jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiao Wang | Shaoang Chen | Yurong Ma | Tianyang Zhang | Yu Zhao | Tiwei He | Hui Huang | Shitong Zhang | Junfeng Rong | Chunfeng Shi | Kangjian Tang | Yang Liu | Zhenhui Kang
Source: ACS Applied Materials & Interfaces (article, 2022-05-31)
Field: Materials Science — Materials Chemistry

Continuous Homogeneous Catalytic Oxidation of C–H Bonds by Metal-Free Carbon Dots with a Poly(ascorbic acid) Structure The activation of the C-H bond, a necessary step to get high-value-added compounds, is one of the most important issues in modern catalysis. Combining the advantages of both homogeneous and heterogeneous catalysis, a certain continuous homogeneous process should be one of the ideal routes for the catalytic activation of C-H bonds. Here, through machine learning (ML), we predicted and fabricated metal-free carbon dot (C-Dot) homogeneous catalysts for C-H bond oxidation. These C-Dots have an ascorbic acid unit based polymer-like structure with a polymerization degree in the range of 3-10. With C-Dots as the catalyst, three groups (aliphatic, aromatic, and cycloalkanes) of 10 hydrocarbon molecules were tested, proving its generality for the catalytic oxidation of the C-H bond. A typical example of cyclohexane that was selectively oxidized to adipic acid (AA) by using a circulation and phase-transfer process demonstrates its critical advantages, such as the continuous and large-scaled producing ability of the homogeneous catalysis process. The one-pass conversion efficiency of cyclohexane to AA reaches 77.49% with selectivity up to 84.24% in 4 h. The yield of 16.32% per hour is about 4 times over that of modern technology. Theoretical calculations suggested that the O<sub>2</sub> activation on C-Dots plays a crucial role in determining the reaction rate of the entire catalytic oxidation process of cyclohexane. Catalysis ; Ascorbic acid ; Cyclohexane ; Materials science ; Homogeneous catalysis ; Polymerization ; Adipic acid ; Chemical engineering ; Polymer ; Photochemistry ; Organic chemistry ; Polymer chemistry ; Chemistry Carbon and Quantum Dots Applications ; Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques
Authors: Achintha Ihalage | Yang Hao
Source: Advanced Science (article, 2022-04-27)
Field: Materials Science — Materials Chemistry

Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials Discovery The success of machine learning (ML) in materials property prediction depends heavily on how the materials are represented for learning. Two dominant families of material descriptors exist, one that encodes crystal structure in the representation and the other that only uses stoichiometric information with the hope of discovering new materials. Graph neural networks (GNNs) in particular have excelled in predicting material properties within chemical accuracy. However, current GNNs are limited to only one of the above two avenues owing to the little overlap between respective material representations. Here, a new concept of formula graph which unifies stoichiometry-only and structure-based material descriptors is introduced. A self-attention integrated GNN that assimilates a formula graph is further developed and it is found that the proposed architecture produces material embeddings transferable between the two domains. The proposed model can outperform some previously reported structure-agnostic models and their structure-based counterparts while exhibiting better sample efficiency and faster convergence. Finally, the model is applied in a challenging exemplar to predict the complex dielectric function of materials and nominate new substances that potentially exhibit epsilon-near-zero phenomena. Computer science ; Graph ; Representation (politics) ; Theoretical computer science ; Artificial neural network ; Property (philosophy) ; Function (biology) ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: R. Patrick Xian | Vincent Stimper | Marios Zacharias | Maciej Dendzik | Shuo Dong | Samuel Beaulieu | Bernhard Schölkopf | Martin Wolf | Laurenz Rettig | Christian Carbogno | Stefan Bauer | Ralph Ernstorfer
Source: Nature Computational Science (article, 2022-12-30)
Field: Materials Science — Materials Chemistry

A machine learning route between band mapping and band structure The electronic band structure and crystal structure are the two complementary identifiers of solid-state materials. Although convenient instruments and reconstruction algorithms have made large, empirical, crystal structure databases possible, extracting the quasiparticle dispersion (closely related to band structure) from photoemission band mapping data is currently limited by the available computational methods. To cope with the growing size and scale of photoemission data, here we develop a pipeline including probabilistic machine learning and the associated data processing, optimization and evaluation methods for band-structure reconstruction, leveraging theoretical calculations. The pipeline reconstructs all 14 valence bands of a semiconductor and shows excellent performance on benchmarks and other materials datasets. The reconstruction uncovers previously inaccessible momentum-space structural information on both global and local scales, while realizing a path towards integration with materials science databases. Our approach illustrates the potential of combining machine learning and domain knowledge for scalable feature extraction in multidimensional data. Computer science ; Artificial intelligence ; Pipeline (software) ; Machine learning ; Scalability ; Electronic band structure ; Physics ; Database Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Rangsiman Ketkaew | Sandra Luber
Source: Journal of Chemical Information and Modeling (article, 2022-11-29)
Field: Materials Science — Materials Chemistry

DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space We present <i>Deep learning for Collective Variables</i> (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials. Python (programming language) ; Computer science ; Software ; Deep learning ; Artificial intelligence ; Metadynamics ; Autoencoder ; Documentation ; Computational science ; Theoretical computer science ; Machine learning ; Programming language ; Molecular dynamics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: William A V Beardall | Guy‐Bart Stan | Mary J. Dunlop
Source: GEN Biotechnology (review, 2022-08-01)
Field: Materials Science — Materials Chemistry

Deep Learning Concepts and Applications for Synthetic Biology Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space. Synthetic biology ; Deep learning ; Artificial intelligence ; Computer science ; Data science ; Artificial neural network ; Machine learning ; Biology ; Computational biology Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Helena A. Merker | Harry Heiberger | Linh Thi Hoai Nguyen | Tongtong Liu | Zhantao Chen | Nina Andrejevic | Nathan C. Drucker | Ryotaro Okabe | Song Eun Kim | Yao Wang | Tess Smidt | Mingda Li
Source: iScience (article, 2022-09-28)
Field: Materials Science — Materials Chemistry

Machine learning magnetism classifiers from atomic coordinates The determination of magnetic structure poses a long-standing challenge in condensed matter physics and materials science. Experimental techniques such as neutron diffraction are resource-limited and require complex structure refinement protocols, while computational approaches such as first-principles density functional theory (DFT) need additional semi-empirical correction, and reliable prediction is still largely limited to collinear magnetism. Here, we present a machine learning model that aims to classify the magnetic structure by inputting atomic coordinates containing transition metal and rare earth elements. By building a Euclidean equivariant neural network that preserves the crystallographic symmetry, the magnetic structure (ferromagnetic, antiferromagnetic, and non-magnetic) and magnetic propagation vector (zero or non-zero) can be predicted with an average accuracy of 77.8% and 73.6%. In particular, a 91% accuracy is reached when predicting no magnetic ordering even if the structure contains magnetic element(s). Our work represents one step forward to solving the grand challenge of full magnetic structure determination. Magnetism ; Magnetic structure ; Antiferromagnetism ; Density functional theory ; Ferromagnetism ; Neutron diffraction ; Physics ; Condensed matter physics ; Artificial intelligence ; Computer science ; Magnetic field ; Magnetization ; Quantum mechanics ; Diffraction Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; X-ray Diffraction in Crystallography
Authors: Debra J. Audus | Austin McDannald | Brian DeCost
Source: ACS Macro Letters (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

Leveraging Theory for Enhanced Machine Learning The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the need to understand the physics behind the machine-learning prediction. The former problem is particularly acute in the polymers domain. Here we aim to simultaneously tackle these challenges through the incorporation of scientific knowledge, thus, providing improved predictions for smaller data sets, both under interpolation and extrapolation, and a degree of explainability. We focus on imperfect theories, as they are often readily available and easier to interpret. Using a system of a polymer in different solvent qualities, we explore numerous methods for incorporating theory into machine learning using different machine-learning models, including Gaussian process regression. Ultimately, we find that encoding the functional form of the theory performs best followed by an encoding of the numeric values of the theory. Extrapolation ; Computer science ; Machine learning ; Artificial intelligence ; Computational learning theory ; Gaussian process ; Interpolation (computer graphics) ; Focus (optics) ; Domain (mathematical analysis) ; Process (computing) ; Imperfect ; Encoding (memory) ; Kriging ; Gaussian ; Active learning (machine learning) ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Yunqi Shao | Linnéa Andersson | Lisanne Knijff | Chao Zhang
Source: Electronic Structure (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Finite-field coupling via learning the charge response kernel Abstract Response of the electronic density at the electrode–electrolyte interface to the external field (potential) is fundamental in electrochemistry. In density-functional theory, this is captured by the so-called charge response kernel (CRK). Projecting the CRK to its atom-condensed form is an essential step for obtaining the response charge of atoms. In this work, the atom-condensed CRK is learnt from the molecular polarizability using machine learning (ML) models and subsequently used for the response-charge prediction under an external field (potential). As the machine-learnt CRK shows a physical scaling of polarizability over the molecular size and does not (necessarily) require the matrix-inversion operation in practice, this opens up a viable and efficient route for introducing finite-field coupling in the atomistic simulation of electrochemical systems powered by ML models. Polarizability ; Density functional theory ; Charge (physics) ; Coupling (piping) ; Field (mathematics) ; Materials science ; Physics ; Statistical physics ; Quantum mechanics ; Molecule ; Mathematics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Fuel Cells and Related Materials
Authors: Hasan Kurban | Mustafa Kurban | Mehmet Dalkılıç
Source: Scientific Reports (article, 2022-08-24)
Field: Materials Science — Materials Chemistry

Rapidly predicting Kohn–Sham total energy using data-centric AI Predicting material properties by solving the Kohn-Sham (KS) equation, which is the basis of modern computational approaches to electronic structures, has provided significant improvements in materials sciences. Despite its contributions, both DFT and DFTB calculations are limited by the number of electrons and atoms that translate into increasingly longer run-times. In this work we introduce a novel, data-centric machine learning framework that is used to rapidly and accurately predicate the KS total energy of anatase [Formula: see text] nanoparticles (NPs) at different temperatures using only a small amount of theoretical data. The proposed framework that we call co-modeling eliminates the need for experimental data and is general enough to be used over any NPs to determine electronic structure and, consequently, more efficiently study physical and chemical properties. We include a web service to demonstrate the effectiveness of our approach. Computer science ; Anatase ; Algorithm ; Predicate (mathematical logic) ; Experimental data ; Electronic structure ; Artificial intelligence ; Basis (linear algebra) ; Machine learning ; Chemistry ; Computational chemistry ; Mathematics ; Statistics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yuxuan Wang | Junwei Sha | Shan Zhu | Liying Ma | Chunnian He | Cheng Zhong | Wenbin Hu | Naiqin Zhao
Source: Journal of Power Sources (article, 2022-12-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-driven design of carbon-based materials for high-performance flexible energy storage devices Supercapacitor ; Energy storage ; Flexibility (engineering) ; Computer science ; Electronics ; Pipeline (software) ; Renewable energy ; Work (physics) ; Capacitance ; Carbon fibers ; Process engineering ; Materials science ; Power (physics) ; Electrical engineering ; Electrode ; Mechanical engineering ; Engineering Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced battery technologies research
Authors: Haotong Liang | Valentin Stanev | A. Gilad Kusne | Yuto Tsukahara | Kaito Ito | Ryota Takahashi | Mikk Lippmaa | Ichiro Takeuchi
Source: Physical Review Materials (article, 2022-06-29)
Field: Materials Science — Materials Chemistry

Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping We have developed a phase mapping method based on machine learning analysis of reflection high-energy electron diffraction (RHEED) images. RHEED produces diffraction patterns containing a wealth of static and dynamic information and is commonly used to determine the growth rate, the growth mode, and the surface morphology of epitaxial thin films. However, the ability to extract quantitative structural information from the RHEED patterns that appear during film growth is limited by the lack of versatile and automated analysis techniques. We have created a deep learning-based analysis method for automating the identification of different RHEED pattern types that occur during the growth of a material. Our approach combines several supervised and unsupervised machine learning techniques and permits the extraction of quantitative phase composition information. We applied this method to the mapping of the structural phase diagram of ${\mathrm{Fe}}_{\mathrm{x}}{\mathrm{O}}_{\mathrm{y}}$ thin films grown by pulsed laser deposition as a function of growth temperature and oxygen pressure close to the hematite-magnetite phase boundary. The in situ RHEED-based mapping method produces results that are qualitatively similar to postsynthesis x-ray diffraction analysis. Reflection high-energy electron diffraction ; Electron diffraction ; Materials science ; Phase (matter) ; Reflection (computer programming) ; Diffraction ; Epitaxy ; Electron backscatter diffraction ; Phase diagram ; Crystallography ; Optics ; Analytical Chemistry (journal) ; Computer science ; Nanotechnology ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Jessica Xu | Dipannita Kalyani | Thomas J. Struble | Spencer D. Dreher | Shane W. Krska | Stephen L. Buchwald | Klavs F. Jensen
Source: ChemRxiv (preprint, 2022-09-19)
Field: Materials Science — Materials Chemistry

Roadmap to Pharmaceutically Relevant Reactivity Models Leveraging High-Throughput Experimentation The merger of High-Throughput Experimentation (HTE) and data science presents an opportunity to both accelerate and inspire innovations in synthetic chemistry. Similarly, developments in machine learning (ML) have enabled the distillation of large and complex data sets into predictive models capable of generalizing patterns in the data. However, efforts to merge HTE with ML remain constrained by a few reported datasets with limited structural diversity and corresponding trained models that do not extrapolate well to substrates beyond the training set. Herein, we detail the first ML models for Pd-catalyzed C–N couplings using pharmaceutically relevant structurally diverse large data sets (~ 5000 unique products) generated using nanomole scale compatible chemistry. Careful consideration is given to both the diversity of the data set and accurate model predictions for substrates bearing features beyond those present in the training set. The structural diversity in the data set is enabled by leveraging the Merck & Co., Inc Building Block Collection with an initial focus on C–N coupling using secondary amines. The large dataset enables the systematic evaluation of model performance using five different data-splitting strategies. These five splits are carefully designed to evaluate the model’s ability to extrapolate beyond the substrates in the training set. The accuracy of classification models built with a lens toward application to medicinal chemistry campaigns exceeded the baseline precision-recall by 25-67% depending on the splitting strategy. These results would manifest as significant enrichment of successful C–N couplings using the hits recommended by the models. In addition, the accuracy of the best models for each of the five splits ranges between 70-87% suggesting excellent overall predictivity of the models even for completely unseen substrates. Merge (version control) ; Computer science ; Set (abstract data type) ; Training set ; Data mining ; Machine learning ; Data set ; Artificial intelligence ; Information retrieval Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Shriram S. Sonawane
Source: Journal of Applied Research and Technology (article, 2022-05-02)
Field: Materials Science — Polymers and Plastics

Artificial neural network model for prediction of viscoelastic behaviour of polycarbonate composites Polymer composites are the result of incorporation of nanoparticles into the polymers and can lead to improvements even with a very small amount of reinforcement which can be tuned according to the applications. In order to understand the behaviour of these polymer composites we need to perform a number of characterizations and analyses which in turn requires investment of money and time. Thus, to reduce the number of characterizations and analyses for developing polymer composites, computational techniques can prove helpful. By means of a computational technique known as artificial neural network (ANN), prediction of the thermo-mechanical properties was made possible. Here dynamic mechanical analysis (DMA) data set was used for characterization of polycarbonate / calcium carbonate-SiO2 core shell composites (polycarbonate composites). Using the dataset, the selected ANN model consisted of a network of [3-10-1]. The prediction accuracy achieved using ANN method, was around 90%. Applicability and performance of ANN to the existing system was also confirmed by mean squared error (MSE), which is favourably small for this case, in the range of 10-5. The output predicted by ANN had a coefficient of correlation of 0.999. Furthermore, sensitivity analysis confirmed the importance of various input variables in relation with output. An optimization of the variables facilitated to maximize the conditions thus predicting glass transition temperature. Polycarbonate ; Artificial neural network ; Materials science ; Composite material ; Viscoelasticity ; Polymer ; Mean squared error ; Biological system ; Algorithm ; Computer science ; Mathematics ; Machine learning ; Statistics Polymer Nanocomposites and Properties