jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yashaswi Pathak | Karandeep Singh Juneja | Girish Varma | Masahiro Ehara | U. Deva Priyakumar
Source: ChemRxiv (preprint, 2020-05-18)
Field: Materials Science — Materials Chemistry

Deep Learning Enabled Inorganic Material Generator Recent years have witnessed utilization of modern machine learning approaches for predicting properties of material using available datasets. However, to identify potential candidates for material discovery, one has to systematically scan through a large chemical space and subsequently calculate the properties of all such samples. On the other hand, generative methods are capable of efficiently sampling the chemical space and can generate molecules/materials with desired properties. In this study, we report a deep learning based inorganic material generator (DING) framework consisting of a generator module and a predictor module. The generator module is developed based upon conditional variational autoencoders (CVAE) and the predictor module consists of three deep neural networks trained for predicting enthalpy of formation, volume per atom and energy per atom chosen to demonstrate the proposed method. The predictor and generator modules have been developed using a one hot key representation of the material composition. A series of tests were done to examine the robustness of the predictor models, to demonstrate the continuity of the latent material space, and its ability to generate materials exhibiting target property values. The DING architecture proposed in this paper can be extended to other properties based on which the chemical space can be efficiently explored for interesting materials/molecules. Chemical space ; Robustness (evolution) ; Computer science ; Generator (circuit theory) ; Deep learning ; Artificial intelligence ; Representation (politics) ; Artificial neural network ; Biological system ; Machine learning ; Chemistry ; Power (physics) ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: I. A. Balyakin | A. A. Rempel | И. А. Балякин | А. А. Ремпель
Source: Electronic scientific archive of UrFU (Ural Federal University) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

MOLECULAR DYNAMICS OF HIGH ENTROPY ALLOYS USING ARTIFICIAL NEURAL NETWORKS Atomistic simulation of multi-component melt using high-dimensional neural network potential was performed using classical molecular dynamics. Ab initio and neural network simulations were compared. Parameters of solidification of the multi-component melt into high-entropy alloy were computed. Artificial neural network ; Molecular dynamics ; Ab initio ; Alloy ; Entropy (arrow of time) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Solidification and crystal growth phenomena
Authors: Obaidur Rahaman | Alessio Gagliardi
Source: ChemRxiv (preprint, 2020-06-30)
Field: Materials Science — Materials Chemistry

Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints The ability to predict material properties without the need of resource consuming experimental efforts can immensely accelerate material and drug discovery. Although ab initio methods can be reliable and accurate in making such predictions, they are computationally too expensive at a large scale. The recent advancements in artificial intelligence and machine learning as well as availability of large quantum mechanics derived datasets enable us to train models on these datasets as benchmark and to make fast predictions on much larger datasets. The success of these machine learning models highly depends on the machine-readable fingerprints of the molecules that capture their chemical properties as well as topological information. In this work we propose a common deep learning based framework to combine different types of molecular fingerprints to enhance prediction accuracy. Graph Neural Network (GNN), Many Body Tensor Representation (MBTR) and a set of simple Molecular Descriptors (MD) were used to predict the total energies, Highest Occupied Molecular Orbital (HOMO) energies and Lowest Unoccupied Molecular Orbital (LUMO) energies of a dataset containing ~62k large organic molecules with complex aromatic rings and remarkably diverse functional groups. The results demonstrate that a combination of best performing molecular fingerprints can produce better results than the individual ones. The simple and flexible deep learning framework developed in this work can be easily adapted to incorporate other types of molecular fingerprints. Computer science ; Molecular graph ; Benchmark (surveying) ; Artificial intelligence ; HOMO/LUMO ; Molecular descriptor ; Representation (politics) ; Ab initio ; Molecular orbital ; Machine learning ; Deep learning ; Artificial neural network ; Graph ; Molecule ; Theoretical computer science ; Physics ; Quantitative structure–activity relationship ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Keith T. Butler
Source: ePrints Soton (University of Southampton) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: Interpretable machine learning for materials design and characterization In a plenary lecture at a recent international conference, one leading researcher in theoretical chemistry remarked "at least 50% of the machine learning papers I see regarding electronic structure theory are junk, and do not meet the minimal standards of scientific publication", specifically referring to the lack of insight in many publications applying ML in that field. But is knowledge inevitably lost in machine learning studies, if not how can it be extracted and how does this apply to machine learning in the context of materials science? In this talk I will look at how we can open up black box machine learning models, to understand the results and gain confidence in predictions. I will present topical examples from designing new dielectric crystals, understanding inelastic neutron scattering data and trusting deep neural networks for tomographic reconstruction. By understanding how and why these models work, we can trust the results and even discover new physical relationships. Artificial intelligence ; Machine learning ; Computer science ; Context (archaeology) ; Field (mathematics) ; Deep learning ; Data science ; Artificial neural network ; Black box Machine Learning in Materials Science
Authors: Kohta Koenuma | Akinori Yamanaka | Ikumu Watanabe | Toshihiko Kuwabara
Source: Institutional Repositories DataBase (IRDB) (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Estimation of Texture-Dependent Stress-Strain Curve and <i>r</i>-Value of Aluminum Alloy Sheet Using Deep Learning Deformation of an aluminum alloy sheet which is affected by its underlying crystallographic texture has been widely studied using crystal plasticity finite element method (CPFEM). Numerical material test based on the CPFEM allows us to quantitatively estimate stress-strain curve and the Lankford value (r-value) of aluminum alloy sheets that depend on the texture. However, to utilize the numerical material test as a means of optimizing the texture to design aluminum alloys, the CPFEM is computationally expensive. In this study, we propose a methodology for rapidly estimating the stress-strain curve and r-value of aluminum alloy sheets using the deep learning with a neural network. We train the neural network with synthetic texture and stress-strain curves calculated by the numerical material test. To capture the feature of synthetic texture from an {111} pole figure image, the neural network incorporates the convolution neural network. Using the trained neural network, we can estimate the uniaxial stress-strain curve and the in-plane anisotropy of r-value for various textures which contain Cube and S components. The results demonstrate that the neural network trained with the results of the numerical material test is a promising methodology for rapidly estimating the deformation of aluminum alloy sheets. Deep drawing ; Aluminium ; Deep learning ; Alloy ; Curve fitting Machine Learning in Materials Science ; Metal Forming Simulation Techniques ; Model Reduction and Neural Networks
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: ChemRxiv (preprint, 2020-01-08)
Field: Materials Science — Materials Chemistry

Combining Automated Microfluidic Experimentation with Machine Learning for Efficient Polymerization Design Understanding polymerization reactions has challenges relating to the complexity of the systems, hazards associated with the reagents, environmental footprint of the operations, and the highly non-linear topologies of reaction spaces. In this work, we aim to present a new methodology for studying such complex reactions using machine-learning-assisted automated microchemical reactors. A custom-designed rapidly prototyped microreactor is used in conjunction with in situ infrared thermography and efficient, high-speed experimentation to map the reaction space for a zirconocene polymerization catalyst. Chemical waste was decreased by two orders of magnitude and catalytic discovery was performed in one hour. Here we show that efficient microfluidic technology can be coupled with machine learning algorithms to obtain high-fidelity datasets on a complex chemical reaction. Microreactor ; Polymerization ; Computer science ; Microfluidics ; Process engineering ; Reagent ; Catalysis ; Materials science ; Nanotechnology ; Chemistry ; Polymer ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-03-11)
Field: Materials Science — Materials Chemistry

Combining automated microfluidic experimentation with machine learning for efficient polymerization design Understanding polymerization reactions has challenges relating to the complexity of the systems, hazards associated with the reagents, environmental footprint of the operations, and the highly non-linear topologies of reaction spaces. In this work, we aim to present a new methodology for studying polymerization reactions using machine-learning-assisted automated microchemical reactors. A custom-designed rapidly prototyped microreactor is used in conjunction with automation and <em>in situ</em> infrared thermography for efficient, high-speed experimentation to map the reaction space of a zirconocene polymerization catalyst and obtain fundamental kinetic parameters. Chemical waste is decreased by two orders of magnitude and catalytic discovery is reduced from weeks to hours. Bayesian regularization backpropagation is used in conjunction with kinetic modelling to understand reaction space and resultant techno-economic topology. Here we show that efficient microfluidic technology can be coupled with machine learning algorithms to obtain high-fidelity datasets on a complex chemical reaction. Microfluidics ; Computer science ; Polymerization ; Process engineering ; Nanotechnology ; Materials science ; Engineering ; Polymer Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-02-05)
Field: Materials Science — Materials Chemistry

Combining automated microfluidic experimentation with machine learning for efficient polymerization design Understanding polymerization reactions has challenges relating to the complexity of the systems, hazards associated with the reagents, environmental footprint of the operations, and the highly non-linear topologies of reaction spaces. In this work, we aim to present a new methodology for studying such complex reactions using machine-learning-assisted automated microchemical reactors. A custom-designed rapidly prototyped microreactor is used in conjunction with automation and <em>in situ</em> infrared thermography for efficient, high-speed experimentation to map the reaction space of a zirconocene polymerization catalyst and obtain fundamental kinetic parameters. Chemical waste was decreased by two orders of magnitude and catalytic discovery was reduced from weeks to hours. Bayesian regularization backpropagation was used in conjunction with kinetic modeling to understand reaction space and resultant techno-economic topology. Here we show that efficient microfluidic technology can be coupled with machine learning algorithms to obtain high-fidelity datasets on a complex chemical reaction. Microfluidics ; Computer science ; Artificial intelligence ; Machine learning ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-02-05)
Field: Materials Science — Materials Chemistry

Combining automated microfluidic experimentation with machine learning for efficient polymerization design Understanding polymerization reactions has challenges relating to the complexity of the systems, hazards associated with the reagents, environmental footprint of the operations, and the highly non-linear topologies of reaction spaces. In this work, we aim to present a new methodology for studying such complex reactions using machine-learning-assisted automated microchemical reactors. A custom-designed rapidly prototyped microreactor is used in conjunction with automation and <em>in situ</em> infrared thermography for efficient, high-speed experimentation to map the reaction space of a zirconocene polymerization catalyst and obtain fundamental kinetic parameters. Chemical waste was decreased by two orders of magnitude and catalytic discovery was reduced from weeks to hours. Bayesian regularization backpropagation was used in conjunction with kinetic modeling to understand reaction space and resultant techno-economic topology. Here we show that efficient microfluidic technology can be coupled with machine learning algorithms to obtain high-fidelity datasets on a complex chemical reaction. Microfluidics ; Polymerization ; Computer science ; Process engineering ; Artificial intelligence ; Nanotechnology ; Materials science ; Engineering ; Composite material ; Polymer Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-02-05)
Field: Materials Science — Materials Chemistry

Combining automated microfluidic experimentation with machine learning for efficient polymerization design Understanding polymerization reactions has challenges relating to the complexity of the systems, hazards associated with the reagents, environmental footprint of the operations, and the highly non-linear topologies of reaction spaces. In this work, we aim to present a new methodology for studying such complex reactions using machine-learning-assisted automated microchemical reactors. A custom-designed rapidly prototyped microreactor is used in conjunction with automation and <em>in situ</em> infrared thermography for efficient, high-speed experimentation to map the reaction space of a zirconocene polymerization catalyst and obtain fundamental kinetic parameters. Chemical waste was decreased by two orders of magnitude and catalytic discovery was reduced from weeks to hours. Bayesian regularization backpropagation was used in conjunction with kinetic modeling to understand reaction space and resultant techno-economic topology. Here we show that efficient microfluidic technology can be coupled with machine learning algorithms to obtain high-fidelity datasets on a complex chemical reaction. Microfluidics ; Polymerization ; Computer science ; Process engineering ; Nanotechnology ; Materials science ; Engineering ; Polymer Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-02-05)
Field: Materials Science — Materials Chemistry

Combining automated microfluidic experimentation with machine learning for efficient polymerization design Understanding polymerization reactions has challenges relating to the complexity of the systems, hazards associated with the reagents, environmental footprint of the operations, and the highly non-linear topologies of reaction spaces. In this work, we aim to present a new methodology for studying such complex reactions using machine-learning-assisted automated microchemical reactors. A custom-designed rapidly prototyped microreactor is used in conjunction with automation and <em>in situ</em> infrared thermography for efficient, high-speed experimentation to map the reaction space of a zirconocene polymerization catalyst and obtain fundamental kinetic parameters. Chemical waste was decreased by two orders of magnitude and catalytic discovery was reduced from weeks to hours. Bayesian regularization backpropagation was used in conjunction with kinetic modeling to understand reaction space and resultant techno-economic topology. Here we show that efficient microfluidic technology can be coupled with machine learning algorithms to obtain high-fidelity datasets on a complex chemical reaction. Microfluidics ; Computer science ; Polymerization ; Process engineering ; Nanotechnology ; Materials science ; Engineering ; Polymer Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Chuhong Wang | Koutarou Aoyagi | Tim Mueller
Source: ECS Meeting Abstracts (article, 2020-11-23)
Field: Materials Science — Surfaces, Coatings and Films

Computational Design of Cathode Coating Materials for All-Solid-State Lithium Ion Batteries with on-the-Fly Machine Learning All-solid-state lithium-ion batteries have attracted significant research interest in recent years. Solid-state electrolytes have emerged as an appealing alternative to flammable organic liquid electrolytes, with the potential to accommodate higher-voltage cathode materials and a metallic lithium anode. Despite the discoveries of solid electrolyte materials exhibiting superionic conductivities, capacity loss and rate performance degradation has been commonly observed due to the prevailing issue of electrolyte/electrode interface reactivity, which originates from abrupt electrochemical potential changes at the electrode-electrolyte interface. The cycling stability of solid-state lithium ion batteries can be improved through the use of interfacial coating materials, but computationally identifying materials with sufficiently high lithium-ion conductivity can be challenging. Methods such as ab initio molecular dynamics that work well for superionic conductors can be prohibitively expensive when used on materials that conduct lithium ions less well but are still suitable for use as interfacial coatings. We demonstrate a way to address this problem using machine-learned interatomic potentials models in the form of moment tensor potentials. To prevent the potentials from significantly deviating from density functional theory (DFT) calculations we use molecular dynamics simulations coupled with on-the-fly machine learning. The efficiency of the calculations is increased by seven orders of magnitude compared to pure DFT, allowing for significantly improved prediction accuracy relative to experimental benchmarks. Using this approach, we have identified two particularly promising materials, Li 3 B 7 O 12 and Li 3 Sc 2 (PO 4 ) 3 , that are predicted to enhance long-term cyclability. The top candidate, Li 3 B 7 O 12 , has outstanding interface stability and to our knowledge has not previously been investigated as a lithium-ion conductor. Electrolyte ; Lithium (medication) ; Materials science ; Anode ; Cathode ; Fast ion conductor ; Ion ; Electrochemistry ; Density functional theory ; Coating ; Chemical physics ; Electrode ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Organic chemistry Electron and X-Ray Spectroscopy Techniques
Authors: Fabio Le Piane | Matteo Baldoni | Francesco Mercuri
Source: arXiv (Cornell University) (preprint, 2020-07-29)
Field: Materials Science — Materials Chemistry

Predicting the properties of molecular materials: multiscale simulation workflows meet machine learning Machine Learning tools are nowadays widely applied extensively to the prediction of the properties of molecular materials, using datasets extracted from high-throughput computational models. In several cases of scientific and technological relevance, the properties of molecular materials are related to the link between molecular structure and phenomena occurring across a wide set of spatial scales, from the nanoscale to the macroscale. Here, we describe an approach for predicting the properties of molecular aggregates based on multiscale simulations and machine learning. Workflow ; Computer science ; Relevance (law) ; Molecular dynamics ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Chemistry ; Database ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Nathan C. Frey
Source:  (preprint, 2020-03-27)
Field: Materials Science — Materials Chemistry

Rational design of 2D magnetic materials and synthesis predictions with machine learning Recent experimental success in the realization of two-dimensional (2D) magnetism has invigorated the search for low-dimensional material systems with tunable magnetic anisotropy that exhibit intrinsic long-range ferromagnetic order. Here, we present a rational design approach for studying and engineering magnetism in 2D transition metal carbides and nitrides (MXenes). Using a crystal field theory model and first-principles simulations, we demonstrate intrinsic ferromagnetism, high magnetic moments, high Curie temperatures, and intrinsic semiconducting and half-metallic transport behavior in nitride MXenes. We report that modifying the surface termination and transition metal in monolayer nitride MXenes gives rise to a rich diversity of noncollinear spin structures and finely tunable magnetic anisotropy. We predict that manipulating the strength of the spin-orbit interaction and electron localization via the chemical degrees of freedom can induce sufficient anisotropy to counteract thermal fluctuations that suppress long-range magnetic order. Further, surface engineering and applied electric fields enable robust switching and stabilization of magnetic behavior in MXenes. Increasingly, the bottleneck in realizing new 2D systems with exotic properties is the synthesis of these materials. We discuss our adaptation of the positive and unlabeled (PU) machine learning method to predict which theoretically proposed 2D materials have the highest likelihood of being successfully synthesized. We identify 18 new MXene compounds that are highly promising candidates for synthesis. By considering both the MXenes and their layered precursors, we further propose 20 new synthesizable MAX phases that can be chemically exfoliated to produce new MXenes.&lt;br/&gt; Computer science ; Artificial intelligence Machine Learning in Materials Science