jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin Stöhr | Leonardo Medrano Sandonas | Alexandre Tkatchenko
Source: The Journal of Physical Chemistry Letters (preprint, 2020-07-30)
Field: Materials Science — Materials Chemistry

Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks We combine density-functional tight binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to construct a nonlinear model for the localized many-body interatomic repulsive energy, which so far has been treated in an atom-pairwise manner in DFTB. Substantially improving upon standard DFTB and DTNN, the resulting DFTB-NN<sub>rep</sub> model yields accurate predictions of atomization and isomerization energies, equilibrium geometries, vibrational frequencies, and dihedral rotation profiles for a large variety of organic molecules compared to the hybrid DFT-PBE0 functional. Our results highlight the potential of combining semiempirical electronic-structure methods with physically motivated machine learning approaches for predicting localized many-body interactions. We conclude by discussing future advancements of the DFTB-NN<sub>rep</sub> approach that could enable chemically accurate electronic-structure calculations for systems with tens of thousands of atoms. Tight binding ; Dihedral angle ; Tensor (intrinsic definition) ; Pairwise comparison ; Density functional theory ; Electronic structure ; Atom (system on chip) ; Artificial neural network ; Statistical physics ; Physics ; Chemistry ; Chemical physics ; Computer science ; Computational chemistry ; Molecule ; Artificial intelligence ; Quantum mechanics ; Hydrogen bond ; Mathematics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Advanced Chemical Physics Studies
Authors: Josè Carlos Madrid Madrid | Kulbir Kaur Ghuman
Source: Advances in Physics X (article, 2020-12-07)
Field: Materials Science — Materials Chemistry

Disorder in energy materials and strategies to model it The functionality of the materials used for energy applications is critically determined by the physical properties of small active regions such as dopants, dislocations, interfaces, grain boundaries, etc. The capability to manipulate and utilize the inevitable disorder in materials, whether due to the finite-dimensional defects (such as vacancies, dopants, grain boundaries) or due to the complete atomic randomness (as in amorphous materials), can bring innovation in designing energy materials. With the increase in computational material science capabilities, it is now possible to understand the complexity present in materials due to various degrees of disorder resulting in pathways required for optimizing their efficiencies. This article provides a critical overview of such computational advancements specifically for designing realistic materials with various types of disorders for sustainable energy applications such as catalysts and electrochemical devices. The ultimate goal is to gain a thorough knowledge of the traditional approaches (implemented via tools such as density functional theory, and molecular dynamics) as well as modern approaches such as machine learning that exist for modeling the disorder present in materials, thereby identify the future opportunities for energy materials design and discovery. Randomness ; Dopant ; Nanotechnology ; Materials science ; Computer science ; Energy (signal processing) ; Doping ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Anthony Chazirakis | Vassia Kirieri | Ilias-Marios Sarris | Evangelia Kalligiannaki | Vagelis Harmandaris
Source: Procedia Computer Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Neural Network Potential Surfaces: A Comparison of two Approaches Molecular dynamics simulations often involve excessive degrees of freedom, and therefore the computational cost is prohibitively high. Coarse-graining high-dimensional molecular systems is a well-established procedure to increase the space- and time- scales accessible. The coarse-level potential energy must be estimated to derive correctly structural and dynamical properties of the reduced system. Here we examine the artificial neural networks as a potential tool to represent high-dimensional potential-energy surfaces. The simulated coarse configurations are fed to the neural network while taking care to represent each pseudo-atom's chemical environment properly. Then, the neural network is trained to reproduce the molecular energy. In this work, we present two approaches designed to preserve the physical symmetries of the coarse system. The first approach introduces symmetry functions and the second local coordinates. We implement these techniques, and we apply and compare them for two molecular systems, a bulk methane system, and a liquid water system. Computer science ; Granularity ; Molecular dynamics ; Artificial neural network ; Degrees of freedom (physics and chemistry) ; Work (physics) ; Potential energy ; Energy (signal processing) ; Potential energy surface ; Symmetry (geometry) ; Artificial intelligence ; Statistical physics ; Physics ; Classical mechanics Machine Learning in Materials Science ; Topic Modeling ; Protein Structure and Dynamics
Authors: Henry Chan | Badri Narayanan | Mathew J. Cherukara | Troy D. Loeffler | Michael Sternberg | Anthony Avarca | Subramanian K. R. S. Sankaranarayanan
Source: arXiv (Cornell University) (preprint, 2020-02-21)
Field: Materials Science — Materials Chemistry

BLAST: Bridging Length/time scales via Atomistic Simulation Toolkit The ever-increasing power of supercomputers coupled with highly scalable simulation codes have made molecular dynamics an indispensable tool in applications ranging from predictive modeling of materials to computational design and discovery of new materials for a broad range of applications. Multi-fidelity scale bridging between the various flavors of molecular dynamics i.e. ab-initio, classical and coarse-grained models has remained a long-standing challenge. Here, we introduce our framework BLAST (Bridging Length/time scales via Atomistic Simulation Toolkit) that leverages machine learning principles to address this challenge. BLAST is a multi-fidelity scale bridging framework that provide users with the capabilities to train and develop their own classical atomistic and coarse-grained interatomic potentials (force fields) for molecular simulations. BLAST is designed to address several long-standing problems in the molecular simulations community, such as unintended misuse of existing force fields due to knowledge gap between developers and users, bottlenecks in traditional force field development approaches, and other issues relating to the accuracy, efficiency, and transferability of force fields. Here, we discuss several important aspects in force field development and highlight features in BLAST that enable its functionalities and ease of use. Bridging (networking) ; Computer science ; Transferability ; Scalability ; Molecular dynamics ; Fidelity ; Force field (fiction) ; High fidelity ; Field (mathematics) ; Data science ; Artificial intelligence ; Physics ; Machine learning Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Parallel Computing and Optimization Techniques
Authors: Volker L. Deringer | Chris J. Pickard | Davide Μ. Proserpio
Source: Angewandte Chemie (article, 2020-06-04)
Field: Materials Science — Materials Chemistry

Hierarchically Structured Allotropes of Phosphorus from Data‐Driven Exploration Abstract The discovery of materials is increasingly guided by quantum‐mechanical crystal‐structure prediction, but the structural complexity in bulk and nanoscale materials remains a bottleneck. Here we demonstrate how data‐driven approaches can vastly accelerate the search for complex structures, combining a machine‐learning (ML) model for the potential‐energy surface with efficient, fragment‐based searching. We use the characteristic building units observed in Hittorf's and fibrous phosphorus to seed stochastic (“random”) structure searches over hundreds of thousands of runs. Our study identifies a family of hierarchically structured allotropes based on a P8 cage as principal building unit, including one‐dimensional (1D) single and double helix structures, nanowires, and two‐dimensional (2D) phosphorene allotropes with square‐lattice and kagome topologies. These findings yield new insight into the intriguingly diverse structural chemistry of phosphorus, and they provide an example for how ML methods may, in the long run, be expected to accelerate the discovery of hierarchical nanostructures. Phosphorene ; Bottleneck ; Black phosphorus ; Nanostructure ; Nanotechnology ; Materials science ; Nanoscopic scale ; Computer science ; Bridging (networking) ; Nanowire ; Chemical physics ; Chemistry Machine Learning in Materials Science ; 2D Materials and Applications ; Surface Chemistry and Catalysis
Authors: Jess Tate | Jeffery A. Aguiar | Matthew L. Gong | Tolga Taşdizen
Source: Microscopy and Microanalysis (article, 2020-07-28)
Field: Materials Science — Materials Chemistry

High Throughput Crystal Structure Classification Advances in imaging and experimental techniques have made materials science abundant, increasing the potential for new discoveries but requiring new techniques to trans-form the vast amount of data into information that can be readily interpreted by researchers. Data-driven models, specifically deep and machine learning, are one set of tools that have been applied with noticeable success to some burdensome materials and microscopy problems. However, the ultimate goal with these models has yet to be realized: to automatically process, characterized, and compressed the increasingly common high-resolution, multimodal imaging data on a single system. One crucial component of many workflows is classifying the crystal structure of a material, yet is mostly performed by trained expert who divine structural information from minute diffraction pattern variations. [1, 2, 3] The process of determining a crystal's space group often involves a lengthy process requiring fitting to a series of non-linear equations and intimate knowledge of a sample to be performed properly, including standardized approaches such as Rietveld refinement. [4, 5] The heavy dependence in complex matching, time intensive processes, potential for dimensionality reduction, and the vast amount of data generated makes crystal classification an ideal case for automation. Throughput ; Materials science ; Computer science X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Wei Li | ZigengWang | Xia Xiao | Zhiqiang Zhang | Anderson Janotti | Sanguthevar Rajasekaran | Bharat Medasani
Source: arXiv (Cornell University) (preprint, 2020-08-27)
Field: Materials Science — Materials Chemistry

Predicting band gaps and band-edge positions of oxide perovskites using DFT and machine learning Density functional theory within the local or semilocal density approximations (DFT-LDA/GGA) has become a workhorse in electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. Accurate prediction of band gaps using firstprinciples methods is time consuming, requiring hybrid functionals, quasi-particle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveiling the main chemical and structural factors involved in this correction is desirable for discovering novel materials in high-throughput calculations. In this direction, we use DFT and machine learning techniques to correct band gaps and band-edge positions of a representative subset of ABO3 perovskite oxides. Relying on results of HSE06 hybrid functional calculations as target values of band gaps, we find a systematic band gap correction of ~1.5 eV for this class of materials, where ~1 eV comes from downward shifting the valence band and ~0.5 eV from uplifting the conduction band. The main chemical and structural factors determining the band gap correction are determined through a feature selection procedure. Band gap ; Density functional theory ; Hybrid functional ; Semimetal ; Electronic band structure ; Semiconductor ; Quasi Fermi level ; Materials science ; Direct and indirect band gaps ; Local-density approximation ; Statistical physics ; Condensed matter physics ; Computational chemistry ; Physics ; Chemistry ; Optoelectronics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ga2O3 and related materials
Authors: Yuexing Han | Leilei Song | Bing Wang | Sheng Sun | Quan Qian | Qian Wang
Source: Modelling and Simulation in Materials Science and Engineering (article, 2020-01-20)
Field: Materials Science — Materials Chemistry

AtomicNet: a novel approach to identify the crystal structure of each simulated atom Abstract The study of crystal structure in the process of plastic deformation is essential to analyse the properties of materials and help to understand the deformation mechanism. With the rapid development of computer hardware and algorithms, great efforts have been paid for atomic simulation with computer science. However, most existing methods can only identify known structures in advance and consider unknown structure as unrecognized ones. In this work, we propose a method to identify crystal structure among atomistic simulations of crystalline materials. First, we develop a new characterization to describe each atom’s local space information, i.e. local spatial characteristics and utilize mutual information to simplify the characteristics. Then, the multi-layer perceptron neural network is used for the classification on the simplified characteristics. With the proposed method, we can not only identify the crystal structures of the surface of atoms, but also obtain the value of probability of each crystal structure. Our work provides significant insights for finding new mechanisms in structure transformation in material science field. Materials science ; Atom (system on chip) ; Crystallography ; Crystal (programming language) ; Crystal structure ; Computer science ; Parallel computing ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Gabriel S. Gusmão | Adhika P. Retnanto | Shashwati C. da Cunha | Andrew J. Medford
Source: arXiv (Cornell University) (preprint, 2020-11-30)
Field: Materials Science — Materials Chemistry

Kinetics-Informed Neural Networks Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. Here, we utilize feed-forward artificial neural networks as basis functions to solve ordinary differential equations (ODEs) constrained by differential algebraic equations (DAEs) that describe microkinetic models (MKMs). We present an algebraic framework for the mathematical description and classification of reaction networks, types of elementary reaction, and chemical species. Under this framework, we demonstrate that the simultaneous training of neural nets and kinetic model parameters in a regularized multi-objective optimization setting leads to the solution of the inverse problem through the estimation of kinetic parameters from synthetic experimental data. We analyze a set of scenarios to establish the extent to which kinetic parameters can be retrieved from transient kinetic data, and assess the robustness of the methodology with respect to statistical noise. This approach to inverse kinetic ODEs can assist in the elucidation of reaction mechanisms based on transient data. Ordinary differential equation ; Artificial neural network ; Ode ; Differential algebraic equation ; Computer science ; Robustness (evolution) ; Inverse problem ; Applied mathematics ; Algebraic number ; Differential equation ; Mathematical optimization ; Biological system ; Mathematics ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Mathematical Biology Tumor Growth
Authors: В. В. Андреев | Olga Andreeva | Vasiliy E. Gai | Maria Berberova
Source: E3S Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Determining the Resource of Safe Operation for Objects by Images In this paper, a systematic study of the microstructure damage process of metals and alloys was carried out. The main elements of the microstructure surface image, as well as the rules for the formation and interaction of rough slip traces and cracks to determine the model of damage accumulation on the image of the microstructure surface under cyclic loading are determined. A classifier that allows to determine the number of loading cycles before a sample goes out of service is proposed. A modernized structure of the convolutional neural network was developed to classify images of the damaged microstructure of the metals and alloys surface. The proposed classifier for determining the number of loading cycles made it possible to achieve a classification accuracy of 78.43%. Microstructure ; Convolutional neural network ; Materials science ; Classifier (UML) ; Artificial neural network ; Artificial intelligence ; Slip (aerodynamics) ; Computer science ; Pattern recognition (psychology) ; Geology ; Composite material ; Engineering ; Aerospace engineering Material Properties and Failure Mechanisms ; Industrial Engineering and Technologies ; Engineering Diagnostics and Reliability
Authors: Juan Manuel Carmona-Loaiza | Zamaan Raza
Source: iMPULSE (Heinz Maier-Leibnitz Zentrum) (preprint, 2020-10-15)
Field: Materials Science — Materials Chemistry

Towards Reflectivity profile inversion through Artificial Neural Networks The goal of Specular Neutron and X-ray Reflectometry is to infer materials Scattering Length Density (SLD) profiles from experimental reflectivity curves. This paper focuses on investigating an original approach to the ill-posed non-invertible problem which involves the use of Artificial Neural Networks (ANN). In particular, the numerical experiments described here deal with large data sets of simulated reflectivity curves and SLD profiles, and aim to assess the applicability of Data Science and Machine Learning technology to the analysis of data generated at neutron scattering large scale facilities. It is demonstrated that, under certain circumstances, properly trained Deep Neural Networks are capable of correctly recovering plausible SLD profiles when presented with never-seen-before simulated reflectivity curves. When the necessary conditions are met, a proper implementation of the described approach would offer two main advantages over traditional fitting methods when dealing with real experiments, namely, 1. sample physical models are described under a new paradigm: detailed layer-by-layer descriptions (SLDs, thicknesses, roughnesses) are replaced by parameter free curves $ρ(z)$, allowing a-priori assumptions to be fed in terms of the sample family to which a given sample belongs (e.g. "thin film", "lamellar structure", etc.) 2. the time-to-solution is shrunk by orders of magnitude, enabling faster batch analyses for large datasets. Artificial neural network ; Specular reflection ; Computer science ; Reflectivity ; Sample (material) ; Inversion (geology) ; A priori and a posteriori ; Reflectometry ; Neutron reflectometry ; Algorithm ; X-ray reflectivity ; Scattering ; Neutron scattering ; Invertible matrix ; Artificial intelligence ; Optics ; Mathematics ; Physics ; Geology ; Small-angle neutron scattering ; Computer vision Machine Learning in Materials Science ; Nuclear Physics and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Jan Schuetzke | Alexander Benedix | Ralf Mikut | Markus Reischl
Source:  (article, 2020-11-20)
Field: Materials Science — Materials Chemistry

Siamese Networks for 1D Signal Identification In material sciences, X-ray diffraction (XRD) or nuclear magnetic response (NMR) are methods to generate one-dimensional signals, describing intensities over an angle or a chemical shift. Each material has a characteristic profile and unknown samples are typically matched to known references. Automatic classification of one-dimensional signal patterns is a non-trivial task due to background noise and varying positions of measured intensities in identical probes. Convolutional Neural Networks prove to be particularly suitable, a limitation, though, is that adding new classes requires retraining. However, continuous discovery of new materials requires possibilities for easy classextension. Siamese Neural Networks are able to extend data set classes easily and are popular in the field of face recognition, where new faces are constantly added to the database of references. In this paper, we apply Siamese networks to one-dimensional XRD-data for the first time and discuss the opportunities and challenges as well as areas of application. We show that Siamese networks are well suited for the transfer between XRD datasets, achieving an accuracy of 99% for materials not present in the training dataset. Computer science ; Convolutional neural network ; Identification (biology) ; Artificial intelligence ; SIGNAL (programming language) ; Field (mathematics) ; Transfer of learning ; Pattern recognition (psychology) ; Face (sociological concept) ; Set (abstract data type) ; Noise (video) ; Retraining ; Artificial neural network ; Task (project management) ; Data set ; Training set ; Image (mathematics) ; Mathematics ; Engineering Machine Learning in Materials Science ; Handwritten Text Recognition Techniques ; X-ray Diffraction in Crystallography
Authors: Shoichi Ishida | Kei Terayama | Ryosuke Kojima | Kiyosei Takasu | Yasushi Okuno
Source: ChemRxiv (preprint, 2020-12-17)
Field: Materials Science — Materials Chemistry

AI-Driven Synthetic Route Design with Retrosynthesis Knowledge Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they exploit their experiments, intuition, and knowledge. Recent breakthroughs in machine learning techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human interventions. However, such CASP applications are yet to incorporate retrosynthesis knowledge sufficiently into their algorithms to reflect chemists' way of thinking flexibly. In this study, we developed a hybrid CASP application of data-driven techniques and various retrosynthesis knowledge called "ReTReK" that integrates the knowledge as adjustable parameters into an evaluation for promising search directions. Experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, and the results indicated that the synthetic routes searched with the knowledge were preferred to those without knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into data-driven CASP applications is expected to contribute to further their development and spread them to chemists widely. Retrosynthetic analysis ; CASP ; Computer science ; Intuition ; Artificial intelligence ; Machine learning ; Protein structure prediction ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering