jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lucian A. Lucia
Source: BioResources (article, 2022-08-02)
Field: Materials Science — Materials Chemistry

Compositomics: A timely conceptual framework for future advancements in green materials’ design and development Higher-order systems found in nature continue to be a source of inspiration for designing highly functional artificial systems. However, compositing these systems requires a precise understanding of how the components required can affect final desired responses. This non-trivial task is daunting and therefore will require a multiplicity of approaches elaborated under the umbrella of compositomics, a proposed –omics cluster dedicated to fabricating green materials through modeling, systems thinking, and machine learning. Compositing ; Systems engineering ; Computer science ; Task (project management) ; Engineering ; Artificial intelligence Machine Learning in Materials Science
Authors: Alexander Ryabov | Iskander Akhatov | Petr Zhilyaev
Source: Research Square (preprint, 2022-08-03)
Field: Materials Science — Materials Chemistry

Application of two-component neural network for exchange-correlation functional interpolation Abstract Density functional theory (DFT) is one of the primary approaches to solving the many-body Schrodinger equation. The essential part of the DFT theory is the exchange-correlation (XC) functional, which can not be obtained in analytical form. Accordingly, the accuracy improvement of the DFT is mainly based on the development of XC functional approximations. Commonly, they are built upon analytic solutions in low-and high-density limits and result from quantum Monte Carlo or post-Hartree-Fock numerical calculations. However, there is no universal functional form to incorporate these data into XC functional. Instead, various parameterizations use heuristic rules to build a specific XC functional. The neural network (NN) approach to interpolate the data from higher precision theories can give a unified path to parametrize an XC functional. Moreover, data from many existing quantum chemical databases could provide the XC functional with improved accuracy. We develop NN XC functional, which gives exchange potential and energy density without direct derivatives of exchange-correlation energy density. Proposed NN architecture consists of two parts NN-E and NN-V, which could be trained in separate ways, adding new flexibility to XC functional. We also show that the developed NN XC functional converges in the self-consistent cycle and gives reasonable energies when applied to atoms, molecules, and crystals. Density functional theory ; Hybrid functional ; Orbital-free density functional theory ; Energy functional ; Interpolation (computer graphics) ; Functional theory ; Statistical physics ; Artificial neural network ; Physics ; Algorithm ; Quantum mechanics ; Computer science ; Artificial intelligence ; Classical mechanics Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Inorganic Chemistry and Materials
Authors: Salma Salem | Stephen R. Byrn | Daniel W. Smith | Vadim J. Gurvich | Stephen W. Hoag | Feng Zhang | Robert O. Williams | Kari Clase
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Impact Assessment of the Variables Affecting the Drug Release and Extraction of Polyethylene Oxide Based Abuse Deterrent Tablets Polyethylene oxide ; Extraction (chemistry) ; Substance abuse ; Drug ; Chromatography ; Medicine ; Materials science ; Chemistry ; Pharmacology ; Psychiatry ; Composite material ; Polymer Analytical Methods in Pharmaceuticals ; Drug Solubulity and Delivery Systems ; Safe Handling of Antineoplastic Drugs
Authors: Alexandra Boltasseva
Source:  (article, 2022-05-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancing photonic design with machine learning Discovering unconventional optical designs via machine-learning promises to advance on-chip circuitry, imaging, sensing, energy, and quantum information technology. In this talk, photonic design approaches and emerging material platforms will be discussed showcasting machine-learning-assisted topology optimization for optical metasurface designs for applications in thermophotovoltaics, reflective optics and more. Photonics ; Computer science ; Thermophotovoltaic ; Electronic engineering ; Engineering ; Materials science ; Optoelectronics Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Plasmonic and Surface Plasmon Research
Authors: Wyatt Bridgman | Xiaoxuan Zhang | Greg Teichert | Mohammed Khalil | Krishna Garikipati | Reese Jones
Source: Computer Methods in Applied Mechanics and Engineering (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

A heteroencoder architecture for prediction of failure locations in porous metals using variational inference In this work we employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities. The process we model is complex, with a progression from initial void nucleation, to saturation, and ultimately failure. The objective of predicting failure locations presents an extreme case of class imbalance since most of the material in the specimens do not fail. In response to this challenge, we develop and demonstrate the effectiveness of data- and loss-based regularization methods. Since there is considerable sensitivity of the failure location to the particular configuration of voids, we also use variational inference to provide uncertainties for the neural network predictions. We connect the deterministic and Bayesian convolutional neural networks at a theoretical level to explain how variational inference regularizes the training and predictions. We demonstrate that the resulting predicted variances are effective in ranking the locations that are most likely to fail in any given specimen. Inference ; Convolutional neural network ; Computer science ; Bayesian inference ; Artificial neural network ; Bayesian probability ; Algorithm ; Artificial intelligence Machine Learning in Materials Science ; Nanoporous metals and alloys ; Generative Adversarial Networks and Image Synthesis
Authors: Xiaoxin Lu | Jiabin Huang | NAN CHENG | Linlin Ren | Jianbin Xu | Jibao Lu | Rong Sun
Source: ChemRxiv (preprint, 2022-07-27)
Field: Materials Science — Materials Chemistry

RETRACTED: Accurate determination of interfacial thermal resistance inside particle-laden composites based on high-throughput computation and machine learning Particle-laden composites are typical thermal interfacial materials (TIMs) in the electronic applications, which are widely used in the electron packaging fields. The effective thermal conductivity (effective TC) of the particle-laden composites is dominant by the particle-matrix and particle-particle interfacial thermal resistance (ITR). The reliable identification of ITR is essential for the accurate prediction of TC of the composites, which has potential in the design of TIMs. In this work, we propose an efficient strategy to identify the interfacial thermal resistance in the particle-laden composites combining the numerical simulation, high-throughput computation, machine learning algorithm and simple experimental measurement. Firstly, the high-throughput computation is conducted based on the numerical modeling of the standard samples, in which the input parameters are ITRs in the composites. Afterwards, a prototypical function-based machine learning strategy is employed on the database to describe the numerical relation between the effective TC and the input parameters. Finally, comparing the numerical predictions from the machine learning model with the experimental measurement of the effective TC, a high-throughput screening of the ITRs is executed for the identification of their values. The reliability of the strategy is validated by an example of Al2O3-AlN/silicone composites, showing that the particle-particle ITR is higher than particle-matrix ITR. Particle (ecology) ; Composite material ; Materials science ; Computation ; Throughput ; Work (physics) ; Thermal ; Thermal conductivity ; Computer science ; Mechanical engineering ; Algorithm ; Physics ; Engineering ; Thermodynamics Thermal properties of materials ; Composite Material Mechanics ; Aerogels and thermal insulation
Authors: Daniel Varivoda | Rongzhi Dong | Sadman Sadeed Omee | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2022-11-04)
Field: Materials Science — Materials Chemistry

Materials Property Prediction with Uncertainty Quantification: A Benchmark Study Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models. It can also be used in active learning to train better models by focusing on getting new training data from uncertain regions. There are several categories of UQ methods each considering different types of uncertainty sources. Here we conduct a comprehensive evaluation on the UQ methods for graph neural network based materials property prediction and evaluate how they truly reflect the uncertainty that we want in error bound estimation or active learning. Our experimental results over four crystal materials datasets (including formation energy, adsorption energy, total energy, and band gap properties) show that the popular ensemble methods for uncertainty estimation is NOT the best choice for UQ in materials property prediction. For the convenience of the community, all the source code and data sets can be accessed freely at \url{https://github.com/usccolumbia/materialsUQ}. Uncertainty quantification ; Computer science ; Property (philosophy) ; Benchmark (surveying) ; Code (set theory) ; Propagation of uncertainty ; Source code ; Machine learning ; Artificial intelligence ; Artificial neural network ; Data mining ; Algorithm Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Vir Karan | A. Maruthi Indresh | Saswata Bhattacharyya
Source: arXiv (Cornell University) (preprint, 2022-11-22)
Field: Materials Science — Materials Chemistry

Accelerated Solutions of Coupled Phase-Field Problems using Generative Adversarial Networks Multiphysics problems such as multicomponent diffusion, phase transformations in multiphase systems and alloy solidification involve numerical solution of a coupled system of nonlinear partial differential equations (PDEs). Numerical solutions of these PDEs using mesh-based methods require spatiotemporal discretization of these equations. Hence, the numerical solutions are often sensitive to discretization parameters and may have inaccuracies (resulting from grid-based approximations). Moreover, choice of finer mesh for higher accuracy make these methods computationally expensive. Neural network-based PDE solvers are emerging as robust alternatives to conventional numerical methods because these use machine learnable structures that are grid-independent, fast and accurate. However, neural network based solvers require large amount of training data, thus affecting their generalizabilty and scalability. These concerns become more acute for coupled systems of time-dependent PDEs. To address these issues, we develop a new neural network based framework that uses encoder-decoder based conditional Generative Adversarial Networks with ConvLSTM layers to solve a system of Cahn-Hilliard equations. These equations govern microstructural evolution of a ternary alloy undergoing spinodal decomposition when quenched inside a three-phase miscibility gap. We show that the trained models are mesh and scale-independent, thereby warranting application as effective neural operators. Discretization ; Computer science ; Artificial neural network ; Partial differential equation ; Spinodal decomposition ; Nonlinear system ; Multiphysics ; Scalability ; Mathematical optimization ; Applied mathematics ; Polygon mesh ; Algorithm ; Phase (matter) ; Artificial intelligence ; Mathematics ; Finite element method ; Mathematical analysis Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Solidification and crystal growth phenomena
Authors: Bastien F. Grosso | Nicola A. Spaldin | Aria Mansouri Tehrani
Source: The Journal of Physical Chemistry Letters (article, 2022-08-03)
Field: Materials Science — Materials Chemistry

Physics-Guided Descriptors for Prediction of Structural Polymorphs We develop a method combining machine learning (ML) and density functional theory (DFT) to predict low-energy polymorphs by introducing physics-guided descriptors based on structural distortion modes. We systematically generate crystal structures utilizing the distortion modes and compute their energies with single-point DFT calculations. We then train a ML model to identify low-energy configurations on the material's high-dimensional potential energy surface. Here, we use BiFeO<sub>3</sub> as a case study and explore its phase space by tuning the amplitudes of linear combinations of a finite set of distinct distortion modes. Our procedure is validated by rediscovering several known metastable phases of BiFeO<sub>3</sub> with complex crystal structures, and its efficiency is proved by identifying 21 new low-energy polymorphs. This approach proposes a new avenue toward accelerating the prediction of low-energy polymorphs in solid-state materials. Metastability ; Crystal structure prediction ; Distortion (music) ; Density functional theory ; Energy (signal processing) ; Crystal (programming language) ; Phase space ; Physics ; Statistical physics ; Set (abstract data type) ; Space (punctuation) ; Crystal structure ; Algorithm ; Computer science ; Crystallography ; Thermodynamics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Multiferroics and related materials ; Heusler alloys: electronic and magnetic properties
Authors: Daniel T. Chang
Source: arXiv (Cornell University) (preprint, 2022-07-16)
Field: Materials Science — Materials Chemistry

Distance-Geometric Graph Attention Network (DG-GAT) for 3D Molecular Geometry Deep learning for molecular science has so far mainly focused on 2D molecular graphs. Recently, however, there has been work to extend it to 3D molecular geometry, due to its scientific significance and critical importance in real-world applications. The 3D distance-geometric graph representation (DG-GR) adopts a unified scheme (distance) for representing the geometry of 3D graphs. It is invariant to rotation and translation of the graph, and it reflects pair-wise node interactions and their generally local nature, particularly relevant for 3D molecular geometry. To facilitate the incorporation of 3D molecular geometry in deep learning for molecular science, we adopt the new graph attention network with dynamic attention (GATv2) for use with DG-GR and propose the 3D distance-geometric graph attention network (DG-GAT). GATv2 is a great fit for DG-GR since the attention can vary by node and by distance between nodes. Experimental results of DG-GAT for the ESOL and FreeSolv datasets show major improvement (31% and 38%, respectively) over those of the standard graph convolution network based on 2D molecular graphs. The same is true for the QM9 dataset. Our work demonstrates the utility and value of DG-GAT for deep learning based on 3D molecular geometry. Geometry ; Graph ; Computer science ; Geometric graph theory ; Spatial network ; Random geometric graph ; Topology (electrical circuits) ; Line graph ; Mathematics ; Theoretical computer science ; Combinatorics ; Voltage graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Yujie Jiang | Yixin Yu | Chaohui Zhang | Wenbo Feng | Shen Liu | Zhiling Zhu | Ning Sui
Source: ChemRxiv (preprint, 2022-04-20)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Graphdiyne based Nanozyme Discovery Nanozymes are nanomaterials with enzyme-like activities, which are promising alternatives for natural enzymes because of their features of excellent performance, controllable activity, and environmental resistance. Improving the catalytic activity and broadening the scope of nanozymes are prerequisites to supplementing or even superseding enzymes. However, the discovery of nanozymes is mostly relied on serendipity with limited fine-tunings of chemical composition, which is often incomprehensive and fragmented so that severely impedes the development of novel nanozymes. In this work, a comprehensive investigation of the peroxidase mimic reaction catalyzed by nonmetallic doped-graphdiyne (GDY) is presented in terms of the dopant atom electronegativity, atomic radius, elemental doping sites, hydrogen peroxide adsorption sites, and bandgaps, covering all nonmetallic elements and enabling the screening of potential nanozymes. The density functional theory (DFT) calculations carefully explore the properties of nanozymes by analyzing two key steps of Gibbs’s free energy of H2O2 decomposition reaction. The machine learning (ML) technique AdaBoost integration model is innovatively utilized based on a fuzzy model for data predicting and it can predict the nanozyme performance with high regression accuracy (R2 >93%) in test set. Thereafter, six different predicted performances were synthesized are synthesized and confirmed in accordance with their excellent kinetics parameters experimentally. From three independent perspectives (DFT, ML, and experimental validation), this work proposes pivotal guidelines for nanozyme design and shows great potential as a general approach for future nanozyme discovery. Electronegativity ; Chemistry ; Catalysis ; Nanotechnology ; Density functional theory ; Biochemical engineering ; Materials science ; Computational chemistry ; Organic chemistry Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Cholinesterase and Neurodegenerative Diseases
Authors: Panagiota Birmpa | Jinchao Feng | Markos A. Katsoulakis | Luc Rey-Bellet
Source: SIAM/ASA Journal on Uncertainty Quantification (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

Model Uncertainty and Correctability for Directed Graphical Models Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning, and artificial intelligence. They allow us to integrate in a natural way expert knowledge, physical modeling, heterogeneous and correlated data, and quantities of interest. For exactly this reason, multiple sources of model uncertainty are inherent within the modular structure of the graphical model. In this paper we develop information-theoretic, robust uncertainty quantification methods and nonparametric stress tests for directed graphical models to assess the effect and the propagation through the graph of multisourced model uncertainties to quantities of interest. These methods allow us to rank the different sources of uncertainty and correct the graphical model by targeting its most impactful components with respect to the quantities of interest. Thus, from a machine learning perspective, we provide a mathematically rigorous approach to correctability that guarantees a systematic selection for improvement of components of a graphical model while controlling potential new errors created in the process in other parts of the model. We demonstrate our methods in two physicochemical examples, namely, quantum scale-informed chemical kinetics and materials screening to improve the efficiency of fuel cells. Graphical model ; Computer science ; Probabilistic logic ; Machine learning ; Artificial intelligence ; Modular design ; Graph ; Model selection ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Yusuf Hendrawan | Bagas Rohmatulloh | Fardha Irfatul Ilmi | Muchammad Riza Fauzy | Retno Damayanti | Dimas Firmanda Al Riza | Mochamad Bagus Hermanto | Sandra Sandra
Source: International Journal on Advanced Science Engineering and Information Technology (article, 2022-10-31)
Field: Materials Science — Biomaterials

Convolutional Neural Network to Detect the Optimal Water Content of Cassava Chips During the Drying Process Cassava chips are used as raw materials to manufacture modified cassava flour. To produce high-quality modified cassava flour, a drying process for cassava chips is required to produce optimal water content in the range of 15-18% wb. This study aims to detect the optimal water content of cassava chips during the drying process in a hybrid hot-air tray dryer with computer vision using a convolutional neural network. Three categories of cassava chips' water content during the drying process are wet (water content of 55-70% wb), semi-dry (20-40% wb), and optimal dry (15-18% wb). In this study, the performance of four types of the pre-trained convolutional neural network, i.e., AlexNet, GoogLeNet, ResNet-50, and SqueezeNet, were compared by using different optimizers (SGDm, Adam, and RMSProp) and different learning rate values, 0.00005 and 0.0001, resulting in 24 types of experimental design. The results showed 12 convolutional neural network models with perfect validation accuracy. AlexNet with the SGDm optimizer and learning rate of 0.00005 was determined as the best model because of its stable training iteration process that experienced no fluctuations, perfect validation accuracy, specifically 100%, as well as perfect testing accuracy was 100%, and fastest training and validation process time, notably 32 minutes. This best convolutional neural network model will later be used to develop a rapid, real-time, and accurate hybrid hot-air tray dryer with computer vision to maintain cassava chip products with optimal water content. Convolutional neural network ; Tray ; Process (computing) ; Artificial neural network ; Computer science ; Water content ; Artificial intelligence ; Deep learning ; Process engineering ; Pattern recognition (psychology) ; Pulp and paper industry ; Engineering ; Mechanical engineering Electrospun Nanofibers in Biomedical Applications ; Food and Agricultural Sciences ; Smart Agriculture and AI
Authors: Satadeep Bhattacharjee | Seung‐Cheol Lee
Source: SSRN Electronic Journal (preprint, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Modified Slater-Pauling Rule for the Heusler Compounds Alloyed with Late Transition Metals Via Compressed Sensing and Density Functional Theory Density functional theory ; Valence electron ; Transition metal ; Magnetic moment ; Materials science ; Valence (chemistry) ; Heusler compound ; Electron ; Electronic structure ; Doping ; Dopant ; Condensed matter physics ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity