jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Victor Fung | Jiaxin Zhang | Eric Juarez | Bobby G. Sumpter
Source: ChemRxiv (preprint, 2021-01-21)
Field: Materials Science — Materials Chemistry

Benchmarking Graph Neural Networks for Materials Chemistry Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis. However, a consistent benchmark of these models remains lacking, hindering the development and consistent evaluation of new models in the materials field. Here, we present a workflow and testing platform, MatDeepLearn, for quickly and reproducibly assessing and comparing GNNs and other machine learning models. We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry. From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized. We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs, due to learned rather than defined representations. Meanwhile several weaknesses of GNNs are also observed including high data requirements, and suggestions for further improvement for applications in materials chemistry are proposed. Benchmarking ; Computer science ; Workflow ; Machine learning ; Hyperparameter ; Artificial intelligence ; Benchmark (surveying) ; Graph ; Flexibility (engineering) ; Strengths and weaknesses ; Artificial neural network ; Theoretical computer science ; Database Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Fuel Cells and Related Materials
Authors: Yajing Sun | Wenping Hu
Source: SmartMat (article, 2021-10-29)
Field: Materials Science — Materials Chemistry

Novel machine learning framework for thermal conductivity prediction by crystal graph convolution embedded ensemble Abstract With the advent of the big data era, artificial intelligence technology has penetrated and deeply affected our daily life. In addition, data‐based machine learning algorithms have been applied to physics, chemistry, material science, and other basic science fields. However, the scarcity of data sets is known as the main obstacle to its development. Mining effective information from the limited data samples and building an appropriate machine learning algorithms framework are the major breakthroughs. For solid materials, the intrinsic properties are closely related to their atomic composition and relative positions, namely crystal structures. Here, inspired by the emerging of graph convolution neural network and material crystal graph, we proposed an integrated algorithms framework embedded crystal graph to train and predict the lattice thermal conductivities of crystal materials. This machine learning algorithms framework showed superior learning and generalization ability. In addition, not only in predicting thermal conductivities, but our framework also has great performance in predicting other phonon or electron‐related properties. This strategy provided a new approach in the design of machine learning framework, which indicated the great potential for the application of machine learning in material science. Machine learning ; Artificial intelligence ; Graph ; Computer science ; Algorithm ; Crystal (programming language) ; Thermal conductivity ; Artificial neural network ; Generalization ; Materials science ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Abdulelah S. Alshehri | Fengqi You
Source: Current Opinion in Chemical Engineering (article, 2021-10-25)
Field: Materials Science — Materials Chemistry

Machine learning for multiscale modeling in computational molecular design Resource (disambiguation) ; Computer science ; Process (computing) ; Product design ; Multiscale modeling ; Product (mathematics) ; Systems engineering ; Engineering design process ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Engineering ; Materials science ; Mechanical engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Josiah Roberts | Julia R. S. Bursten | Chad Risko
Source: Chemistry of Materials (article, 2021-08-20)
Field: Materials Science — Materials Chemistry

Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment Genetic algorithms (GA) and machine learning (ML) have a long history of development and use in chemistry. Recent algorithmic and computational advances, however, have brought these methods to the forefront of chemical research, and chemistry is experiencing a transformation in the way that machines and humans interact to pursue scientific advances. The field of materials chemistry, in particular, has witnessed a considerable expansion in the maturity of GA and ML approaches, as machine-based materials design ushers in a new era of materials development, discovery, and deployment. In addition to predicting new compositions and properties of bulk materials, GA and ML have also guided new insights into the structure, composition, and chemistry of materials surfaces. In this review, we focus on how GA and ML have been used in conjunction with chemical simulation techniques to advance understanding of surface chemistry, examining the history, recent work, and overall success of these applications. Perspective (graphical) ; Computer science ; Field (mathematics) ; Algorithm ; Nanotechnology ; Artificial intelligence ; Composition (language) ; Chemistry ; Machine learning ; Biochemical engineering ; Materials science ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: T. Honda | Shun Muroga | H. Nakajima | Taiyo Shimizu | Kazufumi Kobashi | Hiroshi Morita | Toshiya Okazaki | Kenji Hata
Source: Communications Materials (article, 2021-08-30)
Field: Materials Science — Materials Chemistry

Virtual experimentations by deep learning on tangible materials Abstract Artificial intelligence relying on structure-property databases is an emerging powerful tool to discover new materials with targeted properties. However, this approach cannot be easily applied to tangible structures, such as plastic composites and fabrics, because of their high structural complexity. Here, we propose a deep learning computational framework that can implement virtual experiments on tangible structures. Structural representations of complex carbon nanotube films were conducted by multiple generative adversarial networks of scanning electron microscope images at four levels of magnifications, enabling a deep learning prediction of multiple properties such as electrical conductivity and surface area. 1716 virtual experiments were completed within an hour, a task that would take years for real experiments. The data can be used as a versatile database for material science, in analogy to databases of molecules and solids used in cheminformatics. Useful examples are the investigation of correlations between electrical conductivity, specific surface area, wall number phase diagrams, economic performance, and inversely designed supercapacitors. Cheminformatics ; Computer science ; Deep learning ; Artificial intelligence ; Task (project management) ; Property (philosophy) ; Nanotechnology ; Materials science ; Engineering ; Systems engineering ; Chemistry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Computational Drug Discovery Methods
Authors: Luis Itza Vazquez-Salazar | Eric D. Boittier | Oliver T. Unke | Markus Meuwly
Source: Journal of Chemical Theory and Computation (article, 2021-07-21)
Field: Materials Science — Materials Chemistry

Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies An essential aspect for adequate predictions of chemical properties by machine learning models is the database used for training them. However, studies that analyze how the content and structure of the databases used for training impact the prediction quality are scarce. In this work, we analyze and quantify the relationships learned by a machine learning model (Neural Network) trained on five different reference databases (QM9, PC9, ANI-1E, ANI-1, and ANI-1x) to predict tautomerization energies from molecules in Tautobase. For this, characteristics such as the number of heavy atoms in a molecule, number of atoms of a given element, bond composition, or initial geometry on the quality of the predictions are considered. The results indicate that training on a chemically diverse database is crucial for obtaining good results and also that conformational sampling can partly compensate for limited coverage of chemical diversity. The overall best-performing reference database (ANI-1x) performs on average by 1 kcal/mol better than PC9, which, however, contains about 2 orders of magnitude fewer reference structures. On the other hand, PC9 is chemically more diverse by a factor of ∼5 as quantified by the number of atom-in-molecule-based fragments (amons) it contains compared with the ANI family of databases. A quantitative measure for deficiencies is the Kullback-Leibler divergence between reference and target distributions. It is explicitly demonstrated that when certain types of bonds need to be covered in the target database (Tautobase) but are undersampled in the reference databases, the resulting predictions are poor. Examples of this include the poor performance of all databases analyzed to predict C(sp<sup>2</sup>)-C(sp<sup>2</sup>) double bonds close to heteroatoms and azoles containing N-N and N-O bonds. Analysis of the results with a Tree MAP algorithm provides deeper understanding of specific deficiencies in predicting tautomerization energies by the reference datasets due to inadequate coverage of chemical space. Capitalizing on this information can be used to either improve existing databases or generate new databases of sufficient diversity for a range of machine learning (ML) applications in chemistry. Database ; Tautomer ; Divergence (linguistics) ; Computer science ; Quality (philosophy) ; Atom (system on chip) ; Artificial neural network ; Molecule ; Machine learning ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: R.I. Zubatyuk | Justin S. Smith | Benjamin Nebgen | Sergei Tretiak | Olexandr Isayev
Source: ChemRxiv (preprint, 2021-04-12)
Field: Materials Science — Materials Chemistry

Teaching a Neural Network to Attach and Detach Electrons from Molecules Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry. In particular, interatomic potentials derived with Machine Learning algorithms such as Deep Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. The applicability domain of DNN potentials is usually limited by the type of training data. As such, transferable models are aimed to be extensible in the description of chemical and conformational diversity of organic molecules. However, most DNN potentials, such as the AIMNet model we proposed previously, were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we extend machine learning framework toward open-shell anions and cations. We introduce AIMNet-NSE (Neural Spin Equilibration) architecture, which being properly trained, could predict atomic and molecular properties for an arbitrary combination of molecular charge and spin multiplicity. This model explores a new dimension of transferability by adding the charge-spin space. The AIMNet-NSE model is capable of reproducing reference QM energies for cations, neutrals, and anions with errors of about 2-3 kcal/mol, compared to the reference QM simulations. The spin-charges have errors ~0.01 electrons for small organic molecules containing nine chemical elements {H, C, N, O, F, Si, P, S and Cl}. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions with a speed up to 104 molecules per second on a single modern GPU. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions. Artificial neural network ; Electronegativity ; Density functional theory ; Electron ; Ion ; Chemical space ; Molecule ; Electron affinity (data page) ; Open shell ; Ionization ; Physics ; Formal charge ; Chemistry ; Computer science ; Chemical physics ; Statistical physics ; Computational chemistry ; Atomic physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Lucas Dall Agnol | Heitor Luiz Ornaghi | Francisco Maciel Monticeli | Fernanda Trindade Gonzalez Dias | Otávio Bianchi
Source: Polymer Engineering and Science (article, 2021-04-27)
Field: Materials Science — Polymers and Plastics

Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization Abstract The molar mass of the polyurethanes (PUs)' reagents directly influences their thermal response, affecting both the polymerization process and the enthalpy and the degree of reaction. This study reports applying an artificial neural network (ANN), associated with surface response methodology (SRM) models, to predict the calorimetric behavior of certain PU's bulk polymerizations. A noncatalyzed reaction between an aliphatic hexamethylene diisocyanate (HDI) and a polycarbonate diol (PCD) with distinct molar masses (500, 1000, and 2000 g/mol) was proposed. A high level of reliability of the predicted calorimetric curves was obtained due to an excellent agreement between theoretical and modeled results, enabling creating a 3D surface response to predict the reaction kinetics. Also, it was possible to observe that the polymerization kinetics is affected by the OH group's association phenomena. The applied methodology can be extended for other materials or properties of interest. Molar mass ; Materials science ; Polymerization ; Degree of polymerization ; Kinetics ; Enthalpy ; Degree (music) ; Polymer chemistry ; Mass fraction ; Diol ; Artificial neural network ; Chemical engineering ; Thermodynamics ; Polymer ; Composite material ; Computer science Polymer composites and self-healing ; Polymer Nanocomposites and Properties ; Epoxy Resin Curing Processes
Authors: Xiuyang Lü | Yujing Wu | Xuanjun Wu | Zhixiang Cao | Xionghui Wei | Weiquan Cai
Source: AIChE Journal (article, 2021-09-12)
Field: Materials Science — Materials Chemistry

High‐throughput computational screening of porous polymer networks for natural gas sweetening based on a neural network Abstract The capture and storage of toxic industrial chemicals such as H 2 S using porous polymer networks (PPNs) has shown promising application because of their high porosity, high surface area, high stability, low‐cost and lightweight. In this work, 17,846 PPNs with the diamond‐like topology were computationally screened to identify the optimal adsorbents for the removal of H 2 S and CO 2 from humid natural gas based on the combination of molecular simulation and machine learning algorithms. The top‐performing PPNs such as hPAFs‐0201 with the highest adsorption performance scores (APS) were evaluated and identified based on their adsorption capacities and selectivity for H 2 S and CO 2 . The strong affinity between water molecules and the framework atoms in a few PPNs has a significant impact on the adsorption selectivity of acid gases. Based on decision tree analysis, we found two main design paths of the optimal PPNs for natural gas sweetening, which are the PPNs with LCD ≤ 4.648 Å, V f ≤ 0.035, and PLD ≤ 3.889 Å, and those with 4.648 Å ≤ LCD ≤ 5.959 Å, ρ ≤ 837 kg m −3 . In addition, we constructed different machine learning models, particularly artificial neural network, available to accurately predict the APS of PPNs. 2D projection map of geometrical properties of PPNs using the t‐distributed stochastic neighbor embedding (t‐SNE) method shows that the screened 390 samples exhibit the similar structures. Among the top‐23 PPNs with the highest APS, hPAFs‐0201 has enhanced natural gas sweetening performance due to its strong affinity between the N‐rich organic linkers and acid gases. hPAFs‐0752 shows the highest isosteric adsorption heat of H 2 S and CO 2 ( Q ° st = 49.84 kJ mol −1 ), resulting in its second‐highest APS as well as high hydrophilicity. Based on the combination of molecular simulation and machine learning, comprehensive insights into the high‐throughput screening of PPNs in this work will provide new ideas for the design of high‐performance PPNs for gas separation. Adsorption ; Porosity ; Sweetening ; Natural gas ; Polymer ; Materials science ; Artificial neural network ; Nanotechnology ; Selectivity ; Chemical engineering ; Biological system ; Computer science ; Artificial intelligence ; Chemistry ; Engineering ; Organic chemistry ; Composite material ; Sweetening agents Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Won Jung Lee | H. Shaun Kwak | Deuk-rak Lee | Chunrim Oh | Eul Kgun Yum | Yuling An | Mathew D. Halls | Chi‐Wan Lee
Source: Chemistry of Materials (article, 2021-12-29)
Field: Materials Science — Materials Chemistry

Design and Synthesis of Novel Oxime Ester Photoinitiators Augmented by Automated Machine Learning A new, accelerated design scheme for photoinitiators based on an advanced machine learning framework is studied. Design space for photoinitiators is set by over 120 unique oxime ester compounds synthesized and measured for their photosensitivity. Then, an automated machine learning algorithm is used for rapidly identifying the best quantitative structure–property relationship (QSPR) models among hundreds that are generated, ranked, and validated in an automated fashion to predict photosensitivity. Top-performing models are highly predictive with coefficients of determination of around 0.8 for compounds that are unknown to the models. Visual interpretation of the predictive models based on atom-site contributions offers a clear and intuitive direction to design new photoinitiators. Based on the machine learning-assisted analysis, three new oxime ester compounds were pushed for synthesis and further evaluation as novel photoinitiators. Experimental validation confirms high photosensitivity in all of the newly synthesized candidates. The work demonstrates the value of combining synthesis with the automated machine learning framework as a fast and reliable measure, which provides unbiased insights often hidden in high-dimensional data space. Oxime ; Quantitative structure–activity relationship ; Computer science ; Photosensitivity ; Artificial intelligence ; Machine learning ; Biological system ; Set (abstract data type) ; Algorithm ; Materials science ; Chemistry ; Organic chemistry ; Programming language Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Photopolymerization techniques and applications
Authors: Jihua Chen
Source: Nanomaterials (review, 2021-09-15)
Field: Materials Science — Materials Chemistry

Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications After decades of developments, electron microscopy has become a powerful and irreplaceable tool in understanding the ionic, electrical, mechanical, chemical, and other functional performances of next-generation polymers and soft complexes. The recent progress in electron microscopy of nanostructured polymers and soft assemblies is important for applications in many different fields, including, but not limited to, mesoporous and nanoporous materials, absorbents, membranes, solid electrolytes, battery electrodes, ion- and electron-transporting materials, organic semiconductors, soft robotics, optoelectronic devices, biomass, soft magnetic materials, and pharmaceutical drug design. For synthetic polymers and soft complexes, there are four main characteristics that differentiate them from their inorganic or biomacromolecular counterparts in electron microscopy studies: (1) lower contrast, (2) abundance of light elements, (3) polydispersity or nanomorphological variations, and (4) large changes induced by electron beams. Since 2011, the Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory has been working with numerous facility users on nanostructured polymer composites, block copolymers, polymer brushes, conjugated molecules, organic-inorganic hybrid nanomaterials, organic-inorganic interfaces, organic crystals, and other soft complexes. This review crystalizes some of the essential challenges, successes, failures, and techniques during the process in the past ten years. It also presents some outlooks and future expectations on the basis of these works at the intersection of electron microscopy, soft matter, and artificial intelligence. Machine learning is expected to automate and facilitate image processing and information extraction of polymer and soft hybrid nanostructures in aspects such as dose-controlled imaging and structure analysis. Electron microscope ; Materials science ; Nanotechnology ; Polymer ; Polymer science ; Biomedical engineering ; Chemical engineering ; Medicine ; Composite material ; Engineering ; Optics ; Physics Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Porphyrin and Phthalocyanine Chemistry