jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gopal R. Iyer | Noah Whelpley | Juha Tiihonen | Paul R. C. Kent | Jaron T. Krogel | Brenda M. Rubenstein
Source: arXiv (Cornell University) (preprint, 2024-02-20)
Field: Materials Science — Materials Chemistry

Force-free identification of minimum-energy pathways and transition states for stochastic electronic structure theories Stochastic electronic structure theories, e.g., Quantum Monte Carlo methods, enable highly accurate total energy calculations which in principle can be used to construct highly accurate potential energy surfaces. However, their stochastic nature poses a challenge to the computation and use of forces and Hessians, which are typically required in algorithms for minimum-energy pathway (MEP) and transition state (TS) identification, such as the nudged-elastic band (NEB) algorithm and its climbing image formulation. Here, we present strategies that utilize the surrogate Hessian line-search method - previously developed for QMC structural optimization - to efficiently identify MEP and TS structures without requiring force calculations at the level of the stochastic electronic structure theory. By modifying the surrogate Hessian algorithm to operate in path-orthogonal subspaces and on saddle points, we show that it is possible to identify MEPs and TSs using a force-free QMC approach. We demonstrate these strategies via two examples, the inversion of the ammonia molecule and an SN2 reaction. We validate our results using Density Functional Theory- and coupled cluster-based NEB calculations. We then introduce a hybrid DFT-QMC approach to compute thermodynamic and kinetic quantities - free energy differences, rate constants, and equilibrium constants - that incorporates stochastically-optimized structures and their energies, and show that this scheme improves upon DFT accuracy. Our methods generalize straightforwardly to other systems and other high-accuracy theories that similarly face challenges computing energy gradients, paving the way for highly accurate PES mapping, transition state determination, and thermodynamic and kinetic calculations, at significantly reduced computational expense. Identification (biology) ; Statistical physics ; Energy (signal processing) ; Transition (genetics) ; Physics ; Condensed matter physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science
Authors: Khiem Le | Zhichun Guo | Kaiwen Dong | Xiaobao Huang | Bozhao Nan | Roshni G. Iyer | Xiangliang Zhang | Olaf Wiest | Wei Wang | Nitesh V. Chawla | Chawla, Nitesh V.
Source: arXiv (Cornell University) (preprint, 2024-06-10)
Field: Materials Science — Materials Chemistry

MolX: Enhancing Large Language Models for Molecular Understanding With A Multi-Modal Extension Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understanding. However, their proficiency within the chemistry domain remains restricted, especially in solving molecule-related tasks. This challenge is attributed to their inherent limitations in comprehending molecules using only common textual representations, i.e. SMILES strings. In this study, we seek to enhance the ability of LLMs to comprehend molecules by equipping them with a multi-modal external module, termed MolX. Instead of directly using SMILES strings to represent a molecule, we utilize specific encoders to extract fine-grained features from both SMILES string and 2D molecular graph representations for feeding into an LLM. A hand-crafted molecular fingerprint is incorporated to leverage its embedded domain knowledge. To establish an alignment between MolX and the LLM's textual input space, the model in which the LLM is frozen, is pre-trained with a strategy including a diverse set of tasks. Experimental evaluations show that our proposed method outperforms baselines across downstream molecule-related tasks ranging from molecule-to-text translation to molecular property prediction, with and without fine-tuning the LLM, while only introducing a small number of trainable parameters-0.53% and 0.82%, respectively. Extension (predicate logic) ; Modal ; Computer science ; Linguistics ; Natural language processing ; Artificial intelligence ; Programming language ; Philosophy ; Chemistry Machine Learning in Materials Science ; Topic Modeling
Authors: Ravindran Sujith | P. Anbumani | Sudha Radhika | Rohit Duvvuri | Asif Abdul Azeez
Source: Research Square (preprint, 2024-04-23)
Field: Chemistry — Inorganic Chemistry

New Benchmark Metal-Organic Framework for Ambient Hydrogen Storage: MOF-2087 <title>Abstract</title> The poor tradeoff between gravimetric and volumetric hydrogen storage in metal-organic frameworks (MOFs) hinders their commercial use. Despite MOF-5, IRMOF-20, and PCN-610-the benchmarked MOFs- showing good tradeoffs at 77 K, maintaining them at elevated temperatures (298 K) remains challenging. To address this, we propose a bagged-random forest tree-embedded multi-objective particle swarm optimization framework that enhances this tradeoff by optimizing MOF features. Optimization yields 152 feasible feature combinations of MOFs, further matched with 733,792 existing structures. Our nearest neighbor search pinpointed 43 local best MOFs, and the multi-criteria decision-making method highlights the Zn-based MOF 2087 as the global best with enhanced tradeoff. Optimized features of MOF-2087 demonstrate well-balanced hydrogen storage capacities surpassing benchmarked MOFs, even at elevated temperatures (77 K to 298 K). Our study presents MOF-2087 as a new benchmark for enhanced hydrogen storage up to ambient temperature and also suggests an optimization-oriented accelerated screening procedure for selecting suitable MOFs for specified applications. Benchmark (surveying) ; Metal-organic framework ; Hydrogen storage ; Hydrogen ; Metal ; Environmental science ; Computer science ; Chemistry ; Organic chemistry ; Geography ; Cartography Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Machine Learning in Materials Science
Authors: Ralf Wanzenböck | Esther Heid | Michele Riva | Giada Franceschi | Alexander M. Imre | Jesús Carrete | Ulrike Diebold | Georg K. H. Madsen
Source: ChemRxiv (preprint, 2024-06-26)
Field: Materials Science — Materials Chemistry

Machine-Learning-Backed Evolutionary Exploration of Ti-rich SrTiO3(110) Surface Reconstructions The investigation of inhomogeneous surfaces, where various local structures co-exist, is crucial for understanding interfaces of technological interest, yet it presents significant challenges. Here, we study the atomic configurations of the (2 × m) Ti-rich surfaces at (110)-oriented SrTiO3 by bringing together scanning tunneling microscopy and transferable neural-network force fields combined with evolutionary exploration. We leverage an active learning methodology to iteratively extend the training data as needed for different configurations. Training on only small well-known reconstructions, we are able to extrapolate to the complicated and diverse overlayers encountered in different regions of the heterogeneous SrTiO3(110)-(2×m) surface. Our machine-learning-backed approach generates several new candidate structures, in good agreement with experiment and verified using density functional theory. Geology ; Surface (topology) ; Paleontology ; Computer science ; Mathematics ; Geometry Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Marco Fronzi | Paolo Mele | Giovanna Latronico
Source: Inorganics (article, 2024-10-31)
Field: Materials Science — Materials Chemistry

Advances in Thermoelectric Materials—Bridging the Gap Between Discovery and Application Thermoelectric materials have gained considerable attention in recent years due to their ability to directly convert heat into electricity, making them a key focus in the development of sustainable energy technologies [...] Bridging (networking) ; Thermoelectric effect ; Thermoelectric materials ; Materials science ; Nanotechnology ; Engineering physics ; Computer science ; Engineering ; Physics ; Computer security ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Thermal properties of materials
Authors: Shrinjay Sharma | R. Baur | Marcello Rigutto | Erik Zuidema | Umang Agarwal | Sofı́a Calero | David Dubbeldam | Thijs J. H. Vlugt
Source: Entropy (article, 2024-12-21)
Field: Materials Science — Materials Chemistry

Computing Entropy for Long-Chain Alkanes Using Linear Regression: Application to Hydroisomerization Entropies for alkane isomers longer than C<sub>10</sub> are computed using our recently developed linear regression model for thermochemical properties which is based on second-order group contributions. The computed entropies show excellent agreement with experimental data and data from Scott's tables which are obtained from a statistical mechanics-based correlation. Entropy production and heat input are calculated for the hydroisomerization of C<sub>7</sub> isomers in various zeolites (FAU-, ITQ-29-, BEA-, MEL-, MFI-, MTW-, and MRE-types) at 500 K at chemical equilibrium. Small variations in these properties are observed because of the differences in reaction equilibrium distributions for these zeolites. The effect of chain length on heat input and entropy production is also studied for the hydroisomerization of C<sub>7</sub>, C<sub>8</sub>, C<sub>10</sub>, and C<sub>14</sub> isomers in MTW-type zeolite at 500 K. For longer chains, both heat input and entropy production increase. Enthalpies and absolute entropies of C<sub>7</sub> hydroisomerization reaction products in MTW-type zeolite increase with higher temperatures. These findings highlight the accuracy of our linear regression model in computing entropies for alkanes and provide insight for designing and optimizing zeolite-catalyzed hydroisomerization processes. Zeolite ; Alkane ; Thermodynamics ; Configuration entropy ; Linear regression ; Entropy (arrow of time) ; Entropy production ; Chemistry ; Long chain ; Statistical physics ; Mathematics ; Catalysis ; Physics ; Organic chemistry ; Statistics Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Computational Drug Discovery Methods
Authors: Karima Ata | Tzonka Mineva | Bruno Alonso
Source: ChemRxiv (preprint, 2024-07-23)
Field: Chemistry — Inorganic Chemistry

Strength of London dispersion forces in organic structure directing agent – zeolite assemblies International audience Zeolite ; Dispersion (optics) ; London dispersion force ; Materials science ; Chemical physics ; Chemical engineering ; Nanotechnology ; Chemistry ; Physics ; Engineering ; Optics ; Organic chemistry ; Molecule ; Catalysis Zeolite Catalysis and Synthesis
Authors: Huafeng Zhu | Liangbo Xu | Li Jia | Duanwei He | Jingchuan Wang
Source: Materials (review, 2024-11-22)
Field: Materials Science — Materials Chemistry

A Mini Review of Advances in Porous Materials Designing for Hydrogen Isotope Separation The separation of mixtures of hydrogen isotopes is one of the greatest challenges of modern separation technology. A newly proposed separation mechanism, the quantum sieving (QS) effect, is expected to achieve high separation factors, the main desired properties for hydrogen isotope separation (HIS). Metal-organic frameworks (MOFs) and zeolites are excellent candidates to study these quantum effects because of their well-defined and tunable pore structure and the potential to introduce strong adsorption sites directly into the framework structure. This paper briefly discusses the fundamentals of QS of hydrogen isotopes in nanoporous materials, mainly including kinetic quantum sieving (KQS) and chemical affinity quantum sieving (CAQS). Recent experimental advances in the separation of hydrogen isotopes from MOFs and zeolites are highlighted. Hydrogen isotope ; Porosity ; Materials science ; Hydrogen ; Separation (statistics) ; Chemical engineering ; Nanotechnology ; Composite material ; Chemistry ; Engineering ; Computer science ; Organic chemistry Hydrogen Storage and Materials ; Chemical Synthesis and Characterization ; Boron Compounds in Chemistry
Authors: Irene Piedra | Ramón Manzorro | Maria Guerra | José J. Calvino | Miguel López‐Haro | Ana B. Hungría
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Chemical and morphological stability study of copper oxide nanocubes in controlled and non-controlled atmospheres Copper ; Copper oxide ; Materials science ; Oxide ; Chemical engineering ; Chemical stability ; Nanotechnology ; Metallurgy ; Engineering Copper-based nanomaterials and applications ; Gas Sensing Nanomaterials and Sensors ; Transition Metal Oxide Nanomaterials
Authors: Junjie Hu | Peng Wu | Shiyi Wang | Binju Wang | Guang Yang
Source: Pharmaceutics (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

A Human Feedback Strategy for Photoresponsive Molecules in Drug Delivery: Utilizing GPT-2 and Time-Dependent Density Functional Theory Calculations Photoresponsive drug delivery stands as a pivotal frontier in smart drug administration, leveraging the non-invasive, stable, and finely tunable nature of light-triggered methodologies. The generative pre-trained transformer (GPT) has been employed to generate molecular structures. In our study, we harnessed GPT-2 on the QM7b dataset to refine a UV-GPT model with adapters, enabling the generation of molecules responsive to UV light excitation. Utilizing the Coulomb matrix as a molecular descriptor, we predicted the excitation wavelengths of these molecules. Furthermore, we validated the excited state properties through quantum chemical simulations. Based on the results of these calculations, we summarized some tips for chemical structures and integrated them into the alignment of large-scale language models within the reinforcement learning from human feedback (RLHF) framework. The synergy of these findings underscores the successful application of GPT technology in this critical domain. Excited state ; Reinforcement learning ; Computer science ; Molecule ; Nanotechnology ; Density functional theory ; Excitation ; Biological system ; Drug delivery ; Materials science ; Chemistry ; Physics ; Artificial intelligence ; Computational chemistry ; Biology ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Photoreceptor and optogenetics research ; Quantum Dots Synthesis And Properties
Authors: Lilliana Brandão | C. Reece
Source: ChemRxiv (preprint, 2024-01-16)
Field: Materials Science — Materials Chemistry

Non-Steady State Validation of Kinetic Models for Ethylene Epoxidation over Silver Catalysts Kinetic modelling has been key to developing a mechanistic understanding of the epoxidation of ethylene to ethylene oxide over silver catalysts. However, models of varying active site and mechanistic complexity have all been able to recreate steady state activity and selectivity, leading to an ambiguity about the exact mechanism and nature of the active site. Herein, we validate three leading kinetic models for ethylene epoxidation over metallic silver catalysts by numerically recreating non-steady state Temporal Analysis of Products experiments. We find all of the models are able to very generally recreate the trends observed in the pulse experiments, but that only a two-site model modified to mimic the presence of a subsurface oxygen reservoir is able to accurately recreate the trends observed in a state-altering experiment over oxidised silver. Specific to this model is the inclusion of a nucleophilic oxygen species adsorbed on top of the surface oxide which acts as the active site for the selective oxidation of ethylene. This work exemplifies that while simplified single-site models for ethylene epoxidation are useful tools for broad screening, more complex models are required to capture the precise activity of the catalyst. Ethylene ; Catalysis ; Ethylene oxide ; Selectivity ; Chemistry ; Steady state (chemistry) ; Active site ; Adsorption ; Nucleophile ; Kinetic energy ; Oxygen ; Materials science ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Jaume Colomina-Martínez | Juan Carlos Bravo | Joan Josep Sirvent-Verdú | Adrián Moya | Jorge Francés | Cristian Neipp | Augusto Beléndez
Source: Applied Sciences (article, 2024-11-11)
Field: Materials Science — Surfaces, Coatings and Films

Numerical Estimation of Bending in Holographic Volume Gratings by Means of RCWA and Deep Learning In this paper, we introduce a novel approach to model bending phenomena on holographic volume gratings based on Rigorous Coupled Wave Analysis (RCWA), in which the bending as a phase in the dielectric permittivity expansion is introduced, and the Shooting Method (SM) is employed to solve the resulting system of equations. Further validation of our model is conducted by comparing its predictions to those obtained from reference Finite-Difference Time-Domain (FDTD) simulations and Coupled Wave Theory (CWT, referring to Kubota’s model that includes the bending phenomenon). Furthermore, we propose a methodology for estimating the bending from the diffraction efficiency curves in transmission volume gratings based on deep learning models, with a subsequent study of their accuracy and applicability. Holography ; Finite-difference time-domain method ; Bending ; Diffraction ; Optics ; Diffraction efficiency ; Materials science ; Computer science ; Physics ; Composite material Optical Coatings and Gratings ; Optical measurement and interference techniques ; Surface Roughness and Optical Measurements
Authors: Bin Xu | Fengwen Mu | Y J Liu | Rulei Guo | Shiqian Hu | Junichiro Shiomi
Source: arXiv (Cornell University) (preprint, 2024-04-24)
Field: Materials Science — Materials Chemistry

Low thermal boundary resistance at bonded GaN/diamond interface by controlling ultrathin heterogeneous amorphous layer Thermal boundary resistance (TBR) in semiconductor-on-diamond structure bottlenecks efficient heat dissipation in electronic devices. In this study, to reduce the TBR between GaN and diamond, surface-activated bonding with a hybrid SiOx-Ar ion source was applied to achieve an ultrathin interfacial layer. The simultaneous surface activation and slow deposition of the SiOx binder layer enabled precise control over layer thickness (2.5-5.3 nm) and formation of an amorphous heterogeneous nanostructure comprising a SiOx region between two inter-diffusion regions. Crucially, the 2.5-nm-thick interfacial layer achieved a TBR of 8.3 m2-W/GW, a record low for direct-bonded GaN/diamond interface. A remarkable feature is that the TBR is extremely sensitive to the interfacial thickness; rapidly increasing to 34 m2-K/GW on doubling the thickness to 5.3 nm. Theoretical analysis revealed the origin of this increase: a diamond/SiOx interdiffusion layer extend the vibrational frequency, far-exceeding that of crystalline diamond, which increases the lattice vibrational mismatch and suppresses phonon transmission. Materials science ; Interfacial thermal resistance ; Layer (electronics) ; Diamond ; Amorphous solid ; Thermal ; Interface (matter) ; Thermal resistance ; Optoelectronics ; Composite material ; Nanotechnology ; Crystallography ; Chemistry ; Physics Thermal properties of materials ; GaN-based semiconductor devices and materials ; Metal and Thin Film Mechanics
Authors: Pavel Anatolyevich Nikolaychuk
Source: IJCA (Indonesian Journal of Chemical Analysis) (article, 2024-09-30)
Field: Chemistry — Analytical Chemistry

Spectrophotometric Determination of Norepinephrine in Industrial Equipment Cleaning Rinse Water by Its Own Absorbance and The Tollens’ Reagent A literature review of the available spectrophotometric methods of determination of catecholamines is presented. Based on this review, two simple spectrophotometric methods for the determination of norepinephrine bitartrate in pharmaceutical formulations and industrial equipment cleaning rinse waters were chosen and validated. The first method for the determination of norepinephrine at ppm levels utilizes the absorbance band of the solution of norepinephrine bitartrate at 279 nm. The second method for the determination of norepinephrine at ppb levels is based on the reaction of norepinephrine with the Tollens’ reagent in the presence of the cationic surfactant cetylpyridinium chloride and the colorimetric determination of the formed colloidal silver nanoparticles. The calibration graphs are linear in the range from 1 to 150 mg/l of norepinephrine for the first method and 25 µg/l to 10 mg/l for the second method. The molar attenuation coefficients are 388 and 7560 m2/mol, respectively, the limits of detection are 1.1 mg/l and 3.1 µg/l, and the limits of quantification are 3.3 mg/l and 9.4 µg/l, respectively. Both methods are selective concerning the common excipients, show good accuracy (the relative uncertainties do not exceed 4 and 7%, respectively) and precision (the relative standard deviations do not exceed 4 and 7%, respectively), and do not require lengthy sample preparation and sophisticated laboratory equipment. Reagent ; Absorbance ; Norepinephrine ; Chemistry ; Chromatography ; Pulp and paper industry ; Internal medicine ; Medicine ; Engineering ; Organic chemistry ; Dopamine Analytical chemistry methods development ; Analytical Chemistry and Sensors ; Analytical Methods in Pharmaceuticals
Authors: Zhusheng Liu | Hao Yu | Yanan Chen | Wei Feng
Source: Chemistry (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

A New Hydrotalcite-Like Absorbent OSA-LDH and Its Adsorption Capacity for Pb2+ Ions in Water Hydrotalcite-like materials (OSA-LDH) were prepared used oil shale ash (OSA), which came from a thermal power plant area, as the main raw material. The characterization results of X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), and thermogravimetric-differential scanning calorimetry (TG-DSC) showed that the prepared samples were mesoporous materials in a crystal state and were layered and contained lattice oxygen and a large number of surface hydroxyl groups. The adsorption property of the prepared samples was confirmed and evaluated by adsorption experiments with Pb2+ as the target pollutant. The adsorption process was in accord with the Langmuir isothermal adsorption equation, and the adsorption data fitted perfectly with the pseudo-second kinetic equation. The saturated adsorption capacity for Pb2+ was 120.92 mg·g−1 at a temperature of 298 K and initial concentration of 300 mg·L−1. The main adsorption mechanisms of OSA-LDH for Pb2+ were chemical bond cooperation and electrostatic bond cooperation. This paper aimed to not only prepare an economical and effective adsorbent to remove heavy metal ions from the solution but also provide a new path for the treatment and utilization of OSA so as to realize efficient waste resource utilization. Adsorption ; Thermogravimetric analysis ; Hydrotalcite ; Materials science ; Differential scanning calorimetry ; Scanning electron microscope ; Mesoporous material ; Chemical engineering ; Metal ions in aqueous solution ; Langmuir adsorption model ; Langmuir ; Chemistry ; Ion ; Organic chemistry ; Composite material ; Thermodynamics Layered Double Hydroxides Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Phosphorus and nutrient management