jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Claudia Borredon | Luis A. Miccio | Anh D. Phan | Gustavo A. Schwartz
Source: Journal of Non-Crystalline Solids X (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

Estimating glass transition temperature and related dynamics of molecular glass formers combining artificial neural networks and disordered systems theory Glass transition temperature and related dynamics play an essential role in amorphous materials research since many of their properties and functionalities depend on molecular mobility. However, the temperature dependence of the structural relaxation time for a given glass former is only experimentally accessible after synthesizing it, implying a time-consuming and costly process. In this work, we propose combining artificial neural networks and disordered systems theory to estimate the glass transition temperature and the temperature dependence of the main relaxation time based on the knowledge of the molecule's chemical structure. This approach provides a way to assess the dynamics of molecular glass formers, with reasonable accuracy, even before synthesizing them. We expect this methodology to boost industrial development, save time and resources, and accelerate the scientific understanding of structure-properties relationships. Glass transition ; Amorphous solid ; Relaxation (psychology) ; Molecular dynamics ; Materials science ; Artificial neural network ; Work (physics) ; Chemical physics ; Statistical physics ; Dynamics (music) ; Computer science ; Biological system ; Thermodynamics ; Polymer ; Chemistry ; Physics ; Artificial intelligence ; Computational chemistry ; Composite material Material Dynamics and Properties ; Theoretical and Computational Physics ; Glass properties and applications
Authors: Dario Antweiler | Marc Harmening | Nicole Marheineke | Andre Schmeißer | Raimund Wegener | Pascal Welke
Source: Software Impacts (article, 2022-09-15)
Field: Materials Science — Polymers and Plastics

Machine learning framework to predict nonwoven material properties from fiber graph representations Nonwoven fiber materials are omnipresent in diverse applications including insulation, clothing and filtering. Simulation of material properties from production parameters is an industry goal but a challenging task. We developed a machine learning based approach to predict the tensile strength of nonwovens from fiber lay-down settings via a regression model. Here we present an open source framework implementing the following two-step approach: First, a graph generation algorithm constructs stochastic graphs, that resemble the adhered fiber structure of the nonwovens, given a parameter space. Secondly, our regression model, learned from ODE-simulation results, predicts the tensile strength for unseen parameter combinations. Computer science ; Graph ; Fiber ; Artificial intelligence ; Materials science ; Theoretical computer science ; Composite material Textile materials and evaluations
Authors: Bowen Liu | Fangcheng Lv | Xiaozhou Fan | Yueyi Sui | Jiaxue Wang | Shengdong Yin
Source: Computational Intelligence and Neuroscience (article, 2022-08-11)
Field: Materials Science — Materials Chemistry

Preparation and Performance Analysis of Transformer Aramid Nanopaper-Based Insulating Material Based on Deep Learning Transformers are an essential part of power production. Insulating paper began to be widely used in transformers in the 1990s. The superior aramid nanofiber as the matrix gives the aramid nano-insulating paper excellent mechanical properties, insulation performance, temperature resistance, and flexibility. At first, the heat resistance and service life of insulating paper should be satisfied for use in electrical equipment. With the continuous development of power equipment, people have put forward higher requirements on the properties of insulating paper, especially heat resistance and electrical properties. Insulation paper made of aramid fibers have better thermal stability and more advantages in electrical and mechanical properties, which can significantly improve the service life and safety of electrical appliances. The purpose of this article is to study the use of aramid nanopaper-based insulating materials in transformers to explore the effect of transformer discharge mechanism on aramid nanopaper-based insulating materials. This paper proposes to design multiple deep learning models to identify the discharge mode of the voltage transformer, find the characteristic signal, and carry out related tests on the discharge signal of different modes, and find the maximum temperature value of the aramid nanopaper-based insulating material for industrial use. The experimental results in this paper show that the aramid nanopaper-based insulating material can be used in transformers discharge detection well, and the safety rate is increased by 20%. Aramid ; Materials science ; Transformer ; Electrical insulation paper ; Composite material ; Voltage ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; High-Voltage Power Transmission Systems
Authors: Kazuumi Fujioka | Rui Sun
Source: Chemical Physics (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

Interpolating Moving Ridge Regression (IMRR): A machine learning algorithm to predict energy gradients for ab initio molecular dynamics simulations Ab initio ; Chemistry ; Statistical physics ; Energy (signal processing) ; Algorithm ; Molecular dynamics ; Ridge ; Energy profile ; Set (abstract data type) ; Density functional theory ; Potential energy ; Computational chemistry ; Artificial intelligence ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jie Peng | Jianwen Du | Liping Wang
Source: Journal of Physics Conference Series (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

The Application of Deep Learning on Room Temperature Conductivity of Li<sub>10</sub>GeP<sub>2</sub>S<sub>12</sub> Type Solid State Electrolyte Abstract Room temperature ionic conductivity of the solid-state electrolyte is the essencial element of the commercialization of solid-state lithium-ion batteries. First-principles method is a good tool as it simulates the atomic level dynamics, and provides accurate insides of the ion moving velocity, path, and the microscopic environment. However, the first-principles method is time and computational resources-consuming. It is usually applied to systems with hundreds of atoms and the simulation time is less than 100 picoseconds. The classical molecular dynamics method applies to systems containing thousands of atoms and the simulation time can reach the scale of nanoseconds. However, it is based on the experimental force-field parameters and can only be applied to limited systems with experimentally-proofed good parameters. The method used in this paper is a combination of the first-principles simulation and classical dynamics. Deep-learning tool Deepmd-kit is used to train the first-principles simulation data of the targeted system into a tailor-made force-field model. The verified model for the specific system will then be used in the classical molecular dynamics simulation. With the first-principles precise and classical efficiency, the molecular dynamics simulation of ion movement at room temperature is achieved. The results of Li 10 GeP 2 S 12 and Li 10 SiP 2 S 12 showed good agreement with the experiments. Molecular dynamics ; Force field (fiction) ; Field (mathematics) ; Conductivity ; Ion ; Electrolyte ; Computer science ; Nanosecond ; Simulation ; Ionic conductivity ; Atomic units ; Materials science ; Statistical physics ; Computational physics ; Chemistry ; Physics ; Computational chemistry ; Artificial intelligence ; Physical chemistry ; Mathematics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Antonio Tripodo | Gianfranco Cordella | F. Puosi | Marco Malvaldi | D. Leporini
Source: International Journal of Molecular Sciences (article, 2022-08-18)
Field: Materials Science — Materials Chemistry

Neural Networks Reveal the Impact of the Vibrational Dynamics in the Prediction of the Long-Time Mobility of Molecular Glassformers Two neural networks (NN) are designed to predict the particle mobility of a molecular glassformer in a wide time window ranging from vibrational dynamics to structural relaxation. Both NNs are trained by information concerning the local structure of the environment surrounding a given particle. The only difference in the learning procedure is the inclusion (NN <b>A</b>) or not (NN <b>B</b>) of the information provided by the fast, vibrational dynamics and quantified by the local Debye-Waller factor. It is found that, for a given temperature, the prediction provided by the NN <b>A</b> is more accurate, a finding which is tentatively ascribed to better account of the bond reorientation. Both NNs are found to exhibit impressive and rather comparable performance to predict the four-point susceptibility χ4(t) at τα, a measure of the dynamic heterogeneity of the system. Statistical physics ; Artificial neural network ; Relaxation (psychology) ; Molecular dynamics ; Dynamics (music) ; Biological system ; Contrast (vision) ; Particle (ecology) ; Computer science ; Measure (data warehouse) ; Chemical physics ; Vibrational energy relaxation ; Artificial intelligence ; Physics ; Chemistry ; Data mining ; Computational chemistry ; Molecule ; Quantum mechanics ; Biology Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Ionic liquids properties and applications
Authors: José Ferraz-Caetano
Source: Current Organocatalysis (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Current Outlooks on Machine Learning Methods for the Development ofIndustrial Homogeneous Catalytic Systems Abstract: This brief perspective outlines the pivotal role of Machine Learning methods in the green, digital transition of industrial chemistry. The focus on homogenous catalysis highlights the recent methodologies in the development of industrial processes, including the design of new catalysts and the enhancement of sustainable reaction conditions to lower production costs. We report several examples of Machine Learning assisted methodologies through recent Data Science trends on innovation of industrial homogeneous organocatalytic systems. We also stress the current benefits, drawbacks, and limitations towards the mass implementation of these Data Science methodologies. Homogeneous ; Perspective (graphical) ; Computer science ; Biochemical engineering ; Management science ; Artificial intelligence ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Emir Kocer | Tsz Wai Ko | Jörg Behler
Source: Annual Review of Physical Chemistry (preprint, 2022-01-04)
Field: Materials Science — Materials Chemistry

Neural Network Potentials: A Concise Overview of Methods In the past two decades, machine learning potentials (MLPs) have reached a level of maturity that now enables applications to large-scale atomistic simulations of a wide range of systems in chemistry, physics, and materials science. Different machine learning algorithms have been used with great success in the construction of these MLPs. In this review, we discuss an important group of MLPs relying on artificial neural networks to establish a mapping from the atomic structure to the potential energy. In spite of this common feature, there are important conceptual differences among MLPs, which concern the dimensionality of the systems, the inclusion of long-range electrostatic interactions, global phenomena like nonlocal charge transfer, and the type of descriptor used to represent the atomic structure, which can be either predefined or learnable. A concise overview is given along with a discussion of the open challenges in the field. Artificial neural network ; Curse of dimensionality ; Computer science ; Artificial intelligence ; Range (aeronautics) ; Field (mathematics) ; Machine learning ; Feature (linguistics) ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: An Su | Yingying Cheng | Haotian Xue | Yuanbin She | Krishna Rajan
Source: AIChE Journal (article, 2022-03-23)
Field: Materials Science — Materials Chemistry

Artificial intelligence informed toxicity screening of amine chemistries used in the synthesis of hybrid <scp>organic–inorganic</scp> perovskites Abstract This article describes a machine learning guided framework for screening the potential toxicity impact of amine chemistries used in the synthesis of hybrid organic–inorganic perovskites. Using a combination of a probabilistic molecular fingerprint technique that encodes bond connectivity (MinHash) coupled to non‐linear data dimensionality reduction methods (Uniform Manifold Approximation and Projection), we develop an “Amine Atlas.” We show how the Amine Atlas can be used to rapidly screen the relative toxicity levels of amine molecules used in the synthesis of 2D and 3D perovskites and help identify safer alternatives. Our work also serves as a framework for rapidly identifying molecular similarity guided, structure–function relationships for safer materials chemistries that also incorporate sustainability/toxicity concerns. Amine gas treating ; Chemistry ; SAFER ; Molecule ; Probabilistic logic ; Organic molecules ; Atlas (anatomy) ; Curse of dimensionality ; Computer science ; Combinatorial chemistry ; Biochemical engineering ; Artificial intelligence ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Conducting polymers and applications
Authors: O Amnuaykijvanit | Siripon Anantawaraskul | T Rakthanmanon
Source: Journal of Physics Conference Series (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Estimation of ethylene/1-butene copolymerization conditions using the autoencoder model Abstract Chain microstructures of ethylene/1-butene copolymers produced at specific polymerization conditions can be calculated using the copolymerization kinetic model. However, this kinetic model cannot be solved inversely to estimate polymerization conditions from desired microstructures. In this work, the autoencoder (AE) model, the machine learning techniques based on artificial neural network (ANN) concept, was developed to help estimate polymerization conditions to produce polymers with desired microstructures (e.g., molecular weight distribution (MWD), chemical composition distribution (CCD), and number and weight average molecular weight). Two models were developed in this work: one with only microstructural distributions (MWD and CCD) and the other with additional information on average microstructures and polymer yield. The results showed that the proposed AE models can adequately estimate polymerization conditions from desired microstructures with acceptable mean square error (MSE). More specific microstructures lead to better estimation of polymerization conditions with lower MSE. Polymerization ; Materials science ; Copolymer ; Microstructure ; Molar mass distribution ; Autoencoder ; Polymer ; Work (physics) ; Biological system ; Mean squared error ; Thermodynamics ; Polymer chemistry ; Chemical engineering ; Artificial neural network ; Composite material ; Mathematics ; Computer science ; Statistics ; Physics ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Polymer crystallization and properties ; Injection Molding Process and Properties
Authors: Hao Gao | Valentina Belova | Francesco La Porta | Santiago Cingolani | Mie Andersen | Mehdi Saedi | Oleg Konovalov | Maciej Jankowski | Hendrik H. Heenen | Irene M. N. Groot | G. Renaud | Karsten Reuter
Source: ChemRxiv (preprint, 2022-07-12)
Field: Materials Science — Materials Chemistry

Graphene at Liquid Copper Catalysts: Atomic-Scale Agreement of Experimental and First-Principles Adsorption Height Liquid metal catalysts have recently attracted attention for synthesizing high-quality two-dimensional (2D) materials facilitated via the catalysts’ perfectly smooth surface. However, the microscopic catalytic processes occurring at the surface are still largely unclear because liquid metals escape the accessibility of traditional experimental and computational surface science approaches. For this reason, numerous controversies are found regarding different applications, with graphene (Gr) growth on liquid copper (Cu) as a prominent prototype. In this work, we employ novel in situ and in silico techniques to achieve an atomic-level characterization of the graphene adsorption height above liquid Cu, reaching a quantitative agreement within 0.1 Å between experiment and theory. Our results are obtained via in situ synchrotron X-ray reflectivity (XRR) measurements over wide-range q-vectors and large-scale molecular dynamics simulations based on efficient machine-learning (ML) potentials trained to first-principles density-functional theory (DFT) data. We demonstrate our computational insight to be robust against inherent DFT errors and reveal the nature of graphene binding to be highly comparable at liquid Cu and solid Cu(111). Transporting the predictive first-principles quality via ML potentials to the scales and sampling required for liquid metal catalysis thus provides a powerful approach to reach microscopic understanding, analogous to the established computational approaches for catalysis at solid surfaces. Catalysis ; Graphene ; Density functional theory ; Adsorption ; Copper ; Synchrotron ; Materials science ; Chemical physics ; Characterization (materials science) ; Molecular dynamics ; Atomic units ; Nanotechnology ; Computational chemistry ; Chemistry ; Physical chemistry ; Physics ; Optics ; Metallurgy ; Organic chemistry ; Quantum mechanics Graphene research and applications ; Surface and Thin Film Phenomena ; Theoretical and Computational Physics
Authors: Christopher Kuenneth | Jessica Lalonde | Babetta L. Marrone | Carl N. Iverson | Rampi Ramprasad | Ghanshyam Pilania
Source: arXiv (Cornell University) (preprint, 2022-03-22)
Field: Materials Science — Biomaterials

Bioplastic Design using Multitask Deep Neural Networks Non-degradable plastic waste stays for decades on land and in water, jeopardizing our environment; yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as the polymer family of polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world's plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. In this work, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world's yearly plastic production. We discuss possible synthesis routes for these identified promising materials. The developed multitask polymer property predictors are made available as a part of the Polymer Genome project at https://PolymerGenome.org. Bioplastic ; Biochemical engineering ; Computer science ; Commodity ; Artificial intelligence ; Engineering ; Business ; Waste management biodegradable polymer synthesis and properties ; Machine Learning in Materials Science
Authors: Andrew J. Lew | Kai Jin | Markus J. Buehler
Source: arXiv (Cornell University) (preprint, 2022-12-05)
Field: Materials Science — Materials Chemistry

Architected Materials for Mechanical Compression: Design via Simulation, Deep Learning, and Experimentation Architected materials can achieve enhanced properties compared to their plain counterparts. Specific architecting serves as a powerful design lever to achieve targeted behavior without changing the base material. Thus, the connection between architected structure and resultant properties remains an open field of great interest to many fields, from aerospace to civil to automotive applications. Here, we focus on properties related to mechanical compression, and design hierarchical honeycomb structures to meet specific values of stiffness and compressive stress. To do so, we employ a combination of techniques in a singular workflow, starting with molecular dynamics simulation of the forward design problem, augmenting with data-driven artificial intelligence models to address the inverse design problem, and verifying the behavior of de novo structures with experimentation of additively manufactured samples. We thereby demonstrate an approach for architected design that is generalizable to multiple material properties and agnostic to the identity of the base material. Workflow ; Computer science ; Aerospace ; Compression (physics) ; Stiffness ; Focus (optics) ; Mechanical engineering ; Engineering ; Materials science ; Structural engineering ; Aerospace engineering Machine Learning in Materials Science ; Cellular and Composite Structures ; Block Copolymer Self-Assembly
Authors: Jonas Lederer | Michael Gastegger | Kristof T. Schütt | Michael Kampffmeyer | Klaus‐Robert Müller | Oliver T. Unke
Source: arXiv (Cornell University) (preprint, 2022-03-30)
Field: Materials Science — Materials Chemistry

Automatic Identification of Chemical Moieties In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, from which the properties of interest are predicted. Here, we introduce a method to automatically identify chemical moieties (molecular building blocks) from such representations, enabling a variety of applications beyond property prediction, which otherwise rely on expert knowledge. The required representation can either be provided by a pretrained MPNN, or learned from scratch using only structural information. Beyond the data-driven design of molecular fingerprints, the versatility of our approach is demonstrated by enabling the selection of representative entries in chemical databases, the automatic construction of coarse-grained force fields, as well as the identification of reaction coordinates. Computer science ; Identification (biology) ; Variety (cybernetics) ; Representation (politics) ; Task (project management) ; Quantum chemical ; Property (philosophy) ; Selection (genetic algorithm) ; Scratch ; Artificial neural network ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Chemistry ; Programming language ; Molecule ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics