jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Paul Lafourcade | Jean‐Bernard Maillet | Christophe Denoual | Eléonore Duval | Arnaud Allera | Alexandra M. Goryaeva | Mihai‐Cosmin Marinica
Source: arXiv (Cornell University) (preprint, 2023-07-04)
Field: Materials Science — Materials Chemistry

Robust crystal structure identification at extreme conditions using a density-independent spectral descriptor and supervised learning The increased time- and length-scale of classical molecular dynamics simulations have led to raw data flows surpassing storage capacities, necessitating on-the-fly integration of structural analysis algorithms. As a result, algorithms must be computationally efficient, accurate, and stable at finite temperature to reliably extract the relevant features of the data at simulation time. In this work, we leverage spectral descriptors to encode local atomic environments and build crystal structure classification models. In addition to the classical way spectral descriptors are computed, i.e. over a fixed radius neighborhood sphere around a central atom, we propose an extension to make them independent from the material's density. Models are trained on defect-free crystal structures with moderate thermal noise and elastic deformation, using the linear discriminant analysis (LDA) method for dimensionality reduction and logistic regression (LR) for subsequent classification. The proposed classification model is intentionally designed to be simple, incorporating only a limited number of parameters. This deliberate simplicity enables the model to be trained effectively even when working with small databases. Despite the limited training data, the model still demonstrates inherent transferability, making it applicable to a broader range of scenarios and datasets. The accuracy of our models in extreme conditions is compared to traditional algorithms from the literature, namely adaptive common neighbor analysis (a-CNA), polyhedral template matching (PTM) and diamond structure identification (IDS). Finally, we showcase two applications of our method: tracking a solid-solid BCC-to-HCP phase transformation in Zirconium at high pressure up to high temperature, and visualizing stress-induced dislocation loop expansion in single crystal FCC Aluminum containing a Frank-Read source, at high temperature. Leverage (statistics) ; Computer science ; Artificial intelligence ; Machine learning ; Algorithm ; Initialization ; Curse of dimensionality ; Pattern recognition (psychology) ; Data mining Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: د.زمن عبدالحسين عبادي | د.مصطفى كاظم نعيم | د.مهند دوهان عبيد | د.احمد وحيد راضي
Source: Mağallaẗ Al-Maʿhad Al-ʿālī Lil Dirāsāt Al-Nawʿiyyaẗ /Mağallaẗ Al-Maʿhad Al-ʿālī Lil Dirāsāt Al-Nawʿiyyaẗ (article, 2023-07-01)
Field: Chemistry — Organic Chemistry

Recent Developments in Organic Synthesis: Strategies for the Design and Assembly of Complex Molecules This research paper provides an overview of recent advancements in the field of organic synthesis, focusing on innovative strategies for the design and assembly of complex molecules. The study encompasses a comprehensive review of cutting-edge methodologies, including retrosynthetic analysis, catalytic transformations, and novel reagents. It explores how these strategies have revolutionized the synthesis of intricate organic compounds with applications in medicinal chemistry, materials science, and beyond. Nanotechnology ; Chemistry ; Combinatorial chemistry ; Materials science Inorganic and Organometallic Chemistry ; Asymmetric Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis
Authors: Cristina Caruso | Annalisa Cardellini | Martina Crippa | Daniele Rapetti | Giovanni M. Pavan
Source: arXiv (Cornell University) (preprint, 2023-02-19)
Field: Chemistry — Spectroscopy

TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time-variations of SOAP spectra Many molecular systems and physical phenomena are controlled by local fluctuations and microscopic dynamical rearrangements of the constitutive interacting units that are often difficult to detect. This is the case, for example, of phase transitions, phase equilibria, nucleation events, and defect propagation, to mention a few. A detailed comprehension of local atomic environments and of their dynamic rearrangements is essential to understand such phenomena, and also to draw structure-property relationships useful to unveil how to control complex molecular systems. Considerable progresses in the development of advanced high-dimensional structural descriptors (e.g., Smooth Overlap of Atomic Position (SOAP), etc.) have certainly enhanced the representation of atomic-scale simulations data. However, despite such efforts, local dynamic environment rearrangements remain still difficult to elucidate. Here, exploiting the structural-rich description of atomic environments of SOAP and building on the concept of time-dependent local variations, we developed a time-dependent SOAP-based descriptor, TimeSOAP (tSOAP), which essentially tracks the time variations in the local SOAP environments surrounding each molecule (i.e., each SOAP center) in complex molecular systems along ensemble trajectories. We demonstrate how analysis of the time-series {tSOAP data and of their time-derivatives allows detecting dynamics domains and tracking instantaneous changes of local atomic arrangements (i.e., local fluctuations) in a variety of molecular systems. The approach is simple and general, and we expect will help to shed light on a variety of complex dynamical phenomena. Soap bubble ; Statistical physics ; Molecular dynamics ; Variety (cybernetics) ; Complex system ; Soap film ; Dynamical systems theory ; Biological system ; Physics ; Computer science ; Quantum mechanics ; Artificial intelligence ; Mechanics Molecular spectroscopy and chirality ; Time Series Analysis and Forecasting ; Isotope Analysis in Ecology
Authors: Nhat Khang Ngo | Truong Son Hy | Risi Kondor
Source: arXiv (Cornell University) (preprint, 2023-02-17)
Field: Materials Science — Materials Chemistry

Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures Contemporary graph learning algorithms are not well-defined for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to determine the molecular properties of macromolecules. In this work, we propose Multiresolution Graph Transformers (MGT), the first graph transformer architecture that can learn to represent large molecules at multiple scales. MGT can learn to produce representations for the atoms and group them into meaningful functional groups or repeating units. We also introduce Wavelet Positional Encoding (WavePE), a new positional encoding method that can guarantee localization in both spectral and spatial domains. Our proposed model achieves competitive results on two macromolecule datasets consisting of polymers and peptides, and one drug-like molecule dataset. Importantly, our model outperforms other state-of-the-art methods and achieves chemical accuracy in estimating molecular properties (e.g., GAP, HOMO and LUMO) calculated by Density Functional Theory (DFT) in the polymers dataset. Furthermore, the visualizations, including clustering results on macromolecules and low-dimensional spaces of their representations, demonstrate the capability of our methodology in learning to represent long-range and hierarchical structures. Our PyTorch implementation is publicly available at https://github.com/HySonLab/Multires-Graph-Transformer Computer science ; Transformer ; Hierarchical clustering ; Cluster analysis ; Theoretical computer science ; Graph ; Macromolecule ; Artificial intelligence ; Graph theory ; Pattern recognition (psychology) ; Biological system ; Algorithm ; Machine learning ; Chemistry ; Mathematics ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Sushree Jagriti Sahoo | Qimen Xu | X. L. Lei | Daniel Staros | Gopal R. Iyer | Brenda M. Rubenstein | Phanish Suryanarayana | Andrew J. Medford
Source: arXiv (Cornell University) (preprint, 2023-08-10)
Field: Materials Science — Materials Chemistry

Self-consistent convolutional density functional approximations: Application to adsorption at metal surfaces The exchange-correlation (XC) functional in density functional theory is used to approximate multi-electron interactions. A plethora of different functionals is available, but nearly all are based on the hierarchy of inputs commonly referred to as "Jacob's ladder." This paper introduces an approach to construct XC functionals with inputs from convolutions of arbitrary kernels with the electron density, providing a route to move beyond Jacob's ladder. We derive the variational derivative of these functionals, showing consistency with the generalized gradient approximation (GGA), and provide equations for variational derivatives based on multipole features from convolutional kernels. A proof-of-concept functional, PBEq, which generalizes the PBE$α$ framework where $α$ is a spatially-resolved function of the monopole of the electron density, is presented and implemented. It allows a single functional to use different GGAs at different spatial points in a system, while obeying PBE constraints. Analysis of the results underlines the importance of error cancellation and the XC potential in data-driven functional design. After testing on small molecules, bulk metals, and surface catalysts, the results indicate that this approach is a promising route to simultaneously optimize multiple properties of interest. Density functional theory ; Multipole expansion ; Orbital-free density functional theory ; Consistency (knowledge bases) ; Functional derivative ; Metric (unit) ; Surface (topology) ; Mathematics ; Electron density ; Derivative (finance) ; Applied mathematics ; Hybrid functional ; Computational chemistry ; Electron ; Quantum mechanics ; Physics ; Chemistry ; Mathematical analysis ; Discrete mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Surface Chemistry and Catalysis
Authors: Y. Zhang | Chao Xu | Zhenggang Lan
Source: arXiv (Cornell University) (preprint, 2023-06-25)
Field: Materials Science — Materials Chemistry

Automated Exploration of Reaction Network and Mechanism via Meta-dynamics Nanoreactor We developed an automated approach to construct the complex reaction network and explore the reaction mechanism for several reactant molecules. The nanoreactor type molecular dynamics was employed to generate possible chemical reactions, in which the meta-dynamics was taken to overcome reaction barriers and the semi-empirical GFN2-xTB method was used to reduce computational cost. The identification of reaction events from trajectories was conducted by using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for the further transition state searches at the more accurate electronic structure levels to obtain the reaction mechanism. Then the whole reaction network with multiply pathways was obtained. The feasibility and efficiency of this automated construction of the reaction network was examined by two examples. The first reaction under study was the HCHO + NH3 biomolecular reaction. The second example focused on the reaction network for a multi-species system composed of dozens of HCN and H2O compounds. The result indicated that the proposed approach was a valuable and effective tool for the automated exploration of reaction networks. Nanoreactor ; Computer science ; Mechanism (biology) ; Reaction mechanism ; Reaction dynamics ; Chemical reaction ; Biological system ; Biochemical engineering ; Chemistry ; Molecule ; Nanotechnology ; Materials science ; Engineering ; Physics Machine Learning in Materials Science ; Chemical Synthesis and Analysis
Authors: Sandeep Ravindran
Source: Proceedings of the National Academy of Sciences (article, 2023-02-27)
Field: Materials Science — Polymers and Plastics

Profile of Yi Cui Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans the biological, physical, and social sciences. Ecology ; Biosphere ; Glacier ; Microbial population biology ; Phylogenetic tree ; Community ; Community structure ; Biology ; Geography ; Ecosystem ; Physical geography ; Bacteria ; Genetics ; Gene Conducting polymers and applications ; Quantum Dots Synthesis And Properties ; Advanced Thermoelectric Materials and Devices
Authors: Jan Kloppenburg | Lívia B. Pártay | Hannes Jónsson | Miguel A. Caro
Source: arXiv (Cornell University) (preprint, 2023-01-27)
Field: Materials Science — Materials Chemistry

A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces and nanoparticles A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on DFT data computed for bulk, surfaces and nanostructured platinum, in particular nanoparticles. Across the range of tested properties, which include bulk elasticity, surface energetics and nanoparticle stability, this potential shows excellent transferability and agreement with DFT, providing state-of-the-art accuracy at low computational cost. We showcase the possibilities for modeling of Pt systems enabled by this potential with two examples: the pressure-temperature phase diagram of Pt calculated using nested sampling and a study of the spontaneous crystallization of a large Pt nanoparticle based on classical dynamics simulations over several nanoseconds. Nanoparticle ; Interatomic potential ; Transferability ; Phase diagram ; Gaussian ; Platinum ; Materials science ; Platinum nanoparticles ; Chemical physics ; Statistical physics ; Nanotechnology ; Computer science ; Molecular dynamics ; Phase (matter) ; Machine learning ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Zhengxiao Guo
Source: Highlights in Science Engineering and Technology (article, 2023-07-12)
Field: Chemistry — Inorganic Chemistry

Advanced Metal–Organic Frameworks for Gas Separation In the chemical process, the effective separation of small gaseous molecules with similar physical and chemical properties is of great significance. In addition, clean energy sources such as hydrogen and methane are regarded as an ideal alternative to conventional fossil fuels. However, at present, the storage technology of this kind of natural gas is not perfect. The entry of harmful substances produced by human activities into the atmosphere has become a global danger that is becoming more and more worrying. It has become an important topic for scholars and industries Development of a multi-stage channel structure with high efficiency and low energy consumption for effective separation, storage or capture of toxic and harmful substances. In order to solve this problem, we intend to study the adsorption and adsorption of MOFs in the environment aboutH2/N2, H2/CO and H2/CH4 and some other gases. Compared with molecular sieve, activated carbon and other conventional inorganic porous materials, MOFs have a broad application prospect in gas adsorption and storage with unique properties. At present, the research of metal-organic frame materials with high stability has made great progress. Through the implementation of this project, it is expected to promote its wide application in air pollution control and other aspects. Fossil fuel ; Adsorption ; Environmental science ; Gas separation ; Nanotechnology ; Environmental pollution ; Waste management ; Natural gas ; Process engineering ; Materials science ; Chemistry ; Environmental protection ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Zhi-Hang Lee | Po Chuan Lin | Tzuhsiung Yang
Source: ChemRxiv (preprint, 2023-02-20)
Field: Materials Science — Materials Chemistry

A Data-driven Quantification of Ligand Field Strength for the Design of Inorganic Complexes Transition metal (TM) complexes exhibit diverse structural and electronic properties. The properties of a TM complex can be tuned through modulating the ligand field strength (LFS) inflicted by its ligands. Current quantification of the LFS of a ligand is mainly derived from experimental measurements on a subset of highly symmetrical TM complexes and is limited in ligand scope. Herein, we report a data-driven method to quantify the LFS of ligands assigned from experimental crystal structures of TM complexes. We first show that the experimental metal-ligand bond lengths of over 4000 mononuclear Fe, Co, and Mn complexes form bimodal distributions. Using gaussian fits on the bimodal distributions, each TM complex is assigned with a spin state label. These spin state labels can then be used to calculate the LFS of the ligands of the complexes. Using the obtained data-driven LFS metric, we establish that a semi-supervised deep generative model, junction tree variational autoencoder (JTVAE), can be employed to predict LFS values. Our model exhibits a mean absolute error (MAE) of 0.047 and root mean squared error of 0.072 on the training set. The model also allows the generation of novel ligands with desirable LFS values. Ligand (biochemistry) ; Gaussian ; Chemistry ; Metric (unit) ; Root mean square ; Materials science ; Biological system ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Metal complexes synthesis and properties ; Electron and X-Ray Spectroscopy Techniques
Authors: Lucas M. Sassi | Sathvik Ajay Iyengar | Anand B. Puthirath | Yuefei Huang | Xingfu Li | Tanguy Terlier | Ali Mojibpour | Ana Paula de Carvalho Teixeira | Palash Bharadwaj | Chandra Sekhar Tiwary | Róbert Vajtai | Saikat Talapatra | Boris I. Yakobson | Pulickel M. Ajayan
Source: arXiv (Cornell University) (preprint, 2023-05-23)
Field: Materials Science — Materials Chemistry

Bottom-up Integration of TMDCs with Pre-Patterned Device Architectures via Transfer-free Chemical Vapor Deposition Two-dimensional (2D) transition metal dichalcogenides (TMDCs) remain a topic of immense interest. Specifically, given their low operational switching costs, they find many niche applications in new computing architectures with the promise of continued miniaturization. However, challenges lie in Back End of Line (BEOL) integration temperature and time compliance regarding current requirements for crystal growth. Additionally, deleterious and time-consuming transfer processes and multiple steps involved in channel/contact engineering can cripple device performance. This work demonstrates kinetics-governed in-situ growth regimes (surface or edge growth from gold) of WSe2 and provides a mechanistic understanding of these regimes via energetics across various material interfaces. As a proof-of-concept, field effect transistors (FET) with an in-situ grown WSe2 channel across Au contacts are fabricated, demonstrating a 2D semiconductor transistor via a transfer-free method within the 450-600 C 2h-time window requirement BEOL integration. We leverage directional edge growth to fabricate contacts with robust thickness-dependent Schottky-to-Ohmic behavior. By transitioning between Au and SiO2 growth substrates in situ, this work achieves strain-induced subthreshold swing of 140 mV/decade, relatively high mobility of 107 +- 19 cm2V-1s-1, and robust ON/OFF ratios 10^6 in the fabricated FETs. Materials science ; Nanotechnology ; Chemical vapor deposition ; Optoelectronics ; Ohmic contact ; Field-effect transistor ; Transistor ; Semiconductor ; Miniaturization ; Engineering physics ; Electrical engineering ; Voltage ; Layer (electronics) ; Engineering 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials ; MXene and MAX Phase Materials
Authors: Jianxiao Gong | Zhiyong Tang
Source: Chem (article, 2023-06-08)
Field: Materials Science — Materials Chemistry

A clever trilayer assembly strategy for 2D materials Materials science ; Piezoresistive effect ; Decoupling (probability) ; Nanotechnology ; Anisotropy ; Fabrication ; Mode coupling ; Randomness ; Coupling (piping) ; Substrate (aquarium) ; Optoelectronics ; Physics ; Composite material ; Engineering ; Optics Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials ; 2D Materials and Applications
Authors: Medard Edmund Mswahili | Kyuri Jo | Seung-Dong Lee | Young-Seob Jeong
Source: Research Square (preprint, 2023-06-07)
Field: Chemistry — Physical and Theoretical Chemistry

Multi-type Features Graph Neural Networks for Cocrystals Prediction using API-Coformers Interactions Abstract Active pharmaceutical ingredients (APIs) have gained direct pharmaceutical interest, along with their in vitro properties, and thus utilized as auxiliary solid dosage forms upon FDA guidance and approval on pharmaceutical cocrystals as a potential and attractive route for drug substance development. In this study, we implemented graph neural networks to predict the formationof cocrystals using our first created API-co-formers interactions graph dataset. We further compared our work with previous studies that implemented descriptor-based models (e.g., random forest, support vector machine, extreme gradient boosting,and artificial neural networks). All built graph-based models show compelling performance accuracies (i.e., 91.36, 94.60, and 95.95% for GCN, GraphSAGE, and R-GCN respectively). Furthermore, R-GCN prevailed among the built graph-based models due to its capability to learn the topological structure of the graph from the additionally provided information (i.e., non-ionic andnon-covalent interactions or link information) between APIs and coformers. Graph ; Artificial neural network ; Computer science ; Random forest ; Boosting (machine learning) ; Support vector machine ; Artificial intelligence ; Machine learning ; Data mining ; Theoretical computer science Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Computational Drug Discovery Methods
Authors: Vishnu Saraswat A. Dahshan | H.I. Elsaeedy | N. Mehta
Source: arXiv (Cornell University) (preprint, 2023-09-19)
Field: Materials Science — Materials Chemistry

Impact of micro-indentation load/time and Zinc concentration on the thermo-mechanical characteristics of amorphous Se$_{78}$Te$_{20}$Sn$_2$ alloy We have performed hardness measurement experiments under different loads and loading times by performing micro-indentation marks in the present work. Chalcogenide glasses (ChGs) comprising Se$_{78}$Te$_{20}$Sn$_2$ and Se$_{78-x}$Te$_{20}$Sn$_2$Zn$_x$ (where $x = 0, 2, 4, 6$) alloys are the subject of micro-indentation tests in this work. We have utilized both micro-indentation and optical microscopic methods to determine Vickers hardness. Thermal glass transition phenomena have been identified through DSC techniques. The modulus of elasticity (E), an essential mechanical property, has been evaluated using established empirical equations. Further, we have studied other mechanical parameters [e.g., minimal micro-void formation energy (Eh), glass's fragility index (m), micro-void volume (Vh), etc.] and the covalent character of the glassy system. Additionally, various physical parameters, including density, molar volume, and compactness, have also been determined. Materials science ; Indentation ; Nanoindentation ; Vickers hardness test ; Composite material ; Amorphous solid ; Chalcogenide ; Amorphous metal ; Alloy ; Modulus ; Young's modulus ; Thermal diffusivity ; Void (composites) ; Indentation hardness ; Glass transition ; Microstructure ; Thermodynamics ; Metallurgy ; Crystallography ; Polymer ; Chemistry Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Metallic Glasses and Amorphous Alloys