jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rajesh Jha | George S. Dulikravich
Source: Metals (article, 2020-12-24)
Field: Materials Science — Materials Chemistry

Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence In this work, we studied a Ti-Nb-Zr-Sn system for exploring novel composition and temperatures that will be helpful in maximizing the stability of β phase while minimizing the formation of α” and ω-phase. The Ti-Nb-Zr-Sn system is free of toxic elements. This system was studied under the framework of CALculation of PHAse Diagram (CALPHAD) approach for determining the stability of various phases. These data were analyzed through artificial intelligence (AI) algorithms. Deep learning artificial neural network (DLANN) models were developed for various phases as a function of alloy composition and temperature. Software was written in Python programming language and DLANN models were developed utilizing TensorFlow/Keras libraries. DLANN models were used to predict various phases for new compositions and temperatures and provided a more complete dataset. This dataset was further analyzed through the concept of self-organizing maps (SOM) for determining correlations between phase stability of various phases, chemical composition, and temperature. Through this study, we determined candidate alloy compositions and temperatures that will be helpful in avoiding/minimizing formation of α” and ω-phase in a Ti-Zr-Nb-Sn system. This approach can be utilized in other systems such as ω-free shape memory alloys. DLANN models can even be used on a common Android mobile phone. CALPHAD ; Python (programming language) ; Phase diagram ; Artificial neural network ; Computer science ; Materials science ; Alloy ; Phase (matter) ; Artificial intelligence ; Thermodynamics ; Metallurgy ; Chemistry ; Physics ; Programming language Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Machine Learning in Materials Science
Authors: Kangyu Zhang | Lichang Yin | Gang Liu
Source: Computational Materials Science (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

Physically inspired atom-centered symmetry functions for the construction of high dimensional neural network potential energy surfaces Artificial neural network ; Atom (system on chip) ; Intuition ; Molecular dynamics ; Potential energy ; Symmetry (geometry) ; Computer science ; Statistical physics ; Molecule ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Artificial intelligence ; Atomic physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Fuel Cells and Related Materials
Authors: Motomichi Tashiro | Yutaka Imamura | Michio Katouda
Source: Journal of Computational Chemistry (article, 2020-10-26)
Field: Materials Science — Materials Chemistry

De novo generation of optically active small organic molecules using Monte Carlo tree search combined with recurrent neural network Optically active small organic molecules are computationally designed using the ChemTS python library developed by Tsuda and collaborators, which utilizes a combined Monte Carlo tree search (MCTS) and recurrent neural network model. Geometry optimization and excited-state calculations are performed for each generated molecule, following which the excitation energy and dissymmetry factors are computed to evaluate the score function in the MCTS process. Using this procedure, molecules not contained in existing databases are generated. Molecules having either high dissymmetry factors or high transition dipole strengths can be generated depending on the choice of the score function. In a single trajectory with 100,000 trials, mutually similar high-scoring molecules are generated frequently after the initial 15,000-20,000 trials. This indicates that it is better to sample high-scoring molecules from several trajectories having a modest number of trials each than from a single trajectory having a large number of trials. Monte Carlo method ; Monte Carlo tree search ; Python (programming language) ; Computer science ; Tree (set theory) ; Artificial neural network ; Organic molecules ; Dipole ; Statistical physics ; Recurrent neural network ; Molecule ; Artificial intelligence ; Algorithm ; Physics ; Mathematics ; Statistics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Manareldeen Ahmed | Yan Li | Wenchao Chen | Er‐Ping Li
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Diffusion Barrier Prediction of Graphene and Boron Nitride for Copper Interconnects by Deep Learning The continuous scaling-down size of interconnects should be accompanied with ultra-thin diffusion barrier layers, which is used to suppress Cu diffusion into the dielectrics. Unfortunately, conventional barrier layers with thicknesses less than 4 nm fail to perform well. With the advent of 2D layered materials, graphene and hexagonal boron nitride have been proposed as alternative Cu diffusion barriers with thicknesses of ≈1 nm. However, defects such as vacancies may evolve into a Cu diffusion path, which is a challenging problem in design of diffusion barrier layers. The energy barrier of Cu atom diffused through a di-vacancy defect in graphene and hexagonal boron nitride is calculated by density functional theory. It is found that graphene offers higher energy barrier to Cu than hexagonal boron nitride. The higher energy barrier is attributed to the stronger interaction between Cu and C atoms in graphene as shown by charge density difference and Bader’s charge. Furthermore, we use the energy barriers of different vacancy structures and generate a dataset that will be used for machine learning. Our trained convolutional neural network is used to predict the energy barrier of Cu migration through randomly configured defected graphene and hexagonal boron nitride with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of >99% for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> supercell. These results provide guides on choosing between 2D materials as barrier layers, and applying deep learning to predict the 2D barrier performance. Graphene ; Diffusion barrier ; Materials science ; Boron nitride ; Diffusion ; Vacancy defect ; Boron ; Nanotechnology ; Condensed matter physics ; Physics ; Quantum mechanics Graphene research and applications ; Semiconductor materials and devices ; Copper Interconnects and Reliability
Authors: Zhi Lu | Lin Zhang | Dong M. Fan | Nianmin Yao | Chun X. Zhang
Source: Chemometrics and Intelligent Laboratory Systems (article, 2020-03-02)
Field: Materials Science — Materials Chemistry

Crystal texture recognition system based on image analysis for the analysis of agglomerates Agglomerate ; Texture (cosmology) ; Artificial intelligence ; Crystallization ; Materials science ; Biological system ; Artificial neural network ; Pattern recognition (psychology) ; Computer science ; Crystal (programming language) ; Computer vision ; Image (mathematics) ; Chemical engineering ; Composite material ; Engineering Crystallization and Solubility Studies ; Spectroscopy and Chemometric Analyses ; Image Processing Techniques and Applications
Authors: Alberto Fabrizio | Ksenia R. Briling | Andrea Grisafi | Clémence Corminbœuf
Source: CHIMIA International Journal for Chemistry (article, 2020-04-25)
Field: Materials Science — Materials Chemistry

Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity Machine-learning in quantum chemistry is currently booming, with reported applications spanning all molecular properties from simple atomization energies to complex mathematical objects such as the many-body wavefunction. Due to its central role in density functional theory, the electron density is a particularly compelling target for non-linear regression. Nevertheless, the scalability and the transferability of the existing machine-learning models of ρ(r) are limited by its complex rotational symmetries. Recently, in collaboration with Ceriotti and coworkers, we combined an efficient electron density decomposition scheme with a local regression framework based on symmetry-adapted Gaussian process regression able to accurately describe the covariance of the electron density spherical tensor components. The learning exercise is performed on local environments, allowing high transferability and linear-scaling of the prediction with respect to the number of atoms. Here, we review the main characteristics of the model and show its predictive power in a series of applications. The scalability and transferability of the trained model are demonstrated through the prediction of the electron density of Ubiquitin. Transferability ; Density functional theory ; Electron density ; Gaussian ; Gaussian process ; Scaling ; Statistical physics ; Covariance ; Computer science ; Scalability ; Computational chemistry ; Artificial intelligence ; Machine learning ; Chemistry ; Physics ; Electron ; Mathematics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Various Chemistry Research Topics
Authors: Zhihui Liu | Tao-Tao Shi | Zhao‐Xu Chen
Source: Chemical Physics Letters (article, 2020-07-08)
Field: Materials Science — Materials Chemistry

Machine learning prediction of monatomic adsorption energies with non-first-principles calculated quantities Monatomic ion ; Adsorption ; Density functional theory ; Atom (system on chip) ; Work (physics) ; Monatomic gas ; Thermodynamics ; Chemistry ; Binary number ; Metal ; Materials science ; Computational chemistry ; Physical chemistry ; Physics ; Computer science ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Janani Sampath | Sarah Alamdari | Jim Pfaendtner
Source: Chemistry of Materials (article, 2020-08-17)
Field: Materials Science — Biomaterials

Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution Molecular self-assembly is a powerful tool in materials design, wherein noncovalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable, and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly—biomineralizing peptides, hierarchical peptoids, and large protein assemblies—and will aid in the development of a new synthesis science for achieving precise molecular control in materials design. Biomolecule ; Nanotechnology ; Supramolecular chemistry ; Non-covalent interactions ; Computer science ; Biomolecular structure ; van der Waals force ; Molecular recognition ; Materials science ; Hydrogen bond ; Chemistry ; Molecule ; Protein structure Diatoms and Algae Research ; Machine Learning in Materials Science ; Surface Chemistry and Catalysis
Authors: Wai Shun Mak | Xiaokang Wang | Rigoberto Arenas | Youtian Cui | Steve J. Bertolani | Wen Qiao Deng | Ilias Tagkopoulos | David K. Wilson | Justin B. Siegel
Source: Biochemistry (article, 2020-09-16)
Field: Chemistry — Inorganic Chemistry

Discovery, Design, and Structural Characterization of Alkane-Producing Enzymes across the Ferritin-like Superfamily To complement established rational and evolutionary protein design approaches, significant efforts are being made to utilize computational modeling and the diversity of naturally occurring protein sequences. Here, we combine structural biology, genomic mining, and computational modeling to identify structural features critical to aldehyde deformylating oxygenases (ADOs), an enzyme family that has significant implications in synthetic biology and chemoenzymatic synthesis. Through these efforts, we discovered latent ADO-like function across the ferritin-like superfamily in various species of Bacteria and Archaea. We created a machine learning model that uses protein structural features to discriminate ADO-like activity. Computational enzyme design tools were then utilized to introduce ADO-like activity into the small subunit of <i>Escherichia coli</i> class I ribonucleotide reductase. The integrated approach of genomic mining, structural biology, molecular modeling, and machine learning has the potential to be utilized for rapid discovery and modulation of functions across enzyme families. Computational biology ; Ribonucleotide reductase ; Synthetic biology ; Structural biology ; Archaea ; Protein engineering ; Biology ; Protein design ; Structural genomics ; Enzyme ; SUPERFAMILY ; Protein subunit ; Protein structure ; Biochemistry ; Gene Metal-Catalyzed Oxygenation Mechanisms ; Microbial Metabolic Engineering and Bioproduction ; Bacterial Genetics and Biotechnology
Authors: Christian Feiler | Di Mei | Bérengère Luthringer-Feyerabend | Sviatlana V. Lamaka | Mikhail L. Zheludkevich
Source: CORROSION (article, 2020-07-27)
Field: Materials Science — Biomaterials

Rational Design of Effective Mg Degradation Modulators Prerequisite to unlock the full potential of Mg-based materials is to gain control of their degradation properties. Here a proof of concept is presented for an efficient and robust alternative to the data-driven machine learning approaches that are currently on the rise to facilitate the discovery of corrosion modulating agents. The electronic properties of bipyridine were tuned by its substitution with electron donating and electron withdrawing functional groups to regulate the degradation modulators interaction with different ions and the effect on the corrosion inhibition of pure Mg was predicted based on density functional theory calculations. Bipyridine and two of its derivatives were subsequently investigated experimentally to validate the trend predicted by the quantum chemical calculations. Degradation (telecommunications) ; Density functional theory ; Corrosion ; Rational design ; Ion ; Materials science ; Computational chemistry ; Bipyridine ; Quantum chemical ; Combinatorial chemistry ; Chemical physics ; Nanotechnology ; Chemistry ; Computer science ; Molecule ; Organic chemistry ; Composite material Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; MXene and MAX Phase Materials
Authors: Meng‐Huan Jao | Shun‐Hsiang Chan | Ming‐Chung Wu | Chao‐Sung Lai
Source: The Journal of Physical Chemistry Letters (article, 2020-10-06)
Field: Materials Science — Materials Chemistry

Element Code from Pseudopotential as Efficient Descriptors for a Machine Learning Model to Explore Potential Lead-Free Halide Perovskites The rapid development of machine learning has proven its potential in material science. To acquire an accurate and promising result, the choice of descriptor plays an essential role in dictating the model performance. In this work, we introduce a set of novel descriptors, Element Code, which is generated from pseudopotential. Using a variational autoencoder to perform unsupervised learning, the produced Element Code is verified to contain representative information on elements. Attributed to the successful extraction of information from pseudopotential, Element Code can serve as the primary descriptor for the machine learning model. We construct a model using Element Code as the sole descriptor to predict the bandgap of a lead-free double halide perovskite, and an accuracy of 0.951 and mean absolute error of 0.266 eV are achieved. We believe our work can offer insights into selecting lead-free halide perovskites and establish a paradigm of exploring new materials. Autoencoder ; Pseudopotential ; Element (criminal law) ; Code (set theory) ; Halide ; Computer science ; Artificial intelligence ; Lead (geology) ; Algorithm ; Machine learning ; Set (abstract data type) ; Deep learning ; Physics ; Chemistry ; Condensed matter physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Brendan C. Sweeny | Hanqing Pan | Asmaa Kassem | Jordan C. Sawyer | Shaun G. Ard | Nicholas S. Shuman | Albert A. Viggiano | Sebastian Brickel | Oliver T. Unke | Meenu Upadhyay | Markus Meuwly
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Thermal activation of methane by MgO<sup>+</sup>: temperature dependent kinetics, reactive molecular dynamics simulations and statistical modeling The kinetics of MgO+ + CH4 was studied experimentally using the variable ion source, temperature adjustable selected ion flow tube (VISTA-SIFT) apparatus from 300-600 K and computationally by running and analyzing reactive atomistic simulations. Rate coefficients and product branching fractions were determined as a function of temperature. The reaction proceeded with a rate of k = 5.9 ± 1.5 × 10-10(T/300 K)-0.5±0.2 cm3 s-1. MgOH+ was the dominant product at all temperatures, but Mg+, the co-product of oxygen-atom transfer to form methanol, was observed with a product branching fraction of 0.08 ± 0.03(T/300 K)-0.8±0.7. Reactive molecular dynamics simulations using a reactive force field, as well as a neural network trained on thousands of structures yield rate coefficients about one order of magnitude lower. This underestimation of the rates is traced back to the multireference character of the transition state [MgOCH4]+. Statistical modeling of the temperature-dependent kinetics provides further insight into the reactive potential surface. The rate limiting step was found to be consistent with a four-centered activation of the C-H bond, in agreement with previous calculations. The product branching was modeled as a competition between dissociation of an insertion intermediate directly after the rate-limiting transition state, and traversing a transition state corresponding to a methyl migration leading to a Mg-CH3OH+ complex, though only if this transition state is stabilized significantly relative to the dissociated MgOH+ + CH3 product channel. An alternative, non-statistical mechanism is discussed, whereby a post-transition state bifurcation in the potential surface could allow the reaction to proceed directly from the four-centered TS to the Mg-CH3OH+ complex thereby allowing a more robust competition between the product channels. Kinetics ; Transition state theory ; Branching (polymer chemistry) ; Dissociation (chemistry) ; Molecular dynamics ; Transition state ; Ion ; Chemical kinetics ; Branching fraction Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Advanced Chemical Physics Studies
Authors: Frédéric Delbecq | Guillaume Adenier | Yuki Ogue | Takeshi Kawai
Source: Journal of Molecular Liquids (article, 2020-01-27)
Field: Materials Science — Biomaterials

Gelation properties of various long chain amidoamines: Prediction of solvent gelation via machine learning using Hansen solubility parameters Lamellar structure ; Alkyl ; Solubility ; Supramolecular chemistry ; Solvent ; Steric effects ; Amphiphile ; Rheology ; Amide ; Hildebrand solubility parameter ; Molecule ; Chemical engineering ; Crystallinity ; Materials science ; Aqueous solution ; Chemistry ; Polymer chemistry ; Crystallography ; Organic chemistry ; Polymer ; Composite material Supramolecular Self-Assembly in Materials ; Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions
Authors: Alberto Fabrizio | Benjamin Meyer | Clémence Corminbœuf
Source: The Journal of Chemical Physics (article, 2020-04-17)
Field: Materials Science — Materials Chemistry

Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory. Related to the lack of derivative discontinuity in approximate exchange-correlation potentials, the information about the curvature has been successfully used to restore the physical meaning of Kohn-Sham orbital eigenvalues and to develop non-empirical tuning and correction schemes for density functional approximations. In this work, we propose the construction of a machine-learning framework targeting the average energy curvature between the neutral and the radical cation state of thousands of small organic molecules (QM7 database). The applicability of the model is demonstrated in the context of system-specific gamma-tuning of the LC-ωPBE functional and validated against the molecular first ionization potentials at equation-of-motion coupled-cluster references. In addition, we propose a local version of the non-linear regression model and demonstrate its transferability and predictive power by determining the optimal range-separation parameter for two large molecules relevant to the field of hole-transporting materials. Finally, we explore the underlying structure of the QM7 database with the t-SNE dimensionality-reduction algorithm and identify structural and compositional patterns that promote the deviation from the piecewise linearity condition. Curvature ; Density functional theory ; Range (aeronautics) ; Context (archaeology) ; Statistical physics ; Applied mathematics ; Computer science ; Mathematics ; Algorithm ; Physics ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions