jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ismaeel Ramzan | Linghan Kong | Richard A. Bryce | Neil A. Burton
Source: ChemRxiv (preprint, 2021-04-20)
Field: Materials Science — Materials Chemistry

Machine Learning of Atomic Forces from Quantum Mechanics: a Model Based on Pairwise Interatomic Forces We present a new chemically intuitive approach, pairF-Net, to directly predict the atomic forces in a molecule to quantum chemistry accuracy using machine learning techniques. A residual artificial neural network has been designed and trained with features and targets based on pairwise interatomic forces, to determine the Cartesian atomic forces suitable for use in molecular mechanics and dynamics calculations. The scheme implicitly maintains rotational and translational invariance and predicts Cartesian forces as a linear combination of a set of force components in an interatomic basis. We show that the method can predict the reconstructed Cartesian atomic forces for a set of small organic molecules to less than 2 kcal mol-1 Å-1 from the reference force values obtained via density functional theory. The pairF-Net scheme utilises a simple and chemically intuitive route to furnish atomic forces at a quantum mechanical level but at a fraction of the cost, providing a step towards the efficient calculation of accurate thermodynamic properties. Cartesian coordinate system ; Pairwise comparison ; Central force ; Simple (philosophy) ; Statistical physics ; Quantum ; Classical mechanics ; Computer science ; Physics ; Chemistry ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Kazuki Morita | Daniel W. Davies | Keith T. Butler | Aron Walsh
Source:  (preprint, 2021-08-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals While traditional crystallographic representations of structure play an important role in materials science, they are unsuitable for efficient machine learning. A range of effective numerical descriptors have been developed for molecular and crystal structures. We are interested in a special case, where distortions emerge relative to an ideal high-symmetry parent structure. We demonstrate that irreducible representations form an efficient basis for the featurisation of polyhedral deformations with respect to such an aristotype. Applied to dataset of 552 octahedra in ABO3 perovskite-type materials, we use unsupervised machine learning with irreducible representation descriptors to identify four distinct classes of behaviour, associated with predominately corner, edge, face, and mixed connectivity between neighbouring octahedral units. Through this analysis, we identify SrCrO3 as a material with tuneable multiferroic behaviour. Octahedron ; Multiferroics ; Irreducible representation ; Representation (politics) ; Perovskite (structure) ; Ideal (ethics) ; Crystal structure ; Crystal (programming language) ; Face (sociological concept) ; Range (aeronautics) ; Enhanced Data Rates for GSM Evolution ; Symmetry (geometry) ; Materials science ; Crystallography ; Type (biology) ; Computer science ; Theoretical physics ; Mathematics ; Physics ; Geometry ; Artificial intelligence ; Chemistry ; Pure mathematics ; Geology ; Ferroelectricity ; Sociology Multiferroics and related materials ; Machine Learning in Materials Science ; Advanced Condensed Matter Physics
Authors: Erick J. Braham
Source: OakTrust (Texas A&M University Libraries) (article, 2021-01-22)
Field: Materials Science — Polymers and Plastics

Establishing Control over VO2 Phase Transformations and Perovskite Quantum Confinement Through Targeted Exploration of Materials and Processing Design Spaces Establishing mechanistic and causal understanding of a material’s function requires the ability to solve the inverse problem of relating function to atomistic properties and to subsequently examine how synthetic processes can be designed to arrive at desired atomistic configurations. Though detailed mechanistic study and data-driven methodology the processing-structure-property relationships of a synthetic design space can be leveraged to converge on the desired functional properties. Through the examples of VO2 and metal halide perovskites, materials of great current interest due to their desirable properties of a metal-insulator transition (MIT) and high photovoltaic performance respectively, we have established mechanistic understanding and predictivity of relationships between synthesis and materials properties. This work explores chemical doping in the VO2 system as a vector to modify the MIT properties and establish mechanistic understanding of the modulation observed when doping with tungsten, boron, germanium, and iridium. The interplay of defect dynamics, phase transition kinetics, and crystallographic modification in these doped systems result in mechanisms that lower and raise MIT temperatures, afford control over the MIT hysteresis, create a dynamical response, and stabilize entirely new metastable crystal structures. The synthesis of CsPbBr3 perovskite nanoparticles proves to be a successful case study in the utility and flexibility of machine learning to predict outcomes, such as nanoparticle thickness, from sparse and incomplete data as well as to provide quantitative insight into the mechanisms driving the shape of the reaction landscape. Sparse expensive synthetic data benefit from machine-learning-directed navigation as it is more efficient and tolerant to uncertainties than simpler experimental exploration methods. The utility of this type of the machine learning and feature analysis when combined with Bayesian learning for chemical and material syntheses has potential for high-quality predictions with small and/or high-dimensional datasets with implications for automated experimentation and engineering where solving the inverse problems may provide human-like insight to automation processes. Perovskite (structure) ; Quantum dot ; Phase (matter) ; Control (management) ; Phase control ; Materials processing ; Materials science ; Nanotechnology ; Computer science ; Engineering physics ; Physics ; Engineering ; Quantum mechanics ; Artificial intelligence ; Chemical engineering ; Process engineering Transition Metal Oxide Nanomaterials ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Yaolong Zhang | Jun Jiang | Bin Jiang
Source: ArXiv.org (preprint, 2021-11-08)
Field: Chemistry — Spectroscopy

Learning Dipole Moments and Polarizabilities Machine learning of scalar molecular properties such as potential energy has enabled widespread applications. However, there are relatively few machine learning models targeting directional properties, including permanent and transition dipole (multipole) moments, as well as polarizability. These properties are essential to determine intermolecular forces and molecular spectra. In this chapter, we review machine learning models for these tensorial properties, with special focus on how to encode the rotational equivariance into these models by taking a similar form as the physical definition of these properties. You will then learn how to use an embedded atom neural network model to train dipole moments and polarizabilities of a representative molecule. The methodology discussed in this chapter can be extended to learn similar or higher-rank tensorial properties, such as magnetic dipole moments, non-adiabatic coupling vectors, and hyperpolarizabilities. Polarizability ; Multipole expansion ; Dipole ; Scalar (mathematics) ; Statistical physics ; Physics ; Focus (optics) ; Computational chemistry ; Chemistry ; Molecule ; Quantum mechanics ; Mathematics ; Optics Molecular spectroscopy and chirality ; Molecular Spectroscopy and Structure ; Solid-state spectroscopy and crystallography
Authors: Hangrui Bi | Hengyi Wang | Chence Shi | Connor W. Coley | Jian Tang | Hongyu Guo
Source: NPARC (article, 2021-06-08)
Field: Materials Science — Materials Chemistry

Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction Reliably predicting the products of chemical reactions presents a fundamental challenge in synthetic chemistry. Existing machine learning approaches typically produce a reaction product by sequentially forming its subparts or intermediate molecules. Such autoregressive methods, however, not only require a pre-defined order for the incremental construction but preclude the use of parallel decoding for efficient computation. To address these issues, we devise a non-autoregressive learning paradigm that predicts reaction in one shot. Leveraging the fact that chemical reactions can be described as a redistribution of electrons in molecules, we formulate a reaction as an arbitrary electron flow and predict it with a novel multi-pointer decoding network. Experiments on the USPTO-MIT dataset show that our approach has established a new state-of-the-art top-1 accuracy and achieves at least 27 times inference speedup over the state-of-the-art methods. Also, our predictions are easier for chemists to interpret owing to predicting the electron flows. Autoregressive model ; Computer science ; Speedup ; Inference ; Decoding methods ; Algorithm ; Chemical reaction ; Computation ; Redistribution (election) ; Artificial intelligence ; Theoretical computer science ; Chemistry ; Mathematics ; Parallel computing ; Econometrics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Adam Green | Ravin Chowdhury | Eric Minor | Stian Howard | Cheol Park | Noel Clark
Source: Bulletin of the American Physical Society (article, 2021-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Defect Annihilation in Liquid Crystal Physics: Using Deep Learning to Probe the Dynamics of Defects Annihilation ; Dynamics (music) ; Physics ; Liquid crystal ; Statistical physics ; Condensed matter physics ; Nuclear physics ; Materials science Liquid Crystal Research Advancements ; Material Dynamics and Properties
Authors: Ryan A. McCarthy | Ananya Sen Gupta
Source: IEEE Access (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Employing and Interpreting a Machine Learning Target-Cognizant Technique for Analysis of Unknown Signals in Multiple Reaction Monitoring The aim of this interdisciplinary work is a robust signal processing and autonomous machine learning framework to associate well-known (target) as well as any potentially unknown (non-target) peaks present within gas chromatography-mass spectrometry (GC/MS/MS) raw instrument signal. Particularly, this work evaluates three machine learning algorithms abilities to autonomously associate raw signal peaks based on accuracy in training and testing. A target is a known congener that is expected to be present within the raw instrument signal and a non-target is an unknown or unexpected compound. Autonomously identifying target peaks within the GC/MS/MS and associating them with non-target peaks can help improve the analysis of collected samples. Association of peaks refers to classifying peaks as known congeners regardless if the peak is a target or non-target. Uncertainty of peaks fitted and discovered through raw instrument signals from GC/MS/MS data is assessed to create topographical illustrations of target annotated peaks among sample raw instrument signals collected across diverse locations in the Chicago area. The term "annotated peak" is used to assign peaks found at specific retention times as a known congener. Adaptive signal processing techniques are utilized to smooth data and correct baseline drifts as well as detect and separate coeluted (overlapped) peaks in the raw instrument signal to provide key feature extraction. 150 air samples are analyzed for individual polychlorinated biphenyls (PCB) with GC/MS/MS across Chicago, IL. 80% of the data is used for training classification of target PCBs and 20% of the data is evaluated to identify and associate consistently occurring non-target peaks with target PCBs. A random forest classifier is used to associate identified peaks to target PCB peaks. Geographical topographical representations of target PCBs in the raw instrument signal demonstrates how PCBs accumulate and degrade in certain locations. Computer science ; Artificial intelligence ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fault Detection and Control Systems
Authors: 康伸 安藤 | Yasunobu Ando
Source: Institutional Repositories DataBase (IRDB) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

High-dimensional neural network atomic potentials for examining energy materials: some recent simulations Artificial neural network ; Energy (signal processing) ; Field (mathematics) ; Potential energy ; Noise (video) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Surface and Thin Film Phenomena
Authors: Kyle G. Reeves | Mathieu Salanne
Source: Bulletin of the American Physical Society (article, 2021-03-16)
Field: Chemistry — Electrochemistry

Combining molecular simulations with artificial neural networks to determine the electrochemical properties of solvated species Artificial neural network ; Electrochemistry ; Biological system ; Chemical physics ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Physical chemistry ; Biology Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Fuel Cells and Related Materials
Authors: Mallory Wittwer
Source: Data Archiving and Networked Services (DANS) (article, 2021-11-25)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach to Map Crystal Orientation by Optical Microscopy, Wittwer et al. This repository contains the data and trained models required to reproduce the results presented in our publication. Orientation (vector space) ; Microscopy ; Artificial intelligence ; Computer vision ; Optical microscope ; Materials science ; Optics ; Computer science ; Physics ; Geometry ; Mathematics Crystallization and Solubility Studies
Authors: Anders Johansson | Jonathan Vandermause | Andrea Cepellotti | Boris Kozinsky
Source: Bulletin of the American Physical Society (article, 2021-03-18)
Field: Materials Science — Materials Chemistry

Fast Simulations of Thermal Transport in Complex Materials using Machine Learning and Bayesian Force Fields Bayesian probability ; Force field (fiction) ; Thermal ; Computer science ; Artificial intelligence ; Statistical physics ; Physics ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Phillip Crout
Source: Proceedings of the European Microscopy Congress 2020 (article, 2021-02-26)
Field: Materials Science — Materials Chemistry

Crystal phase identification by machine learning using scanning precession electron diffraction data Crystal (programming language) ; Diffraction ; Precession ; Electron diffraction ; Phase (matter) ; Materials science ; Electron ; Identification (biology) ; Scanning electron microscope ; Computer science ; Artificial intelligence ; Crystallography ; Optics ; Physics ; Condensed matter physics ; Chemistry ; Nuclear physics ; Quantum mechanics X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Mineral Processing and Grinding
Authors: M. Vollmar
Source: ePrints Soton (University of Southampton) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: Machine learning applications for macro-molecular X-ray crystallography at Diamond Proteins are the core machinery in any living organism. Understanding their structure means understanding their function and the mechanism with which they carry out this function. In many diseases, the structure of a protein is altered through amino acid exchange usually as a result of mutations in the encoding DNA. The changes in the structure in turn alter the functions and mechanisms in proteins. Being able to understand these changes on an atomic level, offers the opportunity to design drugs to manipulate, regulate and control these proteins with the aim to reduce or even eliminate the effects they cause on an organism. X-ray crystallography, besides cryo-EM and NMR, is one of the methods with which a proteinâs structure can be revealed to atomic level. Synchrotron facilities such as Diamond have made great investments over the last decade to push for automation and high-throughput. On the other hand, the large data amounts produced as a result, require a new thinking of how to automate data analysis in turn. As a proof-of-principal work, a machine learning (ML) based decision maker has been implemented into the automated data analysis pipelines at Diamond. The aim is, to explore ML based applications for decision making in the data analysis process to change the current threshold-based, brute-force system to one that offers more flexibility. This in turn will reduce the number of executed jobs but does not diminish the success rate and makes more efficient use of the limited, shared compute resources. Computer science ; Automation ; Protein Data Bank ; Function (biology) ; Flexibility (engineering) ; Encoding (memory) ; Process (computing) ; Diamond ; Artificial intelligence ; Machine learning ; Protein structure ; Chemistry ; Engineering ; Biology ; Programming language ; Biochemistry Enzyme Structure and Function ; Genetics, Bioinformatics, and Biomedical Research ; Machine Learning in Materials Science
Authors: Eun-Ah Kim | Se‐Hun Kwon | Dong‐Yeol Yang | Jihun Yu | Kwon-Ill Kim | Hak Sung Lee
Source: Journal of Korean Powder Metallurgy Institute (article, 2021-06-30)
Field: Materials Science — Materials Chemistry

Characterization and Classification of Pores in Metal 3D Printing Materials with X-ray Tomography and Machine Learning Characterization (materials science) ; Materials science ; Tomography ; X-ray ; Metal ; Computed tomography ; Nanotechnology ; Metallurgy ; Optics ; Radiology ; Medicine ; Physics Machine Learning in Materials Science ; Image Processing and 3D Reconstruction
Authors: Max Hodapp | Alexander V. Shapeev
Source: arXiv (Cornell University) (preprint, 2021-07-12)
Field: Materials Science — Materials Chemistry

Machine-learning potentials enable predictive $\textit{and}$ tractable high-throughput screening of random alloys We present an automated procedure for computing stacking fault energies in random alloys from large-scale simulations using moment tensor potentials (MTPs) with the accuracy of density functional theory (DFT). To that end, we develop an algorithm for training MTPs on random alloys. In the first step, our algorithm constructs a set of ~10000 or more training candidate configurations with 50-100 atoms that are representative for the atomic neighborhoods occurring in the large-scale simulation. In the second step, we use active learning to reduce this set to ~100 most distinct configurations - for which DFT energies and forces are computed and on which the potential is ultimately trained. We validate our algorithm for the MoNbTa medium-entropy alloy by showing that the MTP reproduces the DFT $\frac{1}{4}[111]$ unstable stacking fault energy over the entire compositional space up to a few percent. Contrary to state-of-the-art methods, e.g., the coherent potential approximation (CPA) or special quasi-random structures (SQSs), our algorithm naturally accounts for relaxation, is not limited by DFT cell sizes, and opens opportunities to efficiently investigate follow-up problems, such as chemical ordering. In a broader sense, our algorithm can be easily modified to compute related properties of random alloys, for instance, misfit volumes, or grain boundary energies. Moreover, it forms the basis for an efficient construction of MTPs to be used in large-scale simulations of multicomponent systems. Computer science ; Statistical physics ; Density functional theory ; Stacking ; Algorithm ; Relaxation (psychology) ; Scale (ratio) ; Set (abstract data type) ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Stefan Goedecker | Hossein Tahmasbi | Ehsan Rahmatizad Khajehpasha | Samare Rostami | Hossein Asnaashari | Somayeh Faraji | Maximilian Amsler | S. Alireza Ghasemi
Source: Bulletin of the American Physical Society (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Structure prediction of ionic materials using the Minima Hopping method and the CENT machine learning potential Maxima and minima ; Ionic bonding ; Computer science ; Biological system ; Machine learning ; Materials science ; Artificial intelligence ; Statistical physics ; Physics ; Mathematics ; Mathematical analysis ; Quantum mechanics ; Ion ; Biology Machine Learning in Materials Science
Authors: Ryoji Kiyanagi | Takashi Ohhara | Akiko Nakao | Koji Munakata | Yoshihisa Ishikawa | Kentro Moriyama
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Materials Science — Materials Chemistry

Application of machine learning and kernel density estimation for efficient data treatment on single-crystal diffraction data SENJU" is a TOF-Laue neutron single crystal diffractometer installed at J-PARC/MLF in Japan. This instrument is designed to study structures of inorganic materials and organic materials with relatively small cell sizes as well as magnetic structures. SENJU has 41 2D-detectors installed cylindrically surrounding the sample position. With the benefit of high intensity pulsed white neutrons generated at J-PARC, SENJU can measure a quite large 3D reciprocal space at once, which makes a quite efficient measurements possible. In order to make a good use of the efficient measurement, as a natural consequence, efficient data treatment is also demanded. Kernel (algebra) ; Kernel density estimation ; Computer science ; Diffraction ; Materials science ; Artificial intelligence ; Mathematics ; Statistics ; Physics ; Optics ; Discrete mathematics X-ray Diffraction in Crystallography
Authors: Yi-Ming Tseng | Pei - Te Wang | Nan-Yow Chen | An-Cheng Yang | Nien‐Ti Tsou
Source:  (preprint, 2021-09-10)
Field: Materials Science — Materials Chemistry

A Rapid Post-processing Method based on Graph Neural Networks for Atomic Simulations of Shape memory alloys Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions. Computer science ; Artificial neural network ; Graph ; Theoretical computer science ; Artificial intelligence Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electron and X-Ray Spectroscopy Techniques