jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhen Zhu | Baojuan Dong | Huaihong Guo | Teng Yang | Zhidong Zhang
Source: Chinese Physics B (article, 2020-02-13)
Field: Materials Science — Materials Chemistry

Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning* Two-dimensional (2D) semiconductors isoelectronic to phosphorene have been drawing much attention recently due to their promising applications for next-generation (opt)electronics. This family of 2D materials contains more than 400 members, including (a) elemental group-V materials, (b) binary III–VII and IV–VI compounds, (c) ternary III–VI–VII and IV–V–VII compounds, making materials design with targeted functionality unprecedentedly rich and extremely challenging. To shed light on rational functionality design with this family of materials, we systemically explore their fundamental band gaps and alignments using hybrid density functional theory (DFT) in combination with machine learning. First, calculations are performed using both the Perdew–Burke–Ernzerhof exchange–correlation functional within the general-gradient-density approximation (GGA-PBE) and Heyd–Scuseria–Ernzerhof hybrid functional (HSE) as a reference. We find this family of materials share similar crystalline structures, but possess largely distributed band-gap values ranging approximately from 0 eV to 8 eV. Then, we apply machine learning methods, including linear regression (LR), random forest regression (RFR), and support vector machine regression (SVR), to build models for the prediction of electronic properties. Among these models, SVR is found to have the best performance, yielding the root mean square error (RMSE) less than 0.15 eV for the predicted band gaps, valence-band maximums (VBMs), and conduction-band minimums (CBMs) when both PBE results and elemental information are used as features. Thus, we demonstrate that the machine learning models are universally suitable for screening 2D isoelectronic systems with targeted functionality, and especially valuable for the design of alloys and heterogeneous systems. Hybrid functional ; Density functional theory ; Band gap ; Support vector machine ; Ternary operation ; Semiconductor ; Valence band ; Valence (chemistry) ; Machine learning ; Materials science ; Mean squared error ; Phosphorene ; Electronic structure ; Computer science ; Artificial intelligence ; Computational chemistry ; Mathematics ; Physics ; Chemistry ; Optoelectronics ; Quantum mechanics 2D Materials and Applications ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Atharva Kelkar | Bradley C. Dallin | Reid C. Van Lehn
Source: The Journal of Physical Chemistry B (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks The hydrophobicity of functionalized interfaces can be quantified by the structure and dynamics of water molecules using molecular dynamics (MD) simulations, but existing methods to quantify interfacial hydrophobicity are computationally expensive. In this work, we develop a new machine learning approach that leverages convolutional neural networks (CNNs) to predict the hydration free energy (HFE) as a measure of interfacial hydrophobicity based on water positions sampled from MD simulations. We construct a set of idealized self-assembled monolayers (SAMs) with varying surface polarities and calculate their HFEs using indirect umbrella sampling calculations (INDUS). Using the INDUS-calculated HFEs as labels and physically informed representations of interfacial water density from MD simulations as input, we train and evaluate a series of neural networks to predict SAM HFEs. By systematically varying model hyperparameters, we demonstrate that a 3D CNN trained to analyze both spatial and temporal correlations between interfacial water molecule positions leads to HFE predictions that require an order of magnitude less MD simulation time than INDUS. We showcase the power of this model to explore a large design space by predicting HFEs for a set of 71 chemically heterogeneous SAMs with varying patterns and mole fractions. Molecular dynamics ; Convolutional neural network ; Biological system ; Water model ; Computer science ; Artificial neural network ; Set (abstract data type) ; Hyperparameter ; Work (physics) ; Artificial intelligence ; Chemical physics ; Chemistry ; Statistical physics ; Computational chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Electrostatics and Colloid Interactions
Authors: Shenghong Ju | Shuntaro Shimizu | Junichiro Shiomi
Source: Journal of Applied Physics (article, 2020-10-27)
Field: Materials Science — Materials Chemistry

Designing thermal functional materials by coupling thermal transport calculations and machine learning Advances in materials informatics (MI), which combines material property calculations/measurements and informatics algorithms, have realized properties in the nanostructures of thermal functional materials beyond what is accessible using empirical approaches based on physical instincts and models. In this Tutorial, we introduce technological procedures and underlying knowledge of MI combining thermal transport calculations and machine learning using an optimization problem of superlattice structures as an example (sample script available in the supplement). To provide fundamental guidance on how to use MI, we describe practical details about descriptors, objective functions, property calculators, machine learning (Bayesian optimization) algorithms, and optimization efficiencies. We then briefly review the recent successful applications of MI to design thermoelectric and thermal radiation materials. Finally, we summarize and provide future perspectives about the topic. Materials informatics ; Computer science ; Bayesian optimization ; Machine learning ; Property (philosophy) ; Artificial intelligence ; Informatics ; Thermal ; Coupling (piping) ; Health informatics ; Mechanical engineering ; Engineering ; Physics ; Engineering informatics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: А. Н. Залога | Vladimir Stanovov | Oksana E. Bezrukovа | P. S. Dubinin | Igor S. Yakimov
Source: Materials Today Communications (article, 2020-09-09)
Field: Materials Science — Materials Chemistry

Crystal symmetry classification from powder X-ray diffraction patterns using a convolutional neural network Convolutional neural network ; Materials science ; Diffraction ; Normalization (sociology) ; Artificial neural network ; Crystal (programming language) ; Crystal structure ; Pattern recognition (psychology) ; Artificial intelligence ; Crystal system ; Symmetry (geometry) ; Crystallography ; Computer science ; Optics ; Physics ; Mathematics ; Geometry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Aaron L. Liu | Rahul Venkatesh | Michael McBride | Elsa Reichmanis | J. Carson Meredith | Martha A. Grover
Source: ACS Applied Polymer Materials (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

Small Data Machine Learning: Classification and Prediction of Poly(ethylene terephthalate) Stabilizers Using Molecular Descriptors Experimental data from a patent were analyzed to learn about the small molecule additives that were most effective in mitigating the degradation of polyethylene terephthalate. Two sets of molecular descriptors were calculated for a dataset of 39 additive candidates; unsupervised and supervised analyses were performed to determine the most influential structural features that led to reduced degradation. A clustering approach revealed evidence that performance differences had some structural pattern dependence on the molecular descriptors that were employed. To pinpoint the features responsible for those physical differences, a reduced design region approach was applied to analyze descriptors both individually and in multiple dimensions to determine the effectiveness in a binary classification of high and low performances. For each molecular descriptor type, two or three influential descriptors were identified and justified with respect to the additive performance and physicochemical ability to mitigate degradation. Random forest models were constructed with relatively high predictability for both MACCS-166 (AUC = 0.86) and alvaDesc molecular descriptors (AUC = 0.93). We compare molecular descriptor methods for their ability to construct classifiers and to prioritize experimental work toward building a rich dataset. We find that, in small materials datasets, understanding the underlying physicochemical behavior is indispensable for validating the effectiveness of machine learning models. Molecular descriptor ; Predictability ; Construct (python library) ; Artificial intelligence ; Random forest ; Computer science ; Cluster analysis ; Machine learning ; Pattern recognition (psychology) ; Binary classification ; Binary number ; Data mining ; Biological system ; Quantitative structure–activity relationship ; Mathematics ; Support vector machine ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Peng Jia | Muhammad Ramzan | Shuliang Wang | Haizheng Zhong
Source: Chinese Journal of Chemistry (article, 2020-09-06)
Field: Materials Science — Materials Chemistry

How Machine Learning Accelerates the Development of Quantum Dots?<sup>†</sup> Abstract With the rapid developments in the field of information technology, the material research society is looking for an alternate scientific route to the traditional methods of trial and error in material research and process development. Machine learning emerges as a new research paradigm to accelerate the application‐oriented material discovery. Quantum dots are expanded as functional nanomaterials to enhance cutting‐edge photonic technology. However, they suffer from uncertainty in industrial fabrication and application. Here, we discuss how machine learning accelerates the development of quantum dots. The basic principles and operation procedures of machine learning are described with a few representative examples of quantum dots. We emphasize how machine learning contributes to the optimization of synthesis and the analysis of material characterizations. To conclude, we give a short perspective discussing the problems of combining machine learning and quantum dots. Quantum dot ; Field (mathematics) ; Process (computing) ; Nanotechnology ; Perspective (graphical) ; Quantum machine learning ; Quantum ; Computer science ; Artificial intelligence ; Quantum computer ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides
Authors: George S. Fanourgakis | Konstantinos Gkagkas | Emmanuel Tylianakis | George E. Froudakis
Source: The Journal of Physical Chemistry C (article, 2020-08-12)
Field: Chemistry — Inorganic Chemistry

Fast Screening of Large Databases for Top Performing Nanomaterials Using a Self-Consistent, Machine Learning Based Approach Toward the fast and accurate identification of top performing candidates from the huge pool of nanoporous materials that can potentially be synthesized, we present and evaluate a methodology that combines, in a self-consistent way, results of molecular simulations and machine learning approaches. The aim is to reduce as much as possible the number of candidates for which time-consuming and expensive theoretical and/or experimental studies are required. As a case study, we have used the adsorption of methane at different pressures in a large database of covalent-organic frameworks and in a small, but highly structural and chemical diverse database of experimentally synthesized metal–organic frameworks. It is found that when the self-consistent approach is combined with a machine learning algorithm, for which accurate descriptors are used, most of the top performing materials are identified, requiring only a small amount of reference data. The sensitivity of the algorithm to its underlined parameters is investigated and strategies for optimal choices are suggested. Nanoporous ; Computer science ; Machine learning ; Identification (biology) ; Sensitivity (control systems) ; Artificial intelligence ; Database ; Data mining ; Materials science ; Nanotechnology ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Logan Williams | Arpan Mukherjee | Krishna Rajan
Source: The Journal of Physical Chemistry Letters (article, 2020-08-25)
Field: Materials Science — Materials Chemistry

Deep Learning Based Prediction of Perovskite Lattice Parameters from Hirshfeld Surface Fingerprints This Letter describes the use of deep learning methods on Hirshfeld surface representations of crystal structure, as an automated means of predicting lattice parameters in cubic inorganic perovskites. While Hirshfeld Surface Analysis is a well-established tool in organic crystallography, we also introduce modified computational protocols for Hirshfeld Surface Analysis tailored specifically to account for nuanced but important differences dealing with inorganic crystals. We demonstrate how two-dimensional Hirshfeld surface fingerprints can serve as a rich "database" of information encoding the complexity of relationships between chemical bonding and bond geometry characteristics of perovskites. Our results are compared with other studies on lattice parameter prediction involving both experimental and computationally derived data, and it is shown that our approach is an improvement over other reported methods. The paper concludes by discussing how this work opens new avenues for data-driven high throughput computational predictions of structure-property relationships involving complex crystal chemistries. Lattice (music) ; Artificial intelligence ; Materials science ; Computer science ; Crystallography ; Pattern recognition (psychology) ; Chemistry ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Saientan Bag | Abhishek Aggarwal | Prabal K. Maiti
Source: The Journal of Physical Chemistry A (article, 2020-09-02)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Electronic Coupling between the Guanine Bases of DNA Charge transport in deoxyribonucleic acid (DNA) is of immense interest in biology and molecular electronics. Electronic coupling between the DNA bases is an important parameter describing the efficiency of charge transport in DNA. A reasonable estimation of this electronic coupling requires many expensive first principle calculations. In this article, we present a machine learning (ML) based model to calculate the electronic coupling between the guanine bases of the DNA (in the same strand) of any length, thus avoiding expensive first-principle calculations. The electronic coupling between the bases are evaluated using density functional theory (DFT) calculations with the morphologies derived from fully atomistic molecular dynamics (MD) simulations. A new and simple protocol based on the coarse-grained model of the DNA has been used to extract the feature vectors for the DNA bases. A deep neural network (NN) is trained with the feature vector as input and the DFT-calculated electronic coupling as output. Once well trained, the NN can predict the DFT-calculated electronic coupling of new structures with a mean absolute error (MAE) of 0.02 eV. Coupling (piping) ; Guanine ; Density functional theory ; Charge (physics) ; DNA ; Artificial neural network ; Electronic structure ; Feature (linguistics) ; Computer science ; Computational chemistry ; Chemistry ; Biological system ; Physics ; Molecular physics ; Algorithm ; Materials science ; Artificial intelligence ; Quantum mechanics ; Biology ; Nucleotide Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; DNA and Nucleic Acid Chemistry
Authors: Lakhdar Sidi Salah | Mohamed Chouai | Yann Danlée | Isabelle Huynen | Nassira Ouslimani
Source: Micromachines (article, 2020-08-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Simulation and Optimization of Electromagnetic Absorption of Polycarbonate/CNT Composites Using Machine Learning Electronic devices that transmit, distribute, or utilize electrical energy create electromagnetic interference (EMI) that can lead to malfunctioning and degradation of electronic devices. EMI shielding materials block the unwanted electromagnetic waves from reaching the target material. EMI issues can be solved by using a new family of building blocks constituted of polymer and nanofillers. The electromagnetic absorption index of this material is calculated by measuring the "S-parameters". In this article, we investigated the use of artificial intelligence (AI) in the EMI shielding field by developing a new system based on a multilayer perceptron neural network designed to predict the electromagnetic absorption of polycarbonate-carbon nanotubes composites films. The proposed system included 15 different multilayer perception (MLP) networks; each network was specialized to predict the absorption value of a specific category sample. The selection of appropriate networks was done automatically, using an independent block. Optimization of the hyper-parameters using hold-out validation was required to ensure the best results. To evaluate the performance of our system, we calculated the similarity error, precision accuracy, and calculation time. The results obtained over our database showed clearly that the system provided a very good result with an average accuracy of 99.7997%, with an overall average calculation time of 0.01295 s. The composite based on polycarbonate-5 wt.% carbon nanotube was found to be the ultimate absorber over microwave range according to Rozanov formalism. EMI ; Electromagnetic interference ; Materials science ; Electromagnetic shielding ; Polycarbonate ; Composite material ; Carbon nanotube ; Artificial neural network ; Electromagnetic radiation ; Composite number ; Absorption (acoustics) ; Microwave ; Computer science ; Artificial intelligence ; Telecommunications ; Optics ; Physics Electromagnetic wave absorption materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Urban Transport Systems Analysis
Authors: Vivek Sinha | Jochem Jan Laan | Evgeny A. Pidko
Source: Physical Chemistry Chemical Physics (article, 2020-12-09)
Field: Chemistry — Organic Chemistry

Accurate and rapid prediction of p <i>K</i> <sub>a</sub> of transition metal complexes: semiempirical quantum chemistry with a data-augmented approach Rapid and accurate prediction of reactivity descriptors of transition metal (TM) complexes is a major challenge for contemporary quantum chemistry. The recently-developed GFN2-xTB method based on the density functional tight-binding theory (DFT-B) is suitable for high-throughput calculation of geometries and thermochemistry for TM complexes albeit with moderate accuracy. Herein we present a data-augmented approach to improve substantially the accuracy of the GFN2-xTB method for the prediction of thermochemical properties using pKa values of TM hydrides as a representative model example. We constructed a comprehensive database for ca. 200 TM hydride complexes featuring the experimentally measured pKa values as well as the GFN2-xTB-optimized geometries and various computed electronic and energetic descriptors. The GFN2-xTB results were further refined and validated by DFT calculations with the hybrid PBE0 functional. Our results show that although the GFN2-xTB performs well in most cases, it fails to adequately describe TM complexes featuring multicarbonyl and multihydride ligand environments. The dataset was analyzed with the ordinary least squares (OLS) fitting and was used to construct an automated machine learning (AutoML) approach for the rapid estimation of pKa of TM hydride complexes. The results obtained show a high predictive power of the very fast AutoML model (RMSE ∼ 2.7) comparable to that of the much slower DFT calculations (RMSE ∼ 3). The presented data-augmented quantum chemistry-based approach is promising for high-throughput computational screening workflows of homogeneous TM-based catalysts. Transition metal ; Chemistry ; Quantum chemistry ; Hydride ; Quantum ; Quantum chemical ; Computational chemistry ; Metal ; Physical chemistry ; Physics ; Reaction mechanism ; Quantum mechanics ; Molecule ; Catalysis ; Organic chemistry Free Radicals and Antioxidants ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Amirhossein Fallah | Akeem Adeyemi Oladipo | Mustafa Gazi
Source: Journal of Materials Science Materials in Electronics (article, 2020-07-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Boron-doped sucrose carbons for supercapacitor electrode: artificial neural network-based modelling approach Supercapacitor ; Cyclic voltammetry ; Materials science ; Dopant ; Scanning electron microscope ; Raman spectroscopy ; Dielectric spectroscopy ; Electrolyte ; Capacitance ; Boron ; Chemical engineering ; Electrode ; Electrochemistry ; Analytical Chemistry (journal) ; Doping ; Chemistry ; Composite material ; Chromatography ; Optoelectronics ; Organic chemistry ; Physical chemistry Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Conducting polymers and applications
Authors: Jacob Townsend | Konstantinos D. Vogiatzis
Source: Journal of Chemical Theory and Computation (article, 2020-11-03)
Field: Materials Science — Materials Chemistry

Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies Machine learning methods have enabled the low-cost evaluation of molecular properties such as energy at an unprecedented scale. While many of such applications have focused on molecular input based on geometry, few studies consider representations based on the underlying electronic structure. Directing the attention to the electronic structure offers a unique challenge that allows for a more detailed representation of the underlying physics and how they affect molecular properties. The target of this work is to efficiently encode a lower-cost correlated wave function derived from MP2 to predict a higher-cost coupled-cluster singles-and-doubles (CCSD) wave function based on correlation-pair energies and the contributing electron promotions (excitations) and integrals. The new molecular representation explores the short-range behavior of electron correlation and utilizes distinct models that differentiate between two-electron promotions from the same molecular orbital or from two different orbitals. We present a re-engineered set of input features that provide an intuitive description of the orbital properties involved in electron correlation. The overall models are found to be highly transferable and size extensive, necessitating very few training instances to approach the chemical accuracy of a broad spectrum of organic molecules. The efficiency and transferability of the novel representation are demonstrated on a series of linear hydrocarbons, the potential energy surface of the water dimer, and on the GDB-9 database. For the GDB-9 database, we found that data from only 140 randomly selected molecules are adequate to achieve chemical accuracy for more than 133 000 organic molecules. Molecular orbital ; Computer science ; Representation (politics) ; Atomic orbital ; Water dimer ; Wave function ; Electronic correlation ; Function (biology) ; Coupled cluster ; Transferability ; Statistical physics ; Electronic structure ; Electron ; Molecule ; Chemical physics ; Physics ; Chemistry ; Computational chemistry ; Atomic physics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies