovery Methods
Authors: Udit Kumar | A. Arunachalam | Corbin Feit | S. Novia Berriel | Kanad Basu | Parag Banerjee | Sudipta Seal
Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films (article, 2023-11-02)
Field: Materials Science — Materials Chemistry

Real-time artificial intelligence enhanced defect engineering in CeO2 nanostructures CeO2 nanostructures have been utilized for various biomedical, sensor, and catalysis applications owing to their unique defect structure, enabling them to have regenerative oxidative properties. Defect engineering in CeO2 nanostructures has major importance, enabling them to be utilized for specific applications. Despite various synthesis methods, it is challenging to have precise and reversible control over defect structures. Against this backdrop, in the current work, we have explored machine learning (ML) enhanced defect engineering of CeO2 nanofilms. In our earlier work [J. Vac. Sci. Technol. A 39, 060405 (2021)], we have developed an atomic layer deposition process for CeO2 using in situ ellipsometry measurements. In the current work, data collected through in situ spectroscopic ellipsometry and ex situ XPS have been correlated using two ML algorithms (gradient boost and random forest regressor) to exert better control over the chemical properties. Defect structures are one of the desired properties in CeO2 nanomaterials, characterized by the Ce3+/Ce4+ oxidation state ratio leading to its regenerative properties. We have shown that the defect structure of the CeO2 nanofilms can be predicted using in situ ellipsometry data in real time using a trained ML algorithm using two different methods. The first method involves an indirect approach of thickness prediction using an ML algorithm (k-nearest neighbors) followed by Ce3+/Ce4+ estimation using an experimental calibration curve. The second method with a more direct approach involves Ce3+/Ce4+ prediction using real-time ellipsometry data (amplitude ratio ψ and phase difference Δ) using gradient boost and random forest regressor. Materials science ; Nanostructure ; X-ray photoelectron spectroscopy ; Nanomaterials ; In situ ; Work (physics) ; Atomic layer deposition ; Ellipsometry ; Nanotechnology ; Calibration ; Computer science ; Algorithm ; Thin film ; Chemical engineering ; Chemistry ; Mathematics ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Nikhil Thota | Maitreyee Sharma Priyadarshini | Rigoberto Hernandez
Source: Materials Horizons (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

NestedAE: interpretable nested autoencoders for multi-scale materials characterization We introduce an interpretable machine learning architecture, NestedAE, for multiscale materials using nested supervised autoencoders. We benchmarked the performance of NestedAE on two databases: (1) a synthetic dataset created from nested analytical functions whose dimensionality is therefore known a priori, and (2) a multiscale MHP dataset that is a combination of an open source dataset containing atomic and ionic properties, and a second dataset containing device characterization using current density-voltage (<i>J</i>-<i>V</i>) analysis. The NestedAE architecture was found to have higher noise robustness and lower reconstruction losses when compared to a vanilla autoencoder (AE). Its application on the MHP dataset revealed links between crystal scale properties and device performance in agreement with earlier experimental observations. Characterization (materials science) ; Scale (ratio) ; Materials science ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Nanotechnology ; Geography ; Cartography Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Neural Networks and Applications
Authors: Sandro Wieser | Egbert Zojer
Source: arXiv (Cornell University) (preprint, 2023-08-02)
Field: Materials Science — Materials Chemistry

Machine learned Force-Fields for an ab-initio Quality Description of Metal-Organic Frameworks Metal-organic frameworks (MOFs) are an incredibly diverse group of highly porous hybrid materials, which are interesting for a wide range of possible applications. For a reliable description of many of their properties accurate computationally highly efficient methods, like force-field potentials (FFPs), are required. With the advent of machine learning approaches, it is now possible to generate such potentials with relatively little human effort. Here, we present a recipe to parametrize two fundamentally different types of exceptionally accurate and computationally highly efficient machine learned potentials, which belong to the moment-tensor and kernel-based potential families. They are parametrized relying on reference configurations generated in the course of molecular dynamics based, active learning runs and their performance is benchmarked for a representative selection of commonly studied MOFs. For both potentials, comparison to a random set of validation structures reveals close to DFT precision in predicted forces and structural parameters of all MOFs. Essentially the same applies to elastic constants and phonon band structures. Additionally, for MOF-5 the thermal conductivity is obtained with full quantitative agreement to single-crystal experiments. All this is possible while maintaining a high degree of computational efficiency, with the obtained machine learned potentials being only moderately slower than the extremely simple UFF4MOF or Dreiding force fields. The exceptional accuracy of the presented FFPs combined with their computational efficiency has the potential of lifting the computational modelling of MOFs to the next level. Force field (fiction) ; Computer science ; Ab initio ; Moment (physics) ; Kernel (algebra) ; Field (mathematics) ; Statistical physics ; Tensor (intrinsic definition) ; Set (abstract data type) ; Simple (philosophy) ; Quality (philosophy) ; Artificial intelligence ; Machine learning ; Algorithm ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions
Authors: Surbhi | Deeya Shalya | Sarvendra Kumar
Source: East European Journal of Physics (article, 2023-03-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecular Geometry, Homo-Lumo Analysis and Mulliken Charge Distribution of 2,6-Dichloro-4-Fluoro Phenol Using DFT and HF Method Phenolic compounds are used in human diet, commonly present in plants. Foremost polyphenolic compounds found in plants are flavanols, flavonoids, flavonones, iso-flavones, phenolic acids, flavonoids, chalcones, lignans etc. These compounds possess antimicrobial, antiviral and anti-inflammatory properties along with high antioxidative activity. The antioxidative activity of phenolic compounds depends on their structure. The polyphenols are very useful for the treatment of inflammation, cancer, anti-ageing purposes in cosmetic formulations, and nutraceutical applications. This article focused on substituted phenol, taking into concern their potential health benefits. The recent rise in machine-learning methods has engendered many advances in the molecular sciences. Using desired level of electronic structure theory from density functional theory, we can calculate the properties (electronic structure, force field, energy) of atomistic systems. The full electron density carries with it a considerable computational cost. While the DFT calculation loses accuracy when the molecule is either extended or compressed, Δ-DFT corrects these errors. Here, molecular point group symmetries are used to obtain chemical accuracy. The optimal 2,6-dichloro-4-fluoro phenol molecular geometry was derived using the 6-311+G (d, p) basis set and DFT/B3LYP (density functional theory) and Hartree-Fock (HF) techniques. A detailed interpretation of Homo-Lumo analysis of 2,6-dichloro-4-fluoro phenol is also listed. Using the 6-311+G (d, p) basis set and the Hartree-Fock (HF) method, the Mulliken charge distribution of this molecule has also been computed. Mulliken population analysis ; HOMO/LUMO ; Computational chemistry ; Phenol ; Charge (physics) ; Molecular geometry ; Charge density ; Chemistry ; Materials science ; Molecular physics ; Density functional theory ; Molecule ; Physics ; Organic chemistry ; Quantum mechanics Nonlinear Optical Materials Research ; Chemical Thermodynamics and Molecular Structure ; Free Radicals and Antioxidants
Authors: Erfan Azqadan | Jared Uramowski | Mary A. Wells | Arash Arami | Hamid Jahed
Source: Materials Today Communications (article, 2023-08-01)
Field: Materials Science — Biomaterials

Hardness variation in cast-forging process of AZ80 magnesium alloys and its data-driven prediction Materials science ; Forging ; Microstructure ; Magnesium alloy ; Metallurgy ; Dynamic recrystallization ; Grain size ; Alloy ; Recrystallization (geology) ; Hot working Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: A. C. Arun Raj | Sandipan Roy | Shubhabrata Datta
Source: Computer Methods in Biomechanics & Biomedical Engineering (article, 2023-09-28)
Field: Materials Science — Materials Chemistry

Informatics guided FE design of bioactive titanium alloy/composite multi-layered dental implants A dental implant with three distinct layers, of titanium alloy at core, porous titanium alloy at the intermediate layer and titanium alloy hydroxyapatite composite at the outer layer, is designed to achieve low elastic modulus and adequate strength with bioactive surface. Artificial Neural Network (ANN) along with Rule of Mixture (ROM) is used to generate the objective functions for the Genetic Algorithm (GA) based multi-objective optimization for achieving the optimal designs, which are validated using Finite Element Analysis (FEA) simulations. The composition and processing parameters are correlated with the yield strength and elastic modulus of titanium alloy using ANN. The ANN models are generated to express the strength and effective modulus of the implant using ROM. To determine the optimal composition of titanium alloys, porous layers, and composite layers for a three-layer dental implant, multi-objective genetic algorithm is employed. The Pareto optimal solutions provide the guidelines for designing the implant. A few selected non-dominated solutions are used for studying the actual stress distribution at the bone-implant interface using FEA, and showed significant improvements compared to conventional implants. Materials science ; Titanium alloy ; Finite element method ; Titanium ; Elastic modulus ; Modulus ; Composite material ; Dental implant ; Composite number ; Alloy ; Implant ; Layer (electronics) ; Porosity ; Biomedical engineering ; Structural engineering ; Metallurgy ; Engineering ; Surgery Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Bone Tissue Engineering Materials