jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seung‐Hyun Victor Oh | Woohyun Hwang | Kwangrae Kim | Ji‐Hwan Lee | Aloysius Soon
Source: Advanced Science (article, 2022-03-06)
Field: Materials Science — Materials Chemistry

Using Feature‐Assisted Machine Learning Algorithms to Boost Polarity in Lead‐Free Multicomponent Niobate Alloys for High‐Performance Ferroelectrics To expand the unchartered materials space of lead-free ferroelectrics for smart digital technologies, tuning their compositional complexity via multicomponent alloying allows access to enhanced polar properties. The role of isovalent A-site in binary potassium niobate alloys, (K,A)NbO<sub>3</sub> using first-principles calculations is investigated. Specifically, various alloy compositions of (K,A)NbO<sub>3</sub> are considered and their mixing thermodynamics and associated polar properties are examined. To establish structure-property design rules for high-performance ferroelectrics, the sure independence screening sparsifying operator (SISSO) method is employed to extract key features to explain the A-site driven polarization in (K,A)NbO<sub>3</sub> . Using a new metric of agreement via feature-assisted regression and classification, the SISSO model is further extended to predict A-site driven polarization in multicomponent systems as a function of alloy composition, reducing the prediction errors to less than 1%. With the machine learning model outlined in this work, a polarity-composition map is established to aid the development of new multicomponent lead-free polar oxides which can offer up to 25% boosting in A-site driven polarization and achieving more than 150% of the total polarization in pristine KNbO<sub>3</sub> . This study offers a design-based rational route to develop lead-free multicomponent ferroelectric oxides for niche information technologies. Feature (linguistics) ; Polarity (international relations) ; Lead (geology) ; Algorithm ; Materials science ; Computer science ; Artificial intelligence ; Geology ; Chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials
Authors: Gareth John Moore | Olivier Bardagot | Natalie Banerji
Source: Advanced Theory and Simulations (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

Deep Transfer Learning: A Fast and Accurate Tool to Predict the Energy Levels of Donor Molecules for Organic Photovoltaics Abstract Molecular engineering is driving the recent efficiency leaps in organic photovoltaics (OPVs). A presynthetic determination of frontier energy levels makes the screening of potential molecules more efficient, exhaustive, and cost‐effective. Here, a convolutional neural network is developed to predict the highest occupied and lowest unoccupied molecular orbital (HOMO/LUMO) levels of donor molecules for OPV. The model takes a 2D structure image and returns a prediction of its HOMO/LUMO levels comparable to experimental values. Insufficient experimental datasets are overcome with transfer learning where the model is initially trained on the large Harvard Clean Energy Project dataset and then fine‐tuned using experimental data from the Harvard Organic Photovoltaic dataset. Error margins on predicted HOMO/LUMO levels below 200 meV are achieved, without any chemical knowledge implemented. Noticeably, the model outputs have higher accuracy and precision than corresponding density functional theory (DFT) estimations. The model and its limitations are further tested on a home‐built dataset of commercially available donor polymers reported in OPVs (e.g., P3HT, PTB7‐Th, PM6, D18). The results demonstrate both the practical utility of this model, to foster rational molecular engineering for OPV optimization, and the potential for deep learning techniques, in general, to revolutionize the energy materials research and development sector. Organic solar cell ; HOMO/LUMO ; Deep learning ; Photovoltaic system ; Photovoltaics ; Transfer of learning ; Convolutional neural network ; Computer science ; Density functional theory ; Artificial intelligence ; Molecule ; Materials science ; Biological system ; Nanotechnology ; Chemistry ; Computational chemistry ; Electrical engineering ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Nina Andrejevic | Jovana Andrejevic | B. Andrei Bernevig | Nicolas Regnault | Fei Han | G. Fabbris | Thanh Nguyen | Nathan C. Drucker | Chris H. Rycroft | Mingda Li
Source: Advanced Materials (article, 2022-10-04)
Field: Materials Science — Materials Chemistry

Machine‐Learning Spectral Indicators of Topology Topological materials discovery has emerged as an important frontier in condensed matter physics. While theoretical classification frameworks have been used to identify thousands of candidate topological materials, experimental determination of materials' topology often poses significant technical challenges. X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique sensitive to atoms' local symmetry and chemical bonding, which are intimately linked to band topology by the theory of topological quantum chemistry (TQC). Moreover, as a local structural probe, XAS is known to have high quantitative agreement between experiment and calculation, suggesting that insights from computational spectra can effectively inform experiments. In this work, computed X-ray absorption near-edge structure (XANES) spectra of more than 10 000 inorganic materials to train a neural network (NN) classifier that predicts topological class directly from XANES signatures, achieving F<sub>1</sub> scores of 89% and 93% for topological and trivial classes, respectively is leveraged. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine-learning-augmented XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials, and may further inform field-driven phenomena in situ, such as magnetic field-driven topological phase transitions. X-ray absorption spectroscopy ; Topology (electrical circuits) ; XANES ; Materials science ; Amorphous solid ; Absorption spectroscopy ; Spectroscopy ; Nanotechnology ; Physics ; Chemistry ; Optics ; Crystallography ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: Linhong Deng | Changhao Wang | Jinru Luo | Jian Tu | Ning Guo | Haiyan Xu | Pei He | Shengquan Xia | Zhongwen Yao
Source: Materials Characterization (article, 2022-04-12)
Field: Materials Science — Materials Chemistry

Preparation and property optimization of FeCrAl-based ODS alloy by machine learning combined with wedge-shaped hot-rolling Materials science ; Equiaxed crystals ; Hot isostatic pressing ; Ultimate tensile strength ; Microstructure ; Ferrite (magnet) ; Metallurgy ; Carbide ; Alloy ; Composite material ; Composite number ; Oxide Fusion materials and technologies ; Metal and Thin Film Mechanics ; Microstructure and mechanical properties
Authors: Ruihao Yuan | Zhen Liu | Yangyang Xu | Ruowei Yin | Jingjin He | Yang Bai | Yumei Zhou | Jinshan Li | Dezhen Xue | Turab Lookman
Source: Acta Materialia (article, 2022-05-26)
Field: Materials Science — Materials Chemistry

Optimizing Electrocaloric Effect in Barium Titanate-based Room Temperature Ferroelectrics: Combining Landau Theory, Machine Learning and Synthesis Materials science ; Electrocaloric effect ; Landau theory ; Barium titanate ; Polarization (electrochemistry) ; Nonlinear system ; Ferroelectricity ; Machine learning ; Statistical physics ; Artificial intelligence ; Algorithm ; Condensed matter physics ; Computer science ; Physics ; Optoelectronics ; Dielectric ; Physical chemistry ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Mai Said | Ayman Wahba | Diaa Khalil
Source: Chemometrics and Intelligent Laboratory Systems (article, 2022-07-20)
Field: Chemistry — Analytical Chemistry

Semi-supervised deep learning framework for milk analysis using NIR spectrometers Computer science ; Artificial intelligence ; Spectrometer ; Pattern recognition (psychology) ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Feng Wang | Jun Cheng
Source: The Journal of Chemical Physics (article, 2022-06-22)
Field: Materials Science — Materials Chemistry

Automated workflow for computation of redox potentials, acidity constants, and solvation free energies accelerated by machine learning Fast evolution of modern society stimulates intense development of new materials with novel functionalities in energy and environmental applications. Due to rapid progress of computer science, computational design of materials with target properties has recently attracted a lot of interest. Accurate and efficient calculation of fundamental thermodynamic properties, including redox potentials, acidity constants, and solvation free energies, is of great importance for selection and design of desirable materials. Free energy calculation based on ab initio molecular dynamics (AIMD) can predict these properties with high accuracy at complex environments, however, they are being impeded by high computational costs. To address this issue, this work develops an automated scheme that combines iterative training of machine learning potentials (MLPs) and free energy calculation and demonstrates that these thermodynamic properties can be computed by ML accelerated MD with ab initio accuracy and a much longer time scale at cheaper costs, improving poor statistics and convergence of numerical integration by AIMD. Our automated scheme lays the foundation for computational chemistry-assisted materials design. Solvation ; Computer science ; Workflow ; Ab initio ; Convergence (economics) ; Thermodynamic integration ; Computational science ; Computation ; Chemistry ; Statistical physics ; Molecular dynamics ; Computational chemistry ; Algorithm ; Physics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Dylan Bayerl | Christopher M. Andolina | Shyam Dwaraknath | Wissam A. Saidi
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Convergence acceleration in machine learning potentials for atomistic simulations Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory simulations without appreciably sacrificing accuracy of material property prediction. Speedup ; Convergence (economics) ; Acceleration ; Property (philosophy) ; Computer science ; Statistical physics ; Molecular dynamics ; Density functional theory ; Stability (learning theory) ; Algorithm ; Artificial intelligence ; Machine learning ; Computational chemistry ; Physics ; Parallel computing ; Classical mechanics ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Yasin Shokrollahi | Pengfei Dong | Peshala Thibbotuwawa Gamage | Nashaita Y. Patrawalla | Vipuil Kishore | Hozhabr Mozafari | Linxia Gu
Source: Applied Sciences (article, 2022-10-26)
Field: Materials Science — Biomaterials

Finite Element-Based Machine Learning Model for Predicting the Mechanical Properties of Composite Hydrogels In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. The BG particles have diameters ranging from 0.5 µm to 1.5 µm and a volume fraction from 17% to 59%. FE simulations of tensile testing were performed for calculating the Young’s modulus and Poisson’s ratio of 2000 microstructures. The microstructural images and the calculated Young’s modulus and Poisson’s ratio by FE simulation were used for training and testing a convolutional neural network regression model. Results showed that the network developed in this work can effectively predict the mechanical properties of the composite hydrogels. The R-squared values were 95% and 83% for Young’s modulus and Poisson’s ratio, respectively. This work provides a surrogate model of finite element analysis to predict mechanical properties of BG-COL hydrogel using microstructure images, which could be further utilized for characterizing heterogeneous materials in big data-driven material designs. Materials science ; Composite number ; Finite element method ; Composite material ; Self-healing hydrogels ; Modulus ; Poisson's ratio ; Ultimate tensile strength ; Microstructure ; Young's modulus ; Elastic modulus ; Convolutional neural network ; Poisson distribution ; Machine learning ; Structural engineering ; Computer science ; Mathematics ; Engineering ; Statistics ; Polymer chemistry Collagen: Extraction and Characterization ; Advanced machining processes and optimization ; Infrared Thermography in Medicine
Authors: Taiwo W. Quadri | Lukman O. Olasunkanmi | Omolola E. Fayemi | Hassane Lgaz | Omar Dagdag | El‐Sayed M. Sherif | Ekemini D. Akpan | Han‐Seung Lee | Eno E. Ebenso
Source: Journal of Molecular Modeling (article, 2022-08-11)
Field: Materials Science — Materials Chemistry

Predicting protection capacities of pyrimidine-based corrosion inhibitors for mild steel/HCl interface using linear and nonlinear QSPR models Quantitative structure–activity relationship ; Artificial neural network ; Corrosion ; Biological system ; Molecular descriptor ; Pyrimidine ; Linear regression ; Nonlinear system ; Nonlinear regression ; Perceptron ; Computer science ; Chemistry ; Regression analysis ; Artificial intelligence ; Machine learning ; Stereochemistry ; Organic chemistry Corrosion Behavior and Inhibition ; Multicomponent Synthesis of Heterocycles ; Computational Drug Discovery Methods
Authors: Yuxiang Wang | Alper Kızıltaş | P. Blanchard | Tiffany R. Walsh
Source: Journal of Chemical Information and Modeling (article, 2022-05-16)
Field: Materials Science — Surfaces, Coatings and Films

ContactAngleCalculator: An Automated, Parametrized, and Flexible Code for Contact Angle Estimation in Visual Molecular Dynamics Accurate, fast, and flexible approaches for contact angle estimation in molecular dynamics simulations are of great importance for characterization of surface wettability, especially for machine learning approaches which would usually require thousands of computational contact angle evaluations for training and prediction purposes. However, evaluation of the contact angle from molecular simulations is typically a human-intensive process, which hinders the required fast throughput. To address this challenge, here a flexible and automated contact angle estimation tool, ContactAngleCalculator, is developed to meet these new requirements. In contrast to the current widely used computational approaches that are laborious and human intensive, this code is based on the concepts of the coarse-graining technique and equivalent contact area and volume of the droplet. Once the parameters are determined for a target liquid, it can automatically estimate the contact angle of different time points of one case or multiple cases by only one click. This tool is targeted for integration with machine learning methods, in which it can substantially streamline and reduce human labor and time in a computational contact angle estimation. Contact angle ; Computer science ; Process (computing) ; Code (set theory) ; Granularity ; Molecular dynamics ; Artificial intelligence ; Biological system ; Materials science ; Physics Surface Modification and Superhydrophobicity ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Xin Zhang | Zhangming Gao | Yinglin Yang | Shaowei Pan | Jianwei Yin | Xiangyang Yu
Source: Journal of Near Infrared Spectroscopy (article, 2022-01-18)
Field: Chemistry — Analytical Chemistry

Rapid identification of the storage age of dried tangerine peel using a hand-held near infrared spectrometer and machine learning Dried tangerine peel is a Chinese medicine with high medicinal value. The storage age is an important indicator of its medicinal value, so it is very significant to accurately identify the storage age of dried tangerine peel. Traditional physical and chemical analysis methods can be used to achieve this goal, but these methods are limited by their operability and convenience. Near infrared (NIR) spectroscopy and machine learning have excellent performance in the rapid detection of food and pharmaceutical samples. This study investigated the novel application of integrating a hand-held NIR spectrometer combined with machine learning to rapidly and accurately identify the storage age of Xinhui dried tangerine peel. Savitzky–Golay convolution smoothing, standard normal variate (SNV), first derivative, and second derivative pretreatments were employed to preprocess spectral data. Principal component analysis (PCA) was used to reduce the spectral data dimensions and obtain the characteristic spectral variables of each sample. Support vector machine (SVM) and k-nearest neighbor were applied to establish the qualitative discriminant models. The SNV-PCA-SVM model discriminant accuracy was 99.60% in the validation set and was 96.50% in the test set, showing excellent generalization performance. The results indicated that the method of using a hand-held NIR spectrometer combined with machine learning could be applied to rapidly identify the storage age of Xinhui dried tangerine peel. This is a promising and economical hand-held NIR spectroscopic method for assuring the dried tangerine peel age on-site. Principal component analysis ; Near-infrared spectroscopy ; Artificial intelligence ; Pattern recognition (psychology) ; Support vector machine ; Chemistry ; Linear discriminant analysis ; Partial least squares regression ; Spectrometer ; Mathematics ; Computer science ; Biological system ; Statistics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Yingquan Hao | Ying‐Chieh Hung | Yusuke Shimoyama
Source: Crystal Growth & Design (article, 2022-10-24)
Field: Chemistry — Physical and Theoretical Chemistry

Investigating Spatial Charge Descriptors for Prediction of Cocrystal Formation Using Machine Learning Algorithms Recently, drug modification via cocrystals has attracted great attention due to its high flexibility for the modulation of drug physicochemical properties. To reduce the cost of screening experiments, machine learning (ML) algorithms have proven to be one of the most effective ways to rapidly screen cocrystal formation. However, the choice of molecular descriptors has a significant impact on its prediction accuracy. In this work, two space-charge descriptors (COSMO-based σ-profile and three-dimensional (3D) spatial charge) are introduced into ML modeling, and two innovative COSMO-SVM and 3D-CNN ML algorithms are developed to screen the cocrystal formation. The two proposed ML models using space-charge descriptors demonstrate superior predictive accuracy for cocrystal formation compared to preproposed ML methods using extended connectivity fingerprints (ECFPs) and graph convolutional networks (GCNs). Considering computational efficiency and accuracy, 3D-CNN would be a very efficient ML model in the cocrystal formation screening task. In addition, the formation of two new cocrystals of norfloxacin was predicted by machine learning, and the cocrystals were obtained experimentally. Cocrystal ; Algorithm ; Computer science ; Machine learning ; Artificial intelligence ; Support vector machine ; Chemistry ; Molecule Crystallography and molecular interactions ; Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Dicho Stratiev | Ivelina Shishkova | Rosen Dinkov | Svetoslav Nenov | Sotir Sotirov | Evdokia Sotirova | Iliyan Kolev | Vitaly Ivanov | Simeon Ribagin | Krassimir Atanassov | Danail D. Stratiev | Dobromir Yordanov | Dimitar Nedanovski
Source: Fuel (article, 2022-09-11)
Field: Chemistry — Analytical Chemistry

Prediction of petroleum viscosity from molecular weight and density Viscosity ; Thermodynamics ; Crude oil ; Chemistry ; Absolute deviation ; Oil viscosity ; Empirical modelling ; Analytical Chemistry (journal) ; Chromatography ; Mathematics ; Statistics ; Petroleum engineering ; Physics ; Computer science Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Hydrocarbon exploration and reservoir analysis
Authors: Rebecca C. Poulos | Zhaoxiang Cai | Phillip J. Robinson | Roger R. Reddel | Qing Zhong
Source: PROTEOMICS (review, 2022-09-10)
Field: Chemistry — Spectroscopy

Opportunities for pharmacoproteomics in biomarker discovery Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape. Biomarker discovery ; Drug discovery ; Proteomics ; Computer science ; Data science ; Computational biology ; Biomarker ; Drug response ; Resource (disambiguation) ; Big data ; Precision medicine ; Field (mathematics) ; Bioinformatics ; Drug ; Data mining ; Biology ; Pharmacology Advanced Proteomics Techniques and Applications ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Petr Lazarenko | Vadim Kovalyuk | P An | S. A. Kozyukhin | Viktor Takáts | Alexander Golikov | Victoria B. Glukhenkaya | Yu. V. Vorobyov | Т. V. Kulevoy | A. I. Prokhodtsov | А. А. Шерченков | Gregory Goltsman
Source: Acta Materialia (article, 2022-05-03)
Field: Materials Science — Materials Chemistry

Low power reconfigurable multilevel nanophotonic devices based on Sn-doped Ge2Sb2Te5 thin films Materials science ; Thin film ; Optoelectronics ; Photonics ; Nanophotonics ; Amorphous solid ; Doping ; Nanotechnology Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Advanced Optical Imaging Technologies