jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haonan Fan | Xiaodie Yang | Jing Ma | Biaobiao Hao | Fakhre Alam | Xumeng Huang | Aixi Wang | Tao Jiang
Source: Journal of Catalysis (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Computer-assisted design of asymmetric PNP ligands for ethylene tri-/tetramerization: A combined DFT and artificial neural network approach The combination of computational and experimental sciences accelerates the design and development of molecular catalysts. A general strategy for developing ethylene oligomerization catalysts is still lacking. Consequently, herein, we proposed a widely applicable strategy for designing ethylene oligomerization catalysts. We combined density functional theory (DFT) and an artificial neural network (ANN) to establish a relation between catalyst structure and performance. The structure optimization and electronic calculation of a series of asymmetric PNP/Cr active species were conducted using DFT, and the steric and electronic descriptors were extracted to establish datasets. The catalyst prediction model was constructed using ANN and the leave-one-out cross-validation (LOOCV) method was used to verify the generalization ability of the models. The optimized ANN-based models used to predict 1-hexene and 1-octene selectivity exhibited high R2 values, which indicates satisfactory prediction accuracy of the models. We designed new PNP ligands and successfully predicted the ethylene oligomerization performance of PNP/Cr precatalysts using ANN-based models, which were verified through experiments. In addition, we found that the steric properties more significantly affect the performance of precatalysts than the electronic properties. Steric effects ; Chemistry ; Artificial neural network ; Ethylene ; Catalysis ; Generalization ; Electronic effect ; Selectivity ; Density functional theory ; Quantitative structure–activity relationship ; Biological system ; Computational chemistry ; Molecular model ; Combinatorial chemistry ; Artificial intelligence ; Computer science ; Organic chemistry ; Stereochemistry ; Mathematics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis
Authors: Filippo Bigi | Guillaume Fraux | Nicholas J. Browning | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2023-08-08)
Field: Materials Science — Materials Chemistry

Fast evaluation of spherical harmonics with <tt>sphericart</tt> Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted basis to expand functions on a sphere, and they are used routinely in physical and theoretical chemistry as well as in different fields of science and technology, from geology and atmospheric sciences to signal processing and computer graphics. More recently, they have become a key component of rotationally equivariant models in geometric machine learning, including applications to atomic-scale modeling of molecules and materials. We present an elegant and efficient algorithm for the evaluation of the real-valued spherical harmonics. Our construction features many of the desirable properties of existing schemes and allows us to compute Cartesian derivatives in a numerically stable and computationally efficient manner. To facilitate usage, we implement this algorithm in sphericart, a fast C++ library that also provides C bindings, a Python API, and a PyTorch implementation that includes a GPU kernel. Spherical harmonics ; Zonal spherical harmonics ; Python (programming language) ; Computational science ; Computer science ; Equivariant map ; CUDA ; Cartesian coordinate system ; Computation ; Spin-weighted spherical harmonics ; Kernel (algebra) ; Graphics ; Theoretical computer science ; Harmonics ; Algorithm ; Computer graphics (images) ; Geometry ; Parallel computing ; Mathematics ; Vector spherical harmonics ; Physics ; Programming language ; Pure mathematics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Sina Hossein Zadeh | Amir Behbahanian | John Broucek | Mingzhou Fan | Guillermo Vazquez | Mohammad Noroozi | William Trehern | Xiaoning Qian | İbrahim Karaman | Raymundo Arróyave
Source: Computational Materials Science (article, 2023-04-27)
Field: Materials Science — Materials Chemistry

An interpretable boosting-based predictive model for transformation temperatures of shape memory alloys Categorical variable ; Shape-memory alloy ; Austenite ; Martensite ; Transformation (genetics) ; Diffusionless transformation ; Materials science ; Alloy ; Computer science ; Boosting (machine learning) ; Artificial intelligence ; Algorithm ; Machine learning ; Metallurgy ; Microstructure ; Chemistry Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; High Entropy Alloys Studies
Authors: Naoya Ishizuki | Ryota Shimizu | Taro Hitosugi
Source: Science and Technology of Advanced Materials Methods (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Autonomous experimental systems in materials science The emergence of autonomous experimental systems integrating machine learning and robots is ushering in a paradigm shift in materials science. Using computer algorithms and robots to decide and perform all experimental steps, these systems require no human intervention. A current direction focuses on discovering unexpected materials and theories with unconventional research approaches. This article reviews the latest achievements and discusses the impact of autonomous experimental systems, which will fundamentally change the way we understand research. Moreover, as autonomous experimental systems continue to develop, the need to think about the role of human researchers becomes more pressing. While machine learning and robotics can free us from the repetitive aspects of research, we need to understand the strengths and limitations of machine learning and robots and focus on how humans can perform higher creativity. In addition, we also discuss inventorship and authorship in the era of autonomous systems. Robot ; Artificial intelligence ; Computer science ; Robotics ; Paradigm shift ; Creativity ; Human–computer interaction ; Focus (optics) ; Data science ; Cognitive science ; Epistemology ; Psychology Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electronic and Structural Properties of Oxides
Authors: Manideepa Dhar | Ufuoma I. Kara | Supriya Das | Yang Xu | Sohini Mandal | Robert L. Dupont | Eric C. Boerner | Boyuan Chen | Yuxing Yao | Xiaoguang Wang | Uttam Manna
Source: Materials Horizons (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Design of a self-cleanable multilevel anticounterfeiting interface through covalent chemical modulation Counterfeit products have posed a significant threat to consumers safety and the global economy. To address this issue, extensive studies have been exploring the use of coatings with unclonable, microscale features for authentication purposes. However, the ease of readout, and the stability of these features against water, deposited dust, and wear, which are required for practical use, remain challenging. Here we report a novel class of chemically functionalizable coatings with a combination of a physically unclonable porous topography and distinct physiochemical properties (<i>e.g.</i>, fluorescence, water wettability, and water adhesion) obtained through orthogonal chemical modifications (<i>i.e.</i>, 1,4-conjugate addition reaction and Schiff-base reaction at ambient conditions). Unprecedentedly, a self-cleanable and physically unclonable coating is introduced to develop a multilevel anticounterfeiting interface. We demonstrate that the authentication of the fluorescent porous topography can be verified using deep learning. More importantly, the spatially selective chemical modifications can be read with the naked eye <i>via</i> underwater exposure and UV light illumination. Overall, the results reported in this work provide a facile basis for designing functional surfaces capable of independent and multilevel decryption of authenticity. Microscale chemistry ; Materials science ; Coating ; Nanotechnology ; Authentication (law) ; Interface (matter) ; Computer science ; Naked eye ; Fluorescence ; Wetting ; Computer security ; Optics Luminescence and Fluorescent Materials ; Surface Modification and Superhydrophobicity ; Nanomaterials and Printing Technologies
Authors: Lei Chen | Andreas Erlebach | Federico Brivio | Lukáš Grajciar | Zdeněk Tošner | Christopher J. Heard | Petr Nachtigall
Source: Chemical Science (article, 2023-01-01)
Field: Chemistry — Spectroscopy

The need for <i>operando</i> modelling of <sup>27</sup> Al NMR in zeolites: the effect of temperature, topology and water Solid state (ss-) <sup>27</sup>Al NMR is one of the most valuable tools for the experimental characterization of zeolites, owing to its high sensitivity and the detailed structural information which can be extracted from the spectra. Unfortunately, the interpretation of ss-NMR is complex and the determination of aluminum distributions remains generally unfeasible. As a result, computational modelling of <sup>27</sup>Al ss-NMR spectra has grown increasingly popular as a means to support experimental characterization. However, a number of simplifying assumptions are commonly made in NMR modelling, several of which are not fully justified. In this work, we systematically evaluate the effects of various common models on the prediction of <sup>27</sup>Al NMR chemical shifts in zeolites CHA and MOR. We demonstrate the necessity of <i>operando</i> modelling; in particular, taking into account the effects of water loading, temperature and the character of the charge-compensating cation. We observe that conclusions drawn from simple, high symmetry model systems such as CHA do not transfer well to more complex zeolites and can lead to qualitatively wrong interpretations of peak positions, Al assignment and even the number of signals. We use machine learning regression to develop a simple yet robust relationship between chemical shift and local structural parameters in Al-zeolites. This work highlights the need for sophisticated models and high-quality sampling in the field of NMR modelling and provides correlations which allow for the accurate prediction of chemical shifts from dynamical simulations. Topology (electrical circuits) ; Zeolite ; Chemistry ; Materials science ; Physical chemistry ; Organic chemistry ; Catalysis ; Engineering ; Electrical engineering Advanced NMR Techniques and Applications ; Zeolite Catalysis and Synthesis ; Chemical Synthesis and Characterization
Authors: Antonios P. Sarikas | George S. Fanourgakis | Emmanuel Tylianakis | Konstantinos Gkagkas | George E. Froudakis
Source: The Journal of Physical Chemistry C (article, 2023-10-20)
Field: Chemistry — Inorganic Chemistry

Comparison of Energy-Based Machine Learning Descriptors for Gas Adsorption Metal–organic frameworks (MOFs) are a class of nanoporous materials that hold great promise for applications involving adsorption. The structural and chemical tunability of MOFs has led to the realization of an enormous number of materials through experimental or in silico techniques. Although a large candidate pool is desirable, the great number of possible solutions renders its exploration nontrivial. With the advent of machine learning (ML), the identification of promising materials for specific applications can be performed in a matter of seconds with the proviso that ML models are accurate and their required input is (computationally) cheap. With regard to gas adsorption in MOFs, energy-based descriptors can significantly improve the performance of ML models compared to structural descriptors, especially when non-negligible host–guest interactions are present. In this work, we investigate the impact of Dprobes and Henry coefficients, two energy-based descriptors, on the performance of ML models, examining CO2, H2S, and H2 under different adsorption conditions. The ML models built with Dprobes perform in general better, and their approximately 2 orders of magnitude faster calculation (compared to Henry coefficients) enables efficient large-scale screening. Nanoporous ; Adsorption ; Computer science ; Realization (probability) ; Metal-organic framework ; In silico ; Biological system ; Artificial intelligence ; Machine learning ; Process engineering ; Materials science ; Nanotechnology ; Chemistry ; Mathematics ; Engineering ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Ngoc Cuong Nguyen | Andrew Rohskopf
Source: Journal of Computational Physics (article, 2023-02-24)
Field: Materials Science — Materials Chemistry

Proper orthogonal descriptors for efficient and accurate interatomic potentials Representation (politics) ; Convergence (economics) ; Atom (system on chip) ; Orthogonal basis ; Set (abstract data type) ; Mathematics ; Algorithm ; Computer science ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Gang Wang | Peng Hu
Source: International Journal of Refrigeration (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

Prediction of normal boiling point and critical temperature of refrigerants by graph neural network and transfer learning Refrigerant ; Artificial neural network ; Transfer of learning ; Boiling heat transfer ; Boiling point ; Graph ; Computer science ; Machine learning ; Artificial intelligence ; Thermodynamics ; Heat transfer ; Theoretical computer science ; Physics ; Heat transfer coefficient ; Nucleate boiling Machine Learning in Materials Science ; Heat Transfer and Boiling Studies ; Refrigeration and Air Conditioning Technologies
Authors: Yushun Liu | Cheng Yang | Li Lv | Xin Zeng | Lingzhi Xia | Senlin Li | Jing Liu | Fei Kong | Tao Shao
Source: Journal of Physics D Applied Physics (article, 2023-07-14)
Field: Materials Science — Materials Chemistry

Evaluation of aging and degradation for silicone rubber composite insulator based on machine learning Abstract Silicone rubbers (SIRs), as the main material of composite insulator sheds, have aging phenomenon for the long-term operation in the outdoor, which has an important impact on the performance degradation of composite insulator. Periodic examine and replacement of severely aged composite insulator is of great significance for the safe operation of power system. However, there is no clear standard to estimate the aging degree. In order to evaluate the degree of aging and degradation more accurately, the experimental test and analysis of the SIR sheds were carried out from three aspects: physical characteristics, chemical composition and electrical properties. The physical characteristic results show that the surface of the aging SIR will appear obvious holes and cracks, as well as hardening. Chemical composition results show that the internal composition of the continuous decomposition, while a large number of heavy metal elements accumulate in the pollution. The change of physical characteristics and chemical composition of SIR sheds may lead to the deterioration of electrical properties. According to the above results, the influence rule of measurement parameters and aging degree was obtained, and the key characteristic parameters of aging were summarized and extracted. Based on the characteristic parameters, a decision tree aging evaluation model is established for state evaluation. The accuracy of machine learning is 100% and 93.2% respectively. For the on-site application, early warning of moderately aged insulators and replacement of severely aged insulators are proposed in the on-site maintenance of SIR composite insulators. Silicone rubber ; Composite number ; Accelerated aging ; Materials science ; Insulator (electricity) ; Composite material ; Hardening (computing) ; Environmental science High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Infrastructure Maintenance and Monitoring
Authors: Heitor Luiz Ornaghi | Andrés Nohales | M. C. Asensio | Clara M. Gómez | Otávio Bianchi
Source: Polymer Bulletin (article, 2023-05-12)
Field: Materials Science — Polymers and Plastics

Effect of chain extenders on the thermal and thermodegradation behavior of carbonatodiol thermoplastic polyurethane Thermogravimetric analysis ; Materials science ; Differential scanning calorimetry ; Degradation (telecommunications) ; Thermoplastic polyurethane ; Polyurethane ; Chemical engineering ; Thermoplastic ; Composite material ; Thermal stability ; Computer science ; Elastomer ; Thermodynamics Polymer composites and self-healing ; Carbon dioxide utilization in catalysis ; biodegradable polymer synthesis and properties
Authors: Debayan Dey | Ryouichi Tanaka | Hisashi Itô
Source: Journal of Molecular Evolution (article, 2023-03-03)
Field: Chemistry — Inorganic Chemistry

Structural Characterization of the Chlorophyllide a Oxygenase (CAO) Enzyme Through an In Silico Approach Monooxygenase ; Biology ; Heme ; Oxidoreductase ; In silico ; Ferredoxin ; Heme oxygenase ; Oxygenase ; Chlamydomonas reinhardtii ; Biochemistry ; Stereochemistry ; Enzyme ; Chemistry ; Cytochrome P450 ; Gene ; Mutant Metal-Catalyzed Oxygenation Mechanisms ; Photosynthetic Processes and Mechanisms ; Microbial bioremediation and biosurfactants
Authors: Yuanpeng Deng | Chong Wang | Xiang Xu | Hui Li
Source: Theoretical and Applied Mechanics Letters (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Machine learning potential for Ab Initio phase transitions of zirconia Zirconia has been extensively used in aerospace, military, biomedical and industrial fields due to its unusual combination of high mechanical, electrical and thermal properties. However, the fundamental and critical phase transition process of zirconia has not been well studied because of its difficult first-order phase transition with formidable energy barrier. Here, we generated a machine learning interatomic potential with ab initio accuracy to discover the mechanism behind all kinds of phase transition of zirconia at ambient pressure. The machine learning potential precisely characterized atomic interactions among all zirconia allotropes and liquid zirconia in a wide temperature range. We realized the challenging reversible first-order monoclinic-tetragonal and cubic-liquid phase transition processes with enhanced sampling techniques. From the thermodynamic information, we gave a better understanding of the thermal hysteresis phenomenon in martensitic monoclinic-tetragonal transition. The phase diagram of zirconia from our machine learning potential based molecular dynamics simulations corresponded well with experimental results. Monoclinic crystal system ; Cubic zirconia ; Phase diagram ; Materials science ; Phase transition ; Tetragonal crystal system ; Ab initio ; Chemical physics ; Phase (matter) ; Hysteresis ; Condensed matter physics ; Thermodynamics ; Crystallography ; Crystal structure ; Chemistry ; Metallurgy ; Ceramic ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Phase Equilibria and Thermodynamics
Authors: Dongxu Cui | Anna Ściążko | Yosuke Komatsu | A. Nakamura | Toru Hara | Shiliang Wu | Rui Xiao | Naoki Shikazono
Source: Journal of Energy Chemistry (article, 2023-09-04)
Field: Materials Science — Materials Chemistry

Three dimensional microstructures of carbon deposition on Ni-YSZ anodes under polarization Materials science ; Microstructure ; Scanning electron microscope ; Yttria-stabilized zirconia ; Anode ; Polarization (electrochemistry) ; Electrolyte ; Carbon fibers ; Deposition (geology) ; Volume fraction ; Methane ; Chemical engineering ; Composite material ; Electrode ; Ceramic ; Chemistry ; Cubic zirconia Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Tianyi Jin | Connor W. Coley | Alfredo Alexander‐Katz
Source: Macromolecules (article, 2023-02-28)
Field: Materials Science — Surfaces, Coatings and Films

Adsorption of Biomimetic Amphiphilic Heteropolymers onto Graphene and Its Derivatives The adsorption of a polymer chain from a bulk solution onto a solid surface is the fundamental mechanism behind many industrial and biological applications. Here, we used molecular dynamics simulations to investigate the driving factors in the adsorption of a poly(methyl methacrylate)-based biomimetic random heteropolymer (RHP) containing polar (ethylene glycol, or PEG), nonpolar (ethylhexyl), and charged groups (sulfopropyl acid) onto graphene and its derivatives (graphene oxide and carbon nanotube). We applied a combination of unbiased and biased simulations with machine learning to investigate the adsorption mechanism. The long PEG side chains drive the nearby short monomers to be adsorbed. After a short period of side-chain adsorption, due to the glassy nature of the backbone, the adsorption terminates and will only proceed by adding plasticizers, changing the backbone to be more flexible, or raising the temperature over the glass transition temperature. Meanwhile, thermodynamically, the adsorption is favorable due to the amphiphilicity of PEG. The surface hydrophilicity and curvature influence the adsorption as well. This work provides atomistic details of the interaction between an amphiphilic RHP and a hard surface, which can serve as the basis to understand the adsorption of such RHPs on more complex interfaces such as protein surfaces. Adsorption ; Graphene ; Amphiphile ; Polymer ; Polymer adsorption ; Ethylene glycol ; Monomer ; Chemical engineering ; Molecular dynamics ; Chemistry ; Protein adsorption ; Side chain ; Oxide ; Ethylene oxide ; Materials science ; Chemical physics ; Polymer chemistry ; Organic chemistry ; Nanotechnology ; Computational chemistry ; Copolymer Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications ; Advanced Sensor and Energy Harvesting Materials
Authors: D. Vignesh | Ela Rout
Source: Computational Materials Science (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Machine learning advent and derivative discontinuity of DFT functionals over gap state predictions among ACeO3 (A = Ba2+, Sr2+, Ca2+, Mg2+) proton conductors Hybrid functional ; Density functional theory ; Generalization ; Band gap ; Proton ; Statistical physics ; Materials science ; Discontinuity (linguistics) ; Stability (learning theory) ; Computational chemistry ; Chemistry ; Physics ; Chemical physics ; Computer science ; Condensed matter physics ; Quantum mechanics ; Mathematics ; Machine learning ; Mathematical analysis Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Benjamín Plá | Pedro Piqueras | Pau Bares | André Nakaema Aronis
Source: Fuel (article, 2023-02-26)
Field: Materials Science — Materials Chemistry

Model-based simultaneous diagnosis of ammonia injection failure and catalyst ageing in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg" display="inline" id="d1e1075"><mml:msub><mml:mrow><mml:mi mathvariant="normal">deNO</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">x</mml:mi></mml:mrow></mml:msub></mml:math> engine after-treatment systems NOx ; Extended Kalman filter ; Ammonia ; Computer science ; Automotive engineering ; Environmental science ; Kalman filter ; Process engineering ; Materials science ; Chemistry ; Engineering ; Artificial intelligence Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Advanced Combustion Engine Technologies