jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simiao Ren | Ashwin M. Mahendra | Omar Khatib | Yang Deng | Willie J. Padilla | Jordan M. Malof
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse deep learning methods and benchmarks for artificial electromagnetic material design In this work we investigate the use of deep inverse models (DIMs) for designing artificial electromagnetic materials (AEMs) - such as metamaterials, photonic crystals, and plasmonics - to achieve some desired scattering properties (<i>e.g.</i>, transmission or reflection spectrum). DIMs are deep neural networks (<i>i.e.</i>, deep learning models) that are specially-designed to solve ill-posed inverse problems. There has recently been tremendous growth in the use of DIMs for solving AEM design problems however there has been little comparison of these approaches to examine their absolute and relative performance capabilities. In this work we compare eight state-of-the-art DIMs on three unique AEM design problems, including two models that are novel to the AEM community. Our results indicate that DIMs can rapidly produce accurate designs to achieve a custom desired scattering on all three problems. Although no single model always performs best, the Neural-Adjoint approach achieves the best overall performance across all problem settings. As a final contribution we show that not all AEM design problems are ill-posed, and in such cases a conventional deep neural network can perform better than DIMs. We recommend that a deep neural network is always employed as a simple baseline approach when addressing AEM design problems. We publish python code for our AEM simulators and our DIMs to enable easy replication of our results, and benchmarking of new DIMs by the AEM community. Computer science ; Deep learning ; Artificial neural network ; Metamaterial ; Python (programming language) ; Artificial intelligence ; Benchmarking ; Inverse ; Computer engineering ; Deep neural networks ; Computational science ; Mathematical optimization ; Optics ; Mathematics ; Physics Metamaterials and Metasurfaces Applications ; Radio Wave Propagation Studies ; Photonic Crystals and Applications
Authors: Kaycee Low | Michelle L. Coote | Ekaterina I. Izgorodina
Source: Journal of Chemical Information and Modeling (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Explainable Solvation Free Energy Prediction Combining Graph Neural Networks with Chemical Intuition The prediction of a molecule's solvation Gibbs free (Δ<i>G</i><sub>solv</sub>) energy in a given solvent is an important task which has traditionally been carried out via quantum chemical continuum methods or force field-based molecular simulations. Machine learning (ML) and graph neural networks in particular have emerged as powerful techniques for elucidating structure-property relationships. This work presents a graph neural network (GNN) for the prediction of Δ<i>G</i><sub>solv</sub> which, in addition to encoding typical atom and bond-level features, incorporates chemically intuitive, solvation-relevant parameters into the featurization process: semiempirical partial atomic charges and solvent dielectric constant. Solute-solvent interactions are included via an interaction map layer which can be visualized to examine solubility-enhancing or -decreasing interactions learnt by the model. On a test set of small organic molecules, our GNN predicts Δ<i>G</i><sub>solv</sub> in water and cyclohexane with an accuracy comparable to polarizable and ab initio generated force field methods [mean absolute error (MAE) = 0.4 and 0.2 kcal mol<sup>-1</sup>, respectively], without the need for any molecular simulation. For the FreeSolv data set of hydration free energies, the test MAE is 0.7 kcal mol<sup>-1</sup>. Interpretability and applicability of the model is highlighted through several examples including rationalizing the increased solubility of modified diaminoanthraquinones in organic solvents. The clear explanations afforded by our GNN allow for easy understanding of the model's predictions, giving the experimental chemist confidence in employing ML models toward more optimized synthetic routes. Solvation ; Chemistry ; Force field (fiction) ; Artificial neural network ; Gibbs free energy ; Quantitative structure–activity relationship ; Test set ; Computational chemistry ; Molecule ; Molecular descriptor ; Cyclohexane ; Molecular dynamics ; Ab initio ; Implicit solvation ; Machine learning ; Computer science ; Biological system ; Thermodynamics ; Artificial intelligence ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Carsten G. Staacke | Simon Wengert | Christian Künkel | Gábor Csányi | Karsten Reuter | Johannes T. Margraf
Source: Machine Learning Science and Technology (article, 2022-02-18)
Field: Materials Science — Materials Chemistry

Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model Abstract State-of-the-art machine learning (ML) interatomic potentials use local representations of atomic environments to ensure linear scaling and size-extensivity. This implies a neglect of long-range interactions, most prominently related to electrostatics. To overcome this limitation, we herein present a ML framework for predicting charge distributions and their interactions termed kernel charge equilibration (kQEq). This model is based on classical charge equilibration (QEq) models expanded with an environment-dependent electronegativity. In contrast to previously reported neural network models with a similar concept, kQEq takes advantage of the linearity of both QEq and Kernel Ridge Regression to obtain a closed-form linear algebra expression for training the models. Furthermore, we avoid the ambiguity of charge partitioning schemes by using dipole moments as reference data. As a first application, we show that kQEq can be used to generate accurate and highly data-efficient models for molecular dipole moments. Dipole ; Kernel (algebra) ; Electronegativity ; Statistical physics ; Charge (physics) ; Chemistry ; Range (aeronautics) ; Computational chemistry ; Machine learning ; Artificial intelligence ; Algorithm ; Computer science ; Physics ; Quantum mechanics ; Mathematics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Junyi Gong | Wanjun Gong | Bo Wu | Haoran Wang | Wei He | Zhongyang Dai | Yangzhong Li | Yong Liu | Zhiming Wang | Xiaojun Tuo | Jacky W. Y. Lam | Zijie Qiu | Zheng Zhao | Ben Zhong Tang
Source: Aggregate (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

ASBase: The universal database for aggregate science Abstract This paper reports the first universal and versatile database on aggregate materials for the field of aggregate science research. At the current stage, the database ( http://119.91.135.188:8080/ ) contains over 1000 entries of organic aggregate material systems (mainly luminescent systems at the current stage) with a unique data structure which is designed particularly for aggregate materials and containing the photophysics and physicochemical properties of the compounds in different statuses of aggregation, including dilute solution form, pristine solid‐state, stable crystalline, and nanoaggregates formed in solvents. The web‐based interface of the database provided functions to index, search, manipulate, fetch and deposit data entries. In addition, a background calculation service optimizes the chemical structure of new entries on different levels of accuracies. The database also provided background API for interactive developments of prediction or regression models based on machine‐learning algorithms. Aggregate (composite) ; Database ; Computer science ; Interface (matter) ; Field (mathematics) ; Fetch ; Information retrieval ; Data mining ; Materials science ; Mathematics ; Nanotechnology ; Geology Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Covalent Organic Framework Applications
Authors: Saulo Martiello Mastelini | Daniel R. Cassar | Edesio Alcobaça | Tiago Botari | André C. P. L. F. de Carvalho | Edgar Dutra Zanotto
Source: Acta Materialia (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

Machine learning unveils composition-property relationships in chalcogenide glasses Chalcogenide ; Materials science ; Thermal expansion ; Polarizability ; Germanium ; Mineralogy ; Machine learning ; Computer science ; Optoelectronics ; Metallurgy ; Chemistry ; Silicon Phase-change materials and chalcogenides
Authors: Moeta Tsukamoto | Shunji Ito | Kensuke Ogawa | Yuto Ashida | Kento Sasaki | Kensuke Kobayashi
Source: Scientific Reports (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Accurate magnetic field imaging using nanodiamond quantum sensors enhanced by machine learning Nanodiamonds can be excellent quantum sensors for local magnetic field measurements. We demonstrate magnetic field imaging with high accuracy of 1.8 [Formula: see text]T combining nanodiamond ensemble (NDE) and machine learning without any physical models. We discover the dependence of the NDE signal on the field direction, suggesting the application of NDE for vector magnetometry and the improvement of the existing model. Our method enhances the NDE performance sufficiently to visualize nano-magnetism and mesoscopic current and expands the applicability of NDE in arbitrarily shaped materials, including living organisms. This accomplishment bridges machine learning to quantum sensing for accurate measurements. Nanodiamond ; Computer science ; Field (mathematics) ; Magnetic field ; Quantum ; Artificial intelligence ; Materials science ; Physics ; Diamond ; Mathematics ; Quantum mechanics Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; Carbon Nanotubes in Composites
Authors: Avan Kumar | Kamal Kishore Pant | Sreedevi Upadhyayula | Hariprasad Kodamana
Source: ACS Omega (article, 2022-12-16)
Field: Materials Science — Materials Chemistry

Multiobjective Bayesian Optimization Framework for the Synthesis of Methanol from Syngas Using Interpretable Gaussian Process Models Methanol production has gained considerable interest on the laboratory and industrial scale as it is a renewable fuel and an excellent hydrogen energy storehouse. The formation of synthesis gas (CO/H<sub>2</sub>) and the conversion of synthesis gas to methanol are the two basic catalytic processes used in methanol production. Machine learning (ML) approaches have recently emerged as powerful tools in reaction informatics. Inspired by these, we employ Gaussian process regression (GPR) to the model conversion of carbon monoxide (CO) and selectivity of the methanol product using data sets obtained from experimental investigations to capture uncertainty in prediction values. The results indicate that the proposed GPR model can accurately predict CO conversion and methanol selectivity as compared to other ML models. Further, the factors that influence the predictions are identified from the best GPR model employing "Shapley Additive exPlanations" (SHAP). After interpretation, the essential input features are found to be the inlet mole fraction of CO (Y(CO, in)) and the net inlet flow rate (Fin(nL/min)) for our best prediction GPR models, irrespective of our data sets. These interpretable models are employed for Bayesian optimization in a weighted multiobjective framework to obtain the optimal operating points, namely, maximization of both selectivity and conversion. Syngas ; Kriging ; Methanol ; Computer science ; Maximization ; Chemistry ; Biological system ; Process engineering ; Mathematical optimization ; Machine learning ; Mathematics ; Hydrogen ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Energy, Environment, and Transportation Policies ; Catalytic Processes in Materials Science
Authors: Hyung Keun Park | Yongju Kim | Jaimyun Jung | Hak Hyeon Lee | Jeong Min Park | Kei Ameyama | Hyoung Seop Kim
Source: Acta Materialia (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Efficient design of harmonic structure using an integrated hetero-deformation induced hardening model and machine learning algorithm Materials science ; Microstructure ; Representative elementary volume ; Finite element method ; Deformation (meteorology) ; Ultimate tensile strength ; Composite material ; Algorithm ; Structural engineering ; Computer science Microstructure and mechanical properties ; Metal Forming Simulation Techniques ; Metallurgy and Material Forming
Authors: Christian M. Heil | Anvay Patil | Ali Dhinojwala | Arthi Jayaraman
Source: ACS Central Science (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions We present a new open-source, machine learning (ML) enhanced computational method for experimentalists to quickly analyze high-throughput small-angle scattering results from multicomponent nanoparticle mixtures and solutions at varying compositions and concentrations to obtain reconstructed 3D structures of the sample. This new method is an improvement over our original computational reverse-engineering analysis for scattering experiments (CREASE) method (ACS Materials Au2021, 1 (2 (2), ), 140-156), which takes as input the experimental scattering profiles and outputs a 3D visualization and structural characterization (e.g., real space pair-correlation functions, domain sizes, and extent of mixing in binary nanoparticle mixtures) of the nanoparticle mixtures. The new gene-based CREASE method reduces the computational running time by >95% as compared to the original CREASE and performs better in scenarios where the original CREASE method performed poorly. Furthermore, the ML model linking features of nanoparticle solutions (e.g., concentration, nanoparticles' tendency to aggregate) to a computed scattering profile is generic enough to analyze scattering profiles for nanoparticle solutions at conditions (nanoparticle chemistry and size) beyond those that were used for the ML training. Finally, we demonstrate application of this new gene-based CREASE method for analysis of small-angle X-ray scattering results from a nanoparticle solution with unknown nanoparticle aggregation and small-angle neutron scattering results from a binary nanoparticle assembly with unknown mixing/segregation among the nanoparticles. Nanoparticle ; Scattering ; Materials science ; Binary number ; Small-angle neutron scattering ; Neutron scattering ; Light scattering ; Biological system ; Computer science ; Nanotechnology ; Optics ; Physics ; Mathematics Machine Learning in Materials Science ; Nanoparticle-Based Drug Delivery ; Electrostatics and Colloid Interactions
Authors: Corey Oses | Marco Esters | David Hicks | Simon Divilov | Hagen Eckert | Rico Friedrich | Michael J. Mehl | Andriy Smolyanyuk | Xiomara Campilongo | Axel van de Walle | Jan Schroers | A. Gilad Kusne | Ichiro Takeuchi | Eva Zurek | Marco Buongiorno Nardelli | Marco Fornari | Yoav Lederer | Ohad Levy | Cormac Toher | Stefano Curtarolo
Source: Computational Materials Science (article, 2022-11-16)
Field: Materials Science — Materials Chemistry

aflow++: A C++ framework for autonomous materials design Workflow ; Computer science ; Interoperability ; Software ; Consistency (knowledge bases) ; Key (lock) ; Systems engineering ; Software engineering ; Database ; Engineering ; Artificial intelligence Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; X-ray Diffraction in Crystallography
Authors: Sajad Sabzi | Mohammad Nadimi | Yousef Abbaspour‐Gilandeh | Jitendra Paliwal
Source: International Journal of Fruit Science (article, 2022-07-15)
Field: Chemistry — Analytical Chemistry

Non-Destructive Estimation of Physicochemical Properties and Detection of Ripeness Level of Apples Using Machine Vision Nondestructive estimation of physicochemical properties, post-harvest physiology, and level of ripeness of fruits is essential to their automated harvesting, sorting, and handling. Recent research efforts have identified machine vision systems as a promising noninvasive nondestructive tool for exploring the relationship between physicochemical and appearance characteristics of fruits at various ripening levels. In this regard, the purpose of the current study is to provide an intelligent algorithm for estimating two physical properties including firmness, and soluble solid content (SSC), three chemical properties viz. starch, acidity, and titratable acidity (TA), as well as detection of the ripening level of apples (cultivar Red Delicious) using video processing and artificial intelligence. To this end, videos of apples in orchards at four levels of ripeness were recorded and 444 color and texture features were extracted from these samples. Five physicochemical properties including firmness, SSC, starch, acidity, and TA were measured. Using the hybrid artificial neural network-difference evolution (ANN-DE), six most effective features (one texture and five color features) were selected to estimate the physicochemical properties of apples. The physicochemical estimation was then further optimized using a hybrid multilayer perceptron artificial neural network-cultural algorithm (ANN-CA). The results showed that the coefficient of determinations (R2) related to the prediction models for the physicochemical properties were in excess of 0.92. Additionally, the ripeness level of apples was estimated based on physicochemical properties using a hybrid multilayer perceptron artificial neural network-harmonic search algorithm (ANN-HS) classifier. The developed machine vision system examined ripeness levels of 1356 apples in natural orchard environments and achieved a correct classification rate (CCR) of 97.86%. Ripeness ; Ripening ; Horticulture ; Machine vision ; Artificial intelligence ; Food science ; Mathematics ; Computer science ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Qian Wu | Baibiao Huang | Ying Dai | Thomas Heine | Yandong Ma
Source: npj 2D Materials and Applications (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Main-group metal elements as promising active centers for single-atom catalyst toward nitric oxide reduction reaction Abstract Current research efforts on single-atom catalysts (SACs) exclusively focus on nonmetal or transition-metal atoms as active centers, while employing main-group metal elements is seemingly excluded because their delocalized s/p-bands are prone to yield a broadened resonance for the interaction with adsorbates. Here, we use high-throughput first-principles calculations to investigate the possible incorporation of Mg, Al, and Ga to form graphene-based SACs for NO reduction reaction (NORR) toward NH 3 . 51 SAC candidates with different metal coordination environments have been computationally screened employing a rationally designed four-step process, yielding six SACs with high catalytic activity and NORR selectivity. The performance is rationalized by the modulation of s/p-band filling of the main-group metals. The adsorption free energy of NO is identified as an efficient descriptor for such SACs. The underlying physical mechanism is revealed and generally applicable to other main group metal SACs. These fundamental insights extend NORR SACs to main-group metal elements. Catalysis ; Delocalized electron ; Main group element ; Atom (system on chip) ; Metal ; Yield (engineering) ; Nonmetal ; Group (periodic table) ; Oxide ; Transition metal ; Chemistry ; Photochemistry ; Materials science ; Nanotechnology ; Combinatorial chemistry ; Organic chemistry ; Metallurgy ; Computer science Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Xie Zhang | Jian Zhang | Hongcai Wang | Jutta Rogal | Hongyi Li | Su‐Huai Wei | Tilmann Hickel
Source: Applied Physics Reviews (article, 2022-11-15)
Field: Materials Science — Materials Chemistry

Defect-characterized phase transition kinetics Phase transitions are a common phenomenon in condensed matter and act as a critical degree of freedom that can be employed to tailor the mechanical or electronic properties of materials. Understanding the fundamental mechanisms of the thermodynamics and kinetics of phase transitions is, thus, at the core of modern materials design. Conventionally, studies of phase transitions have, to a large extent, focused on pristine bulk phases. However, realistic materials exist in a complex form; their microstructures consist of different point and extended defects. The presence of defects impacts the thermodynamics and kinetics of phase transitions, but has been commonly ignored or treated separately. In recent years, with the significant advances in theoretical and experimental techniques, there has been an increasing research interest in modeling and characterizing how defects impact or even dictate phase transitions. The present review systematically discusses the recent progress in understanding the kinetics of defect-characterized phase transitions, derives the key mechanisms underlying these phase transitions, and envisions the remaining challenges and fruitful research directions. We hope that these discussions and insights will help to inspire future research and development in the field. Phase transition ; Kinetics ; Phase (matter) ; Materials science ; Nanotechnology ; Statistical physics ; Degrees of freedom (physics and chemistry) ; Chemical physics ; Thermodynamics ; Physics Shape Memory Alloy Transformations ; Intermetallics and Advanced Alloy Properties ; High Temperature Alloys and Creep
Authors: Jiaojiao Zhou | Yue Gui | Xuqin Lv | Jiangling He | Fang Xie | Jinjie Li | Jie Cai
Source: Biosensors (review, 2022-11-23)
Field: Materials Science — Materials Chemistry

Nanomaterial-Based Fluorescent Biosensor for Food Safety Analysis Food safety issues have become a major threat to public health and have garnered considerable attention. Rapid and effective detection methods are crucial for ensuring food safety. Recently, nanostructured fluorescent materials have shown considerable potential for monitoring the quality and safety of food because of their fascinating optical characteristics at the nanoscale. In this review, we first introduce biomaterials and nanomaterials for food safety analysis. Subsequently, we perform a comprehensive analysis of food safety using fluorescent biosensors based on nanomaterials, including mycotoxins, heavy metals, antibiotics, pesticide residues, foodborne pathogens, and illegal additives. Finally, we provide new insights and discuss future approaches for the development of food safety detection, with the aim of improving fluorescence detection methods for the practical application of nanomaterials to ensure food safety and protect human health. Food safety ; Risk analysis (engineering) ; Food packaging ; Nanotechnology ; Food quality ; Biochemical engineering ; Biotechnology ; Computer science ; Business ; Materials science ; Engineering ; Chemistry ; Food science ; Biology Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection