jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Di Chen | Yiwei Bai | Sebastian Ament | Wenting Zhao | Dan Guevarra | Lan Zhou | Bart Selman | R. B. van Dover | John M. Gregoire | Carla P. Gomes
Source: arXiv (Cornell University) (preprint, 2021-08-21)
Field: Materials Science — Materials Chemistry

Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials. Materials science experts excel at solving simple systems but cannot solve complex systems, creating a major bottleneck in high-throughput materials discovery. Herein we show how to automate crystal-structure phase mapping. We formulate phase mapping as an unsupervised pattern demixing problem and describe how to solve it using Deep Reasoning Networks (DRNets). DRNets combine deep learning with constraint reasoning for incorporating scientific prior knowledge and consequently require only a modest amount of (unlabeled) data. DRNets compensate for the limited data by exploiting and magnifying the rich prior knowledge about the thermodynamic rules governing the mixtures of crystals with constraint reasoning seamlessly integrated into neural network optimization. DRNets are designed with an interpretable latent space for encoding prior-knowledge domain constraints and seamlessly integrate constraint reasoning into neural network optimization. DRNets surpass previous approaches on crystal-structure phase mapping, unraveling the Bi-Cu-V oxide phase diagram, and aiding the discovery of solar-fuels materials. Bottleneck ; Computer science ; Constraint (computer-aided design) ; Artificial neural network ; Artificial intelligence ; Deep learning ; Phase (matter) ; Machine learning ; Chemistry ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Hydrocarbon exploration and reservoir analysis
Authors: Changhae Andrew Kim | Nathan D. Ricke | Troy Van Voorhis
Source: The Journal of Chemical Physics (article, 2021-09-30)
Field: Materials Science — Materials Chemistry

Machine learning dynamic correlation in chemical kinetics Lattice models are a useful tool to simulate the kinetics of surface reactions. Since it is expensive to propagate the probabilities of the entire lattice configurations, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation. Unfortunately, simple closures, such as the mean-field and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics using the lattice Lotka-Volterra model as a model system. We trained feedforward neural networks on kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the ML moment closure (MLMC) gave accurate predictions of the instantaneous three-site occupation probabilities. Solving the kinetic equations in conjunction with MLMC gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pretrained closures that would enable researchers to extract useful insight at a fraction of the computational cost. Moment closure ; Monte Carlo method ; Statistical physics ; Moment (physics) ; Lattice (music) ; Closure (psychology) ; Mathematics ; Computer science ; Physics ; Thermodynamics ; Classical mechanics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Marcus H. Reis | Filipp Gusev | Nicholas G. Taylor | Sang Hun Chung | Matthew Verber | Yueh Z. Lee | Olexandr Isayev | Frank A. Leibfarth
Source:  (preprint, 2021-07-20)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis Modern polymer science is plagued by the curse of multidimensionality; the large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure–property relationships. To tackle this challenge in the context of 19F MRI agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software controlled, continuous polymer synthesis platform was developed to enable iterative experimental–computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The non-intuitive design criteria identified by ML, which was accomplished by exploring less than 0.9% of overall compositional space, upended conventional wisdom in the design of 19F MRI agents and lead to the identification of &gt;10 copolymer compositions that outperformed state-of-the-art materials. Copolymer ; Computer science ; Context (archaeology) ; Polymer ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Dongyuan Ni | Wei Wu | Yaguang Guo | Sheng Gong | Qian Wang
Source: Computational Materials Science (article, 2021-02-05)
Field: Materials Science — Materials Chemistry

Identifying key parameters for predicting materials with low defect generation efficiency by machine learning Artificial neural network ; Computer science ; Key (lock) ; Artificial intelligence ; Process (computing) ; Construct (python library) ; Radiation damage ; Materials science ; Machine learning ; Radiation ; Biological system ; Physics ; Nuclear physics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Radioactive element chemistry and processing
Authors: Sai R. Panuganti | Nor Hadhirah Halim | Nian Wei Tan | Wasan Saphanuchart | Emad Elsebakhi
Source: Egyptian Journal of Petroleum (article, 2021-08-26)
Field: Chemistry — Analytical Chemistry

Harness AI and machine learning in de-emulsifier chemical selection The most common method of preventing the formation of emulsions in the petroleum industry is by the application of a de-emulsifier chemical. The standard approach of selecting an appropriate de-emulsifying agent is by scanning different chemistries with changing properties to identify the emulsion breaking region. However, the disadvantage is that these tests are time-consuming. This work presents a faster alternative for choosing de-emulsifier chemicals by using machine learning. For data to train and test machine learning models, several bottle tests are analyzed at different combination of essential parameters. For both non-EOR/normal and EOR-induced emulsion, the models are validated with real crude oil to output a list of de-emulsifiers that work in breaking the emulsion and rank them for success based on probability. An application workflow of this de-emulsifier prediction tool is also created for deploying the model to provide guideline for quick de-emulsifier chemical selection. Emulsion ; Workflow ; Computer science ; Machine learning ; Artificial intelligence ; Engineering ; Database ; Chemical engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Thomas M. Whitehead | Flora Chen | Christopher Daly | G. J. Conduit
Source: Johnson Matthey Technology Review (article, 2021-07-23)
Field: Materials Science — Materials Chemistry

Accelerating the Design of Automotive Catalyst Products Using Machine Learning The design of catalyst products to reduce harmful emissions is currently an intensive process of expert-driven discovery, taking several years to develop a product. Machine learning can accelerate this timescale, leveraging historic experimental data from related products to guide which new formulations and experiments will enable a project to most directly reach its targets. We used machine learning to accurately model 16 key performance targets for catalyst products, enabling detailed understanding of the factors governing catalyst performance and realistic suggestions of future experiments to rapidly develop more effective products. The proposed formulations are currently undergoing experimental validation. Automotive industry ; Process (computing) ; Key (lock) ; Product (mathematics) ; Manufacturing engineering ; Engineering ; Systems engineering ; Process engineering ; Computer science ; Biochemical engineering ; Industrial engineering ; Aerospace engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Ziyan Zhang | Aria Mansouri Tehrani | Anton O. Oliynyk | Blake Day | Jakoah Brgoch
Source: Advanced Materials (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Machine Learning: Finding the Next Superhard Material through Ensemble Learning (Adv. Mater. 5/2021) In article number 2005112, Jakoah Brgoch and co-workers establish an ensemble machine-learning method to find new superhard materials. The model is trained on the sparse experimental data available in the literature to predict load-dependent Vickers hardness based only on chemical composition. Crystal-structure databases and unknown phase diagrams are then screened in search of novel materials with an outstanding mechanical response. Materials science ; Vickers hardness test ; Ensemble learning ; Alma mater ; Machine learning ; Composite material ; Computer science ; Microstructure Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Advanced ceramic materials synthesis
Authors: Yanfei Guan | Shree Sowndarya SV | Liliana C. Gallegos | Peter C. St. John | Robert S. Paton
Source: ChemRxiv (preprint, 2021-06-18)
Field: Materials Science — Materials Chemistry

Real-time Prediction of 1H and 13C Chemical Shifts with DFT accuracy using a 3D Graph Neural Network The computational prediction of NMR chemical shifts using quantum mechanical calculations is now commonplace in aiding organic structural assignment since spectra can be computed for several candidate structures and then compared with experimental values to find the best possible match. However, the computational demands of calculating multiple structural- and stereo-isomers, each of which may typically exist as an ensemble of rapidly-interconverting conformations calculations, are expensive. In this work, we address both of these shortcomings by developing a rapid machine learning (ML) protocol to predict 1H and 13C chemical shifts through an efficient graph neural network (GNN) using 3D structures as input. Transfer learning with experimental data is used to improve the final prediction accuracy of a model training using QM calculations. When tested on the CHESHIRE dataset, the 13C chemical shifts are predicted with comparable accuracy to the best-performing DFT functionals (1.5 ppm) in around 1/6000 of the CPU time. Quantum chemical ; Chemical shift ; Computer science ; Artificial neural network ; Graph ; Artificial intelligence ; Training set ; Algorithm ; Machine learning ; Pattern recognition (psychology) ; Theoretical computer science ; Chemistry ; Molecule ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Shahram Amiri | Elnaz Karimi
Source: Energy Sources Part A Recovery Utilization and Environmental Effects (article, 2021-12-20)
Field: Materials Science — Materials Chemistry

Estimation of catalytic hydrogen production through water-gas shift reaction using a machine learning technique Hydrogen (H2) is an important and environmentally friendly energy source and the need for its usage is growing across the world. Water-Gas Shift (WGS) reaction is the key approach for hydrogen production which uses different catalysts with respect to conditions at which the reaction occurs. This research examines the applicability of a machine learning technique called Least Square Support Vector Machine (LSSVM) to predict the conversion of carbon monoxide (CO) in WGS reactions according to various compositions for active phase and different kinds of support compounds for catalysts. The implemented method considers the intrinsic catalyst variables to predict the performance of the reaction by using variables such as surface area, calcination time and temperature. The outcomes indicate that the LSSVM approach can precisely estimate the real CO conversion information with overall R2, AARD%, and RMSE values of 0.9996, 2.55, and 0.0069, respectively. This research shows the reliable performance of machine learning approaches such as the LSSVM technique for predicting better catalysts and process conditions for this important reaction which is of great importance in researches related to the environmental field. Water-gas shift reaction ; Catalysis ; Hydrogen production ; Support vector machine ; Carbon monoxide ; Hydrogen ; Calcination ; Process engineering ; Response surface methodology ; Water gas ; Process (computing) ; Chemistry ; Machine learning ; Computer science ; Syngas ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Hybrid Renewable Energy Systems
Authors: Grzegorz Matyszczak | K. Zberecki
Source: Journal of Computational Chemistry (article, 2021-02-14)
Field: Materials Science — Materials Chemistry

Application of artificial neural network and global optimization techniques for high throughput modeling of the crystal structure of stannites and kesterites This study aims to apply artificial neural networks for the prediction of the lattice parameters of materials with stannite- and kesterite-type structure, and to compare the results of predictions with that obtained in the calculations exploiting the density functional theory. Crystallographic data for 49 compounds with stannite-type structure and for four compounds with the kesterite-type structure are found and, based on it, crystal structures are calculated using the density functional theory (DFT) method in a two-step relaxation procedure for all compounds. An multilayer Perceptron is constructed, which then is trained on gathered crystallographic data. Values predicted by a neural network (lattice parameters) are compared with experimental data and with results of DFT calculations. Moreover, a global optimization method (the Uspex code) is used to find potentially novel crystal structures for investigated chemical compositions. The results are discussed in the term of advantages and disadvantages of each method. Crystal structure ; Density functional theory ; Artificial neural network ; Kesterite ; Multilayer perceptron ; Computer science ; Materials science ; Crystal structure prediction ; Network structure ; Biological system ; Artificial intelligence ; Computational chemistry ; Crystallography ; Chemistry ; Machine learning ; Nanotechnology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Yejian Wu | Chengyun Zhang | Ling Wang | Hongliang Duan
Source: ChemRxiv (preprint, 2021-02-02)
Field: Materials Science — Materials Chemistry

A Graph-Convolutional Neural Network for Addressing Small-Scale Reaction Prediction We describe a graph-convolutional neural network (GCN) model whose reaction prediction capable as potent as the transformer model on sufficient data, and adopt the Baeyer-Villiger oxidation to explore their performance differences on limited data. The top-1 accuracy of GCN model (90.4%) is higher than that of transformer model (58.4%). Convolutional neural network ; Transformer ; Computer science ; Graph ; Artificial intelligence ; Artificial neural network ; Machine learning ; Theoretical computer science ; Engineering ; Voltage Machine Learning in Materials Science ; Advanced Graph Neural Networks
Authors: Takashi Kojima | Takashi Washio | Satoshi Hara | Masataka KOISHI | Naoya Amino
Source: Polymers (article, 2021-08-11)
Field: Materials Science — Materials Chemistry

Analysis on Microstructure–Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations A better understanding of the microstructure-property relationship can be achieved by sampling and analyzing a microstructure leading to a desired material property. During the simulation of filled rubber, this approach includes extracting common aggregates from a complex filler morphology consisting of hundreds of filler particles. However, a method for extracting a core structure that determines the rubber mechanical properties has not been established yet. In this study, we analyzed complex filler morphologies that generated extremely high stress using two machine learning techniques. First, filler morphology was quantified by persistent homology and then vectorized using persistence image as the input data. After that, a binary classification model involving logistic regression analysis was developed by training a dataset consisting of the vectorized morphology and stress-based class. The filler aggregates contributing to the desired mechanical properties were extracted based on the trained regression coefficients. Second, a convolutional neural network was employed to establish a classification model by training a dataset containing the imaged filler morphology and class. The aggregates strongly contributing to stress generation were extracted by a kernel. The aggregates extracted by both models were compared, and their shapes and distributions producing high stress levels were discussed. Finally, we confirmed the effects of the extracted aggregates on the mechanical property, namely the validity of the proposed method for extracting stress-contributing fillers, by performing coarse-grained molecular dynamics simulations. Microstructure ; Materials science ; Filler (materials) ; Natural rubber ; Biological system ; Stress (linguistics) ; Composite material ; Composite number ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Topological and Geometric Data Analysis
Authors: Jacob Kerner | Horst A. von Recum
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Predicting Drug Interactions to Unassociated Biomedical Implants Using Machine Learning Techniques and Model Polymers Polymer ; Drug ; Computer science ; Biomedical engineering ; Materials science ; Artificial intelligence ; Machine learning ; Medicine ; Pharmacology ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Franz M. Rohrhofer | Santanu Saha | Simone Di Cataldo | Bernhard C. Geiger | Wolfgang von der Linden | Lilia Boeri
Source: arXiv (Cornell University) (preprint, 2021-01-30)
Field: Materials Science — Materials Chemistry

Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures Drive towards improved performance of machine learning models has led to the creation of complex features representing a database of condensed matter systems. The complex features, however, do not offer an intuitive explanation on which physical attributes do improve the performance. The effect of the database on the performance of the trained model is often neglected. In this work we seek to understand in depth the effect that the choice of features and the properties of the database have on a machine learning application. In our experiments, we consider the complex phase space of carbon as a test case, for which we use a set of simple, human understandable and cheaply computable features for the aim of predicting the total energy of the crystal structure. Our study shows that (i) the performance of the machine learning model varies depending on the set of features and the database, (ii) is not transferable to every structure in the phase space and (iii) depends on how well structures are represented in the database. Computer science ; Machine learning ; Artificial intelligence ; Space (punctuation) ; Set (abstract data type) ; Feature (linguistics) ; Database ; Feature engineering ; Feature selection ; Feature vector ; Data mining ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Shuhui Liu | Changcheng Xu | Zhongyuan Zhang | Qiao Zhao | Liying Yao | Wei Liu
Source: Materials Today Communications (article, 2021-08-18)
Field: Materials Science — Materials Chemistry

Precisely identify the geometry of catalyst particles from S/TEM images via a boundary attention deep learning network Materials science ; Boundary (topology) ; Nanoparticle ; Particle (ecology) ; Statistic ; Artificial intelligence ; Enhanced Data Rates for GSM Evolution ; Dispersion (optics) ; Characterization (materials science) ; Embedding ; Computer science ; Geometry ; Biological system ; Nanotechnology ; Optics ; Physics ; Mathematics ; Mathematical analysis ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Zaixi Zhang | Qi Liu | Shengyu Zhang | Chang‐Yu Hsieh | Liang Shi | Chee‐Kong Lee
Source: arXiv (Cornell University) (preprint, 2021-12-02)
Field: Materials Science — Materials Chemistry

Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors The search for new high-performance organic semiconducting molecules is challenging due to the vastness of the chemical space, machine learning methods, particularly deep learning models like graph neural networks (GNNs), have shown promising potential to address such challenge. However, practical applications of GNNs for chemistry are often limited by the availability of labelled data. Meanwhile, unlabelled molecular data is abundant and could potentially be utilized to alleviate the scarcity issue of labelled data. Here, we advocate the use of self-supervised learning to improve the performance of GNNs by pre-training them with unlabeled molecular data. We investigate regression problems involving ground and excited state properties, both relevant for optoelectronic properties of organic semiconductors. Additionally, we extend the self-supervised learning strategy to molecules in non-equilibrium configurations which are important for studying the effects of disorder. In all cases, we obtain considerable performance improvement over results without pre-training, in particular when labelled training data is limited, and such improvement is attributed to the capability of self-supervised learning in identifying structural similarity among unlabeled molecules. Computer science ; Chemical space ; Machine learning ; Artificial intelligence ; Graph ; Artificial neural network ; Similarity (geometry) ; Supervised learning ; Scarcity ; Labeled data ; Deep learning ; Training set ; Theoretical computer science ; Bioinformatics ; Biology Machine Learning in Materials Science ; Conducting polymers and applications ; Computational Drug Discovery Methods
Authors: Tiago Rodrigues
Source: Chem (article, 2021-12-30)
Field: Materials Science — Materials Chemistry

Deriving intuition in catalyst design with machine learning Iridium ; Enantioselective synthesis ; Catalysis ; Chemistry ; Organic synthesis ; Combinatorial chemistry ; Computer science ; Nanotechnology ; Organic chemistry ; Materials science Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Synthetic Organic Chemistry