jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: B. U. V. Prashanth | Mohammed Riyaz Ahmed
Source: arXiv (Cornell University) (preprint, 2019-07-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial Neural Network Algorithm based Skyrmion Material Design of Chiral Crystals The model presented in this research predicts ideal chiral crystal and propose a new direction of designing chiral crystals. Skyrmions are topologically protected and structurally assymetric materials with an exotic spin composition. This work presents deep learning method for skyrmion material design of chiral crystals. This paper presents an approach to construct a probabilistic classifier and an Artificial Neural Network(ANN) from a true or false chirality dataset consisting of chiral and achiral compounds with 'A' and 'B' type elements. A quantitative predictor for accuracy of forming the chiral crystals is illustrated. The feasibility of ANN method is tested in a comprehensive manner by comparing with probalistic classifier method. Throughout this manuscript we present deep learnig algorithm design with modelling and simulations of materials. This research work elucidated paves a way to develop sophisticated software tool to make an indicator of crystal design. Skyrmion ; Artificial neural network ; Algorithm ; Classifier (UML) ; Chirality (physics) ; Artificial intelligence ; Computer science ; Probabilistic logic ; Crystal (programming language) ; Construct (python library) ; Materials science ; Physics ; Condensed matter physics ; Chiral symmetry ; Particle physics Multiferroics and related materials ; Advanced Condensed Matter Physics ; X-ray Diffraction in Crystallography
Authors: 정유성
Source:  (article, 2019-05-03)
Field: Materials Science — Materials Chemistry

Small molecule activation using computational catalysis and machine learning Electochemical activation of small molecules (CO2, N2, etc) towards value-added products is an efficient and sustainable way to address energy problems and global climate changes. In this talk, I will discuss some of our recent efforts to understand and design new materials towards electrochemical catalysis using density functional calculations. In addition, solid state materials are often complex, but can also be potentially highly tunable if there is a way to accurately extrapolate the large set of existing data for a new discovery, an area in which machine learning can help significantly accelerate the discovery proces with improved accuracy. At the end of this talk, thus, I will briefly describe some of our initial efforts to use machine learning for chemical science that can contribute greatly to creating solutions to catalysis and materials problems, in general. Computer science ; Set (abstract data type) ; Biochemical engineering ; Nanotechnology ; Materials science ; Engineering Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Michele Parrinello
Source: StatPhys 27 Main Conference (article, 2019-05-15)
Field: Materials Science — Materials Chemistry

Machine learning and molecular dynamics Atom based computer simulation is one of the most important tools of contemporary physical chemistry. In spite of its many successes, it suffers from severe limitations. Here we show how machine-learning techniques can help in solving at least two different problems. The first one is the accuracy of current interatomic potential models; the second is the limited time scale that simulations can explore. In order to solve the first problem we train a neural network on a set of accurate but expensive quantum chemical calculations. In this way, it is possible to obtain an accurate description of the system at a relatively low computational cost. Crucial for the success of this program has been the design of the neural work and the selection of the training set. We apply this approach to study a metal non-metal transition and to chemical reactions in condensed phases. These applications would not have been possible without the use of efficient sampling methods capable of lifting the time scale barrier. To this effect, we have developed two very efficient sampling methods, metadynamics and variationally enhanced sampling. Both methods are based on the identification of appropriate collective variables, or slow modes, whose sampling needs to be accelerated. Machine learning can be used also for the construction of efficient collective variables based on a modification of the well-known linear discriminant analysis classification method. Finally, we use the variational enhanced sampling approach and a deep neural network to further increase our sampling ability. Metadynamics ; Computer science ; Artificial neural network ; Sampling (signal processing) ; Machine learning ; Artificial intelligence ; Set (abstract data type) ; Scale (ratio) ; Molecular dynamics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Szu‐Chia Chien | G. S. Frankel | Wolfgang Windl
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Metals and Alloys

Machine Learning for Corrosion Resistance Alloys Determining the ability of corrosion resistance of alloys is of great importance to many applications. While one could call the pitting resistance equivalence number (PREN) a quantitative description of the relative corrosion resistance of stainless steels, it is an empirical value based on the chemical composition of the elements in alloys only, derived by mathematically fitting of experimental alloy data. It provides no scientific insight. In this work we collect a large number of experimental data published in the literature on corrosion of alloys to study the factors that determine the corrosion resistance of materials. A database that includes the alloy composition, electrochemical parameters as well as polarization testing parameters was therefore generated. Machine learning approaches (Lasso and Ridge regression) were used to find the unexpected correlations between aforementioned experimental parameters and the materials' properties. We further use the machine learning to prioritize the features that determine the corrosion resistance and anticipate that this work can be an useful tool for materials design. Corrosion ; Polarization (electrochemistry) ; Materials science ; Alloy ; Equivalence (formal languages) ; Metallurgy ; Work (physics) ; Computer science ; Mechanical engineering ; Engineering ; Mathematics ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Corrosion Behavior and Inhibition
Authors: Samuel Hallinder
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Reaction Conditions Data Mining : The application of Machine Learning towards predicting the future of process development In organic chemistry and especially process chemistry, there is a constant need to develop cost-effective ways to optimize different reaction conditions. With the increased development of Machine Learning (ML) combined with Data Mining (DM) new possibilities arise to reduce time and costs in the field of chemical science. In order to address the need for reduced time-/cost savings in process chemistry, the often-employed Suzuki-Miyaura reaction was studied by such ML and DM methods. A representative dataset containing molecular and structural properties of substrates and product were calculated with open-source toolkits Indigo, Chemistry Development Kit and RDKit available in KNIME. To predict any form of reaction outcomes, catalysts and reaction conditions were ranked based on several binary classification Machine Learning models designed with a Random Forest algorithm. On model lead to a binary classification model performing at a low computational cost. It showed an AUC of 98.5% predicting a reaction to a certain threshold of yield ( &gt;=60% and&lt;=40%). A second model encompassed six unique binary classification models and presented an average accuracy of 91.6% to predict a correct catalyst. These six different models were combined to later rank catalysts that are best suited for a new reaction and gave a probability result between 23.6% to 77.3%. The experimental validation was proven to highlight the uncertainty of the performance, were the least suitable (23.6%) catalyst demonstrated best performance. Overall, the models showed a promising correlation to support the synthesis optimization problem and with further adjustment there are great opportunities to obtain a model that can assist chemists in the future. Process (computing) ; Computer science ; Artificial intelligence ; Machine learning ; Data mining ; Data science Machine Learning in Materials Science ; Analytical Chemistry and Chromatography ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yunqi Shao | Matti Hellström | Pavlin D. Mitev | Lisanne Knijff | Chao Zhang
Source: arXiv (Cornell University) (article, 2019-10-08)
Field: Materials Science — Materials Chemistry

PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials. Despite many successes, developing interpretable ANN architectures and implementing existing ones efficiently are still challenging. This calls for reliable, general-purpose and open-source codes. Here, we present a python library named PiNN as a solution toward this goal. In PiNN, we designed a new interpretable and high-performing graph convolutional neural network variant, PiNet, as well as implemented the established Behler-Parrinello high-dimensional neural network. These implementations were tested using datasets of isolated small molecules, crystalline materials, liquid water and an aqueous alkaline electrolyte. PiNN comes with a visualizer called PiNNBoard to extract chemical insight "learned" by ANNs, provides analytical stress tensor calculations and interfaces to both the Atomic Simulation Environment and a development version of the Amsterdam Modeling Suite. Moreover, PiNN is highly modularized which makes it useful not only as a standalone package but also as a chain of tools to develop and to implement novel ANNs. The code is distributed under a permissive BSD license and is freely accessible at https://github.com/Teoroo-CMC/PiNN/ with full documentation and tutorials. Python (programming language) ; Computer science ; Suite ; MIT License ; Template ; Convolutional neural network ; Artificial neural network ; Source code ; Documentation ; Compiler ; Code refactoring ; Artificial intelligence ; Programming language ; Software Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Shiru Lin | Zhongfang Chen
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Computational Screening of Single-Atom Electrocatalysts for Oxygen Reduction Reaction By Machine Learning Algorithm In this work, we advanced emerging descriptor-based screening and machine learning techniques to identify new families of catalysts to be synthesized. We present a computaional flow to evaluate the catalysis performance of single-atom catalysts for oxygen reduction reaction (ORR). The onset potentials of single-transitional metal on graphene defects computed by density funtional theory (DFT) were used as our input data, and electronegativity of metal atoms and surrounding atoms (C, N or B), the coordinate numbers of central metal atoms and neighbor atoms were set as the intrinsic features for predicting catalysis performance. We used random forest algorithm to train the machine learning model, and employed this moderl to generate prominent single-atom catalysts for ORR. The predictive model can predict the onset potential directly from the structural properties of single-atom-base systems, which is a great progress from the models which can only predict the adsorption energy of intermediates. Therefore, we applied various single-atom catalysts systems with structural information, and the model screened out the most promising ones among them. This work demonstrates that the collocation of traditional computational chemistry and data-driven science can rapidly expand computing power and solve the most challenging catalysis problems! Electronegativity ; Catalysis ; Atom (system on chip) ; Graphene ; Algorithm ; Density functional theory ; Computer science ; Chemistry ; Materials science ; Machine learning ; Nanotechnology ; Computational chemistry ; Parallel computing Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Shruti Venkatram | Chiho Kim | Anand Chandrasekaran | Rampi Ramprasad
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Prediction of Solvents and Non-Solvents for Polymers Using Machine Learning Techniques As renewable energy sources are increasingly gaining traction as an alternative to scarce fossil fuels, exploratory research on energy storage devices which are reliable, cost-effective and have low energy losses has become prolific. This has spearheaded the development of solid polymer electrolytes for safer lithium-ion batteries with comparable properties as conventional liquid electrolyte-based lithium-ion batteries. Synthesizing solid polymer electrolytes involves preparing an electrolyte film from a solution of polymer and Li-ion salts in an appropriate solvent. Identifying appropriate solvents for the polymer solution is usually done by trial-and-error, and therefore, is time-consuming. To mitigate this problem, quantitative measures of solvent-polymer miscibility known as solubility parameters have been developed in the past. In the present study, we first assessed the performance of the Hildebrand and Hansen solubility parameters to predict solvents and non-solvents for a set of benchmark polymers. Based on the results of the assessment, a machine learning model was trained on a dataset of known polymer Hildebrand solubility parameters to predict the solubility parameter of a queried polymer. Matching the predicted value with known solvent solubility parameters was then utilized to identify suitable solvents and non-solvents for the queried polymer. This capability has been implemented at www.polymergenome.org. Solubility ; Hildebrand solubility parameter ; Polymer ; Solvent ; Materials science ; Electrolyte ; Miscibility ; Polymer electrolytes ; Process engineering ; Chemical engineering ; Chemistry ; Organic chemistry ; Ionic conductivity ; Composite material ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Data Processing Techniques
Authors: Bryce Meredig
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

(Invited) Machine Learning in Materials Discovery Machine learning (ML) is rapidly emerging as an important tool for materials discovery. In this talk, we will address key considerations involved in applying ML to materials design problems, including: using sequential learning for inverse design of materials with target properties; quantifying model performance when extrapolating to new chemistries (rather than interpolating among well-known chemistries); and optimizing the design space (i.e., the set of candidates to search with ML). Key (lock) ; Computer science ; Set (abstract data type) ; Machine learning ; Learning design ; Artificial intelligence ; Mathematics ; Programming language ; Mathematics education Machine Learning in Materials Science
Authors: James Cumby
Source: ePrints Soton (University of Southampton) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Materials Science Report 2019 This five-day workshop consisted of a two-day school introducing machine learning techniques for materials science with a mixture of presentations and hands-on tutorials, followed by a three-day conference made up of invited presentations, contributed talks, a poster session and a number of panel discussions. The event drew participants from primarily chemistry, physics and computer science backgrounds, all with a focus on applying machine learning to materials discovery or understanding. Some of the invited talks were given by industrial participants, complementing the academic research presenting, and offering a different view. Coffee breaks and a conference dinner/poster session allowed plenty of time for networking amongst this diverse group. Session (web analytics) ; Focus (optics) ; Computer science ; Mathematics education ; Multimedia ; Psychology ; World Wide Web Machine Learning in Materials Science
Authors: Jack Yang | Nathan Li | Sean Li
Source: ChemRxiv (preprint, 2019-07-26)
Field: Materials Science — Materials Chemistry

The Interplay among Molecular Structures, Crystal Symmetries and Lattice Energy Landscapes Revealed by Unsupervised Machine Learning: A Closer Look at Pyrrole Azaphenacenes The ability to perform large-scale crystal structure predictions (CSP) have significantly advanced the synthesis of functional molecular solids by designs. In our recent work [Chem. Mater., 30, 4361 (2018)], we demonstrated our latest developments in organic CSPs by screening a set of 28 pyrrole azaphenacene isomers which led to one new molecule with higher thermodynamic stability and carrier mobilities in its crystalline form, compared to the one reported experimentally. Hereby, using the lattice energy landscapes for pyrrole azaphenacenes as examples, we applied machine-learning techniques to statistically reveal in more details, on how molecular symmetry and Z' values translate to the crystal packing landscapes, which in terms affect the coverage of landscape through quasi-random crystal structure samplings. A recurring theme in crystal engineering is to identify the probabilities of targeting isostructures to a specific reference crystal upon chemical functionalisations. For this, we propose here a global similarity index in conjunction with the Energy-Density Isostructurality (EDI) map to analyse the lattice energy landscapes for halogen substituted pyrrole azaphenacenes. A continue effort in the field is to accelerate CSPs for sampling a much wider chemical space for high-throughput material screenings, we propose a potential solution to this challenge drawn upon this study. Our work will hopefully stimulate the crystal engineering community in adapting a more statistically-oriented approach in understanding crystal packing of organic molecules in the age of digitisation. Pyrrole ; Lattice energy ; Crystal structure prediction ; Crystal engineering ; Energy landscape ; Crystal (programming language) ; Crystal structure ; Lattice (music) ; Chemical physics ; Molecule ; Statistical physics ; Chemistry ; Materials science ; Computer science ; Artificial intelligence ; Crystallography ; Physics ; Supramolecular chemistry ; Thermodynamics ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Jocelyn Sunseri | David Ryan Koes
Source: arXiv (Cornell University) (preprint, 2019-12-10)
Field: Materials Science — Materials Chemistry

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications There are many ways to represent a molecule as input to a machine learning model and each is associated with loss and retention of certain kinds of information. In the interest of preserving three-dimensional spatial information, including bond angles and torsions, we have developed libmolgrid, a general-purpose library for representing three-dimensional molecules using multidimensional arrays. This library also provides functionality for composing batches of data suited to machine learning workflows, including data augmentation, class balancing, and example stratification according to a regression variable or data subgroup, and it further supports temporal and spatial recurrences over that data to facilitate work with recurrent neural networks, dynamical data, and size extensive modeling. It was designed for seamless integration with popular deep learning frameworks, including Caffe, PyTorch, and Keras, providing good performance by leveraging graphical processing units (GPUs) for computationally-intensive tasks and efficient memory usage through the use of memory views over preallocated buffers. libmolgrid is a free and open source project that is actively supported, serving the growing need in the molecular modeling community for tools that streamline the process of data ingestion, representation construction, and principled machine learning model development. Computer science ; Workflow ; Artificial intelligence ; Deep learning ; Machine learning ; Process (computing) ; Artificial neural network ; Class (philosophy) ; Representation (politics) ; Programming language ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jack Yang | Nathan Li | Sean Li
Source: ChemRxiv (preprint, 2019-07-26)
Field: Materials Science — Materials Chemistry

The Interplay among Molecular Structures, Crystal Symmetries and Lattice Energy Landscapes Revealed by Unsupervised Machine Learning: A Closer Look at Pyrrole Azaphenacenes The ability to perform large-scale crystal structure predictions (CSP) have significantly advanced the synthesis of functional molecular solids by designs. In our recent work [Chem. Mater., 30, 4361 (2018)], we demonstrated our latest developments in organic CSPs by screening a set of 28 pyrrole azaphenacene isomers which led to one new molecule with higher thermodynamic stability and carrier mobilities in its crystalline form, compared to the one reported experimentally. Hereby, using the lattice energy landscapes for pyrrole azaphenacenes as examples, we applied machine-learning techniques to statistically reveal in more details, on how molecular symmetry and Z' values translate to the crystal packing landscapes, which in terms affect the coverage of landscape through quasi-random crystal structure samplings. A recurring theme in crystal engineering is to identify the probabilities of targeting isostructures to a specific reference crystal upon chemical functionalisations. For this, we propose here a global similarity index in conjunction with the Energy-Density Isostructurality (EDI) map to analyse the lattice energy landscapes for halogen substituted pyrrole azaphenacenes. A continue effort in the field is to accelerate CSPs for sampling a much wider chemical space for high-throughput material screenings, we propose a potential solution to this challenge drawn upon this study. Our work will hopefully stimulate the crystal engineering community in adapting a more statistically-oriented approach in understanding crystal packing of organic molecules in the age of digitisation. Lattice energy ; Pyrrole ; Crystal structure prediction ; Crystal engineering ; Crystal (programming language) ; Energy landscape ; Crystal structure ; Lattice (music) ; Chemical physics ; Molecule ; Statistical physics ; Computer science ; Chemistry ; Materials science ; Artificial intelligence ; Machine learning ; Crystallography ; Physics ; Supramolecular chemistry ; Thermodynamics ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods