jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joseph Pugar | Calvin Gang | Christine S. Huang | Karl W. Haider | Newell R. Washburn
Source: ACS Applied Materials & Interfaces (article, 2022-03-30)
Field: Materials Science — Materials Chemistry

Predicting Young’s Modulus of Linear Polyurethane and Polyurethane–Polyurea Elastomers: Bridging Length Scales with Physicochemical Modeling and Machine Learning Predicting the properties of complex polymeric materials based on monomer chemistry requires modeling physical interactions that bridge molecular, interchain, microstructure, and bulk length scales. For polyurethanes, a polymer class with global commercial and industrial significance, these multiscale challenges are intrinsic due to the thermodynamic incompatibility of the urethane and polyol-rich domains, resulting in heterogeneities from molecular to microstructural length scales. Machine learning can model patterns in data to establish a relationship between the monomer chemistry and bulk material properties, but this is made difficult by small data sets and a diverse set of monomers. Using a data set of 63 industrially relevant and complex elastomers, we demonstrate that accurate machine learning predictions are possible when monomer chemistry is used to estimate interactions at interchain length scales. Here, these features were used to accurately (r2 = 0.91) predict the Young’s modulus of polyurethane and polyurethane–urea elastomers. Furthermore, by a query of the trained model for compositions that yield a target modulus within the range of accessible values, the capabilities of using this methodology as a design tool are demonstrated. The presented methodology could become increasingly useful in building models for materials with small data sets and may guide the interpretation of the underlying physicochemical forces. Polyurea ; Elastomer ; Materials science ; Monomer ; Polyurethane ; Polymer ; Modulus ; Bridging (networking) ; Polymer science ; Composite material ; Biological system ; Computer science Machine Learning in Materials Science ; Polymer composites and self-healing ; Computational Drug Discovery Methods
Authors: Wenjing Guo | Jie Liu | Fan Dong | Ru Chen | Jayanti Das | Weigong Ge | Xiaoming Xu | Huixiao Hong
Source: Nanomaterials (article, 2022-09-27)
Field: Chemistry — Inorganic Chemistry

Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials Metal-organic frameworks (MOFs), a class of porous nanomaterials, have been widely used in gas adsorption-based applications due to their high porosities and chemical tunability. To facilitate the discovery of high-performance MOFs for different applications, a variety of machine learning models have been developed to predict the gas adsorption capacities of MOFs. Most of the predictive models are developed using traditional machine learning algorithms. However, the continuously increasing sizes of MOF datasets and the complicated relationships between MOFs and their gas adsorption capacities make deep learning a suitable candidate to handle such big data with increased computational power and accuracy. In this study, we developed models for predicting gas adsorption capacities of MOFs using two deep learning algorithms, multilayer perceptron (MLP) and long short-term memory (LSTM) networks, with a hypothetical set of about 130,000 structures of MOFs with methane and carbon dioxide adsorption data at different pressures. The models were evaluated using 10 iterations of 10-fold cross validations and 100 holdout validations. The MLP and LSTM models performed similarly with high prediction accuracy. The models for predicting gas adsorption at a higher pressure outperformed the models for predicting gas adsorption at a lower pressure. The deep learning models are more accurate than the random forest models reported in the literature, especially for predicting gas adsorption capacities at low pressures. Our results demonstrated that deep learning algorithms have a great potential to generate models that can accurately predict the gas adsorption capacities of MOFs. Adsorption ; Nanomaterials ; Materials science ; Environmental science ; Chemical engineering ; Environmental chemistry ; Nanotechnology ; Chemistry ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Haoxin Mai | Tu C. Le | Dehong Chen | David A. Winkler | Rachel A. Caruso
Source: Advanced Science (review, 2022-10-26)
Field: Chemistry — Inorganic Chemistry

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment. Software deployment ; Greenhouse gas ; Computer science ; Process engineering ; Resource (disambiguation) ; Systems engineering ; Biochemical engineering ; Engineering ; Software engineering Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Alexandra Wahab | Lara Pfuderer | Eno Paenurk | Renana Gershoni‐Poranne
Source: Journal of Chemical Information and Modeling (article, 2022-07-26)
Field: Chemistry — Organic Chemistry

The COMPAS Project: A Computational Database of Polycyclic Aromatic Systems. Phase 1: <i>cata</i> -Condensed Polybenzenoid Hydrocarbons Chemical databases are an essential tool for data-driven investigation of structure-property relationships and for the design of novel functional compounds. We introduce the first phase of the COMPAS Project─a COMputational database of Polycyclic Aromatic Systems. In this phase, we developed two data sets containing the optimized ground-state structures and a selection of molecular properties of ∼34k and ∼9k <i>cata</i>-condensed polybenzenoid hydrocarbons (at the GFN2-xTB and B3LYP-D3BJ/def2-SVP levels, respectively) and placed them in the public domain. Herein, we describe the process of the data set generation, detail the information available within the data sets, and show the fundamental features of the generated data. We analyze the correlation between the two types of computations as well as the structure-property relationships of the calculated species. The data and insights gained from them can inform rational design of novel functional aromatic molecules for use in, e.g., organic electronics, and can provide a basis for additional data-driven machine- and deep-learning studies in chemistry. Phase (matter) ; Chemistry ; Gas phase ; Environmental chemistry ; Database ; Computer science ; Computational chemistry ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Graph theory and applications ; Fullerene Chemistry and Applications
Authors: Srihari M. Kastuar | Chinedu E. Ekuma | Zhongyue Liu
Source: Scientific Reports (article, 2022-03-08)
Field: Materials Science — Materials Chemistry

Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials An efficient automated toolkit for predicting the mechanical properties of materials can accelerate new materials design and discovery; this process often involves screening large configurational space in high-throughput calculations. Herein, we present the ElasTool toolkit for these applications. In particular, we use the ElasTool to study diversity of 2D materials and heterostructures including their temperature-dependent mechanical properties, and developed a machine learning algorithm for exploring predicted properties. Computer science ; Throughput ; Process (computing) ; Machine learning ; Materials science Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Jigyasa Nigam | Sergey N. Pozdnyakov | Guillaume Fraux | Michele Ceriotti
Source: arXiv (Cornell University) (article, 2022-02-03)
Field: Materials Science — Materials Chemistry

Unified theory of atom-centered representations and message-passing machine-learning schemes Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, that are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), that are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes, that gather information on the relationship between neighboring atoms using "message-passing" ideas, cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and provides a coherent foundation to systematize our understanding of both atom-centered and message-passing, invariant and equivariant machine-learning schemes. Invariant (physics) ; Atom (system on chip) ; Computer science ; Equivariant map ; Basis (linear algebra) ; Message passing ; Property (philosophy) ; Topology (electrical circuits) ; Theoretical computer science ; Statistical physics ; Physics ; Pure mathematics ; Mathematics ; Quantum mechanics ; Geometry ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Seo‐Yul Kim | Seungyun Han | Seulchan Lee | Jo Hong Kang | Sunghyun Yoon | Wanje Park | Min Woo Shin | Jin‐Young Kim | Yongchul G. Chung | Youn‐Sang Bae
Source: Advanced Science (article, 2022-05-07)
Field: Chemistry — Inorganic Chemistry

Discovery of High‐Performing Metal–Organic Frameworks for On‐Board Methane Storage and Delivery via LNG–ANG Coupling: High‐Throughput Screening, Machine Learning, and Experimental Validation Liquefied natural gas (LNG) gasification coupled with adsorbed natural gas (ANG) charging (LNG-ANG coupling) is an emerging strategy for efficient delivery of natural gas. However, the potential of LNG-ANG to attain the advanced research projects agency-energy (ARPA-E) target for onboard methane storage has not been fully investigated. In this work, large-scale computational screening is performed for 5446 metal-organic frameworks (MOFs), and over 193 MOFs whose methane working capacities exceed the target (315 cm<sup>3</sup> (STP) cm<sup>-3</sup> ) are identified. Furthermore, structure-performance relationships are realized under the LNG-ANG condition using a machine learning method. Additional molecular dynamics simulations are conducted to investigate the effects of the structural changes during temperature and pressure swings, further narrowing down the materials, and two synthetic targets are identified. The synthesized DUT-23(Cu) and DUT-23(Co) show higher working capacities (≈373 cm<sup>3</sup> (STP) cm<sup>-3</sup> ) than that of any other porous material under ANG or LNG-ANG conditions, and excellent stability during cyclic LNG-ANG operation. Throughput ; High-throughput screening ; Coupling (piping) ; Methane ; Metal-organic framework ; Materials science ; Computer science ; Chemistry ; Telecommunications ; Metallurgy ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis ; Zeolite Catalysis and Synthesis
Authors: Jiayan Xu | Wenbo Xie | Yulan Han | P. Hu
Source: ACS Catalysis (article, 2022-11-22)
Field: Materials Science — Materials Chemistry

Atomistic Insights into the Oxidation of Flat and Stepped Platinum Surfaces Using Large-Scale Machine Learning Potential-Based Grand-Canonical Monte Carlo Understanding catalyst surface structure changes under reactive conditions has become an important topic with the increasing interest in operando measurement and modeling. In this work, we develop a workflow to build machine learning potentials (MLPs) for simulating complicated chemical systems with large spatial and time scales, in which the committee model strategy equips the MLP with uncertainty estimation, enabling the active learning protocol. The methods are applied to constructing PtOx MLP based on explored configurations from bulk oxides to amorphous oxidized surfaces, which cover most ordered high-oxygen-coverage platinum surfaces within an accessible energy range. This MLP is used to perform large-scale grand canonical Monte Carlo simulations to track detailed structure changes during oxidations of flat and stepped Pt surfaces, which is normally inaccessible to costly ab initio calculations. These structural evolution trajectories reveal the stages of surface oxidation without laborious manual construction of surface models. We identify the building blocks of oxide formation and elucidate the surface oxide formation mechanism on Pt surfaces. The insightful interpretations would deeply help us understand the oxide formation on other metal surfaces. We demonstrate that these large-scale simulations would be a powerful tool to investigate realistic structures and the formation mechanisms of complicated systems.<br/><br/> Oxide ; Monte Carlo method ; Kinetic Monte Carlo ; Workflow ; Amorphous solid ; Scale (ratio) ; Computer science ; Ab initio ; Materials science ; Chemistry ; Nanotechnology ; Statistical physics ; Chemical physics ; Physics ; Database ; Mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Joseph H. Montoya | Muratahan Aykol | Abraham Anapolsky | Chirranjeevi Balaji Gopal | Patrick K. Herring | Jens S. Hummelshøj | Linda Hung | Ha-Kyung Kwon | Daniel Schweigert | Shijing Sun | Santosh K. Suram | Steven B. Torrisi | Amalie Trewartha | Brian D. Storey
Source: Applied Physics Reviews (article, 2022-01-13)
Field: Materials Science — Materials Chemistry

Toward autonomous materials research: Recent progress and future challenges The modus operandi in materials research and development is combining existing data with an understanding of the underlying physics to create and test new hypotheses via experiments or simulations. This process is traditionally driven by subject expertise and the creativity of individual researchers, who “close the loop” by updating their hypotheses and models in light of new data or knowledge acquired from the community. Since the early 2000s, there has been notable progress in the automation of each step of the scientific process. With recent advances in using machine learning for hypothesis generation and artificial intelligence for decision-making, the opportunity to automate the entire closed-loop process has emerged as an exciting research frontier. The future of fully autonomous research systems for materials science no longer feels far-fetched. Autonomous systems are poised to make the search for new materials, properties, or parameters more efficient under budget and time constraints, and in effect accelerate materials innovation. This paper provides a brief overview of closed-loop research systems of today, and our related work at the Toyota Research Institute applied across different materials challenges and identifies both limitations and future opportunities. Process (computing) ; Computer science ; Creativity ; Data science ; Automation ; Work in process ; Management science ; Artificial intelligence ; Knowledge management ; Engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Mustafa Şen | Elif Yüzer | Vakkas Doğan | İpek Avcı | Kenan Ensarioğlu | Ahmet Aykaç | Nusret Kaya | Mustafa Can | Volkan Kılıç
Source: Microchimica Acta (article, 2022-09-06)
Field: Materials Science — Materials Chemistry

Colorimetric detection of H2O2 with Fe3O4@Chi nanozyme modified µPADs using artificial intelligence Nanochemistry ; Detection limit ; Thermogravimetric analysis ; Fourier transform infrared spectroscopy ; Repeatability ; Colorimetry ; Computer science ; Materials science ; Spectroscopy ; Chemistry ; Support vector machine ; Artificial intelligence ; Analytical Chemistry (journal) ; Nanotechnology ; Chromatography ; Optics ; Physics Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Sai Praneeth Thota | Partha Pratim Bag | Praveen V. Vadlani | Siva Kumar Belliraj
Source: Engineered Science (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plant Biomass Derived Multidimensional Nanostructured Materials: A Green Alternative for Energy Storage Utilization of plant-based bioresources toward the exploration and development of novel nano materials for long-term sustainable energy storage could enhance cost competitiveness in terms of energy supply markets and reduction of environmental impacts, and meet the urgent need for green and sustainable development strategies. The latest research trend in energy storage is to focus on storage devices including supercapacitors (SC), lithium-ion batteries, fuel cells, and leadacid batteries. SCs are attractive due to their superior performance in power and energy densities along with prolonged life cycle and easy operating conditions in applications such as electric vehicles, portable electronics as well as stationary electric grids. Since SCs are constructed by non-renewable and fossil resources, urgent alternative effective materials are the need of the hour. Multidimensional high porosity nanostructured carbon from renewable biomass sources could be a promising greener alternative as electrode materials for SC. In SCs, porous nanocarbon derived from biomass acts as conductive layer on the electrode surface. Electrical conductivity, accessibility of electrolyte, pore structure and shape, pore size distribution, along with high surface area play major roles on the specific capacitance of the SC. This review comprises the latest research platforms on biomass-derived multidimensional nano carbon electrode materials for energy storage devices specifically for SCs and their future outlook. Supercapacitor ; Energy storage ; Renewable energy ; Materials science ; Biomass (ecology) ; Nanotechnology ; Environmental science ; Process engineering ; Fossil fuel ; Electrolyte ; Capacitance ; Electrode ; Waste management ; Electrical engineering ; Power (physics) ; Engineering ; Chemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Louisah M. Mahlaule-Glory | Nomso C. Hintsho‐Mbita
Source: Catalysts (review, 2022-07-29)
Field: Materials Science — Materials Chemistry

Green Derived Zinc Oxide (ZnO) for the Degradation of Dyes from Wastewater and Their Antimicrobial Activity: A Review The quest for eco-friendly synthetic routes that can be used for the development of multifunctional materials, in particular for water treatment, has reinforced the use of plant extracts as replacement solvents in their use as reducing and capping agents during the synthesis of green derived materials. Amongst the various nanoparticles, Zinc Oxide (ZnO) has emerged as one of the preferred candidates for photocatalysis due to its optical properties. Moreover, ZnO has also been reported to possess antimicrobial properties against various bacterial strains such as E. coli and S. aureus. In this review, various types of pollutants including organic dyes and natural pollutants are discussed. The treatment methods that are used to purify wastewater with their limitations are highlighted. The distinguishing properties of ZnO are clearly outlined and defined, not to mention the performance of ZnO as a green derived photocatalyst and an antimicrobial agent, as well. Lastly, an overview is given of the challenges and possible further perspectives. Antimicrobial ; Zinc ; Wastewater ; Photocatalysis ; Pollutant ; Nanotechnology ; Environmentally friendly ; Materials science ; Chemistry ; Biochemical engineering ; Environmental chemistry ; Environmental science ; Environmental engineering ; Organic chemistry ; Biology ; Engineering ; Catalysis Nanoparticles: synthesis and applications ; Nanomaterials for catalytic reactions ; Advanced Nanomaterials in Catalysis
Authors: Ruixiang Qu | Guoqiang Li
Source: Biosensors (review, 2022-03-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Overview of Liquid Crystal Biosensors: From Basic Theory to Advanced Applications Liquid crystals (LCs), as the remarkable optical materials possessing stimuli-responsive property and optical modulation property simultaneously, have been utilized to fabricate a wide variety of optical devices. Integrating the LCs and receptors together, LC biosensors aimed at detecting various biomolecules have been extensively explored. Compared with the traditional biosensing technologies, the LC biosensors are simple, visualized, and efficient. Owning to the irreplaceable superiorities, the research enthusiasm for the LC biosensors is rapidly rising. As a result, it is necessary to overview the development of the LC biosensors to guide future work. This article reviews the basic theory and advanced applications of LC biosensors. We first discuss different mesophases and geometries employed to fabricate LC biosensors, after which we introduce various detecting mechanisms involved in biomolecular detection. We then focus on diverse detection targets such as proteins, enzymes, nucleic acids, glucose, cholesterol, bile acids, and lipopolysaccharides. For each of these targets, the development history and state-of-the-art work are exhibited in detail. Finally, the current challenges and potential development directions of the LC biosensors are introduced briefly. Biosensor ; Nanotechnology ; Biomolecule ; Materials science ; Computer science Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Plant Reproductive Biology