jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Phong Dang | J. P. Draayer | Feng Pan | H. P. Beck
Source: The European Physical Journal Plus (article, 2024-08-08)
Field: Chemistry — Spectroscopy

New procedure for evaluation of U(3) coupling and recoupling coefficients Abstract A simple method to calculate Wigner coupling coefficients and Racah recoupling coefficients for U(3) in two group–subgroup chains is presented. While the canonical $$\rm U(3)\supset U(2)\supset U(1)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>U</mml:mi> <mml:mo>(</mml:mo> <mml:mn>3</mml:mn> <mml:mo>)</mml:mo> <mml:mo>⊃</mml:mo> <mml:mi>U</mml:mi> <mml:mo>(</mml:mo> <mml:mn>2</mml:mn> <mml:mo>)</mml:mo> <mml:mo>⊃</mml:mo> <mml:mi>U</mml:mi> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> coupling and recoupling coefficients are applicable to any system that respects U(3) symmetry, the $$\rm U(3) \supset SO(3)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>U</mml:mi> <mml:mo>(</mml:mo> <mml:mn>3</mml:mn> <mml:mo>)</mml:mo> <mml:mo>⊃</mml:mo> <mml:mi>SO</mml:mi> <mml:mo>(</mml:mo> <mml:mn>3</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> coupling coefficients are more specific to nuclear structure studies. This new procedure precludes the use of binomial coefficients and alternating sums which were used in the 1973 formulation of Draayer and Akiyama, and in so doing provides a faster and more accurate determination of any and all required results. The resolution of the outer multiplicity is based on the null space concept of the U(3) generators proposed by Alex et al., whereas the inner multiplicity in the angular momentum subgroup chain is obtained from the dimension of the null space of the SO(3) raising operator. It is anticipated that a C++ library will ultimately be available for determining generic coupling and recoupling coefficients associated with both the canonical and the physical group–subgroup chains of U(3). Algorithm ; Materials science ; Computer science Advanced NMR Techniques and Applications ; Lanthanide and Transition Metal Complexes ; Quantum Chromodynamics and Particle Interactions
Authors: Jing Li | Lu Zhou | Liuchang Wang | Huiqing Shi | Bixin Chu | Yingwei Qu | Zichen Ye | Di Qu
Source: Journal of Neuroimmune Pharmacology (article, 2024-06-18)
Field: Chemistry — Organic Chemistry

Novel Coumarins Derivatives for A. baumannii Lung Infection Developed by High-Throughput Screening and Reinforcement Learning With the increasing resistance of Acinetobacter baumannii (A. baumannii) to antibiotics, researchers have turned their attention to the development of new antimicrobial agents. Among them, coumarin-based heterocycles have attracted much attention due to their unique biological activities, especially in the field of antibacterial infection. In this study, a series of coumarin derivatives were synthesized and screened for their bactericidal activities (Ren et al. 2018; Salehian et al. 2021). The inhibitory activities of these compounds on bacterial strains were evaluated, and the related mechanism of the new compounds was explored. Firstly, the MIC values and bacterial growth curves were measured after compound treatment to evaluate the antibacterial activity in vitro. Then, the in vivo antibacterial activities of the new compounds were assessed on A. baumannii-infected mice by determining the mice survival rates, counting bacterial CFU numbers, measuring inflammatory cytokine levels, and histopathology analysis. In addition, the ROS levels in the bacterial cells were measured with DCFH-DA detection kit. Furthermore, the potential target and detailed mechanism of the new compounds during infection disease therapy were predicted and evidenced with molecular docking. After that, ADMET characteristic prediction was completed, and novel, synthesizable, drug-effective molecules were optimized with reinforcement learning study based on the probed compound as a training template. The interaction between the selected structures and target proteins was further evidenced with molecular docking. This series of innovative studies provides important theoretical and experimental data for the development of new anti-A. baumannii infection drugs. Acinetobacter baumannii ; Antimicrobial ; In vivo ; Antibacterial activity ; Antibiotics ; Coumarin ; Docking (animal) ; Combinatorial chemistry ; Chemistry ; Bacteria ; Pharmacology ; Microbiology ; Biology ; Computational biology ; Medicine ; Pseudomonas aeruginosa ; Biotechnology ; Organic chemistry Synthesis and biological activity ; Computational Drug Discovery Methods ; Synthesis and Biological Evaluation
Authors: Md. Shamim Hussain | Mohammed J. Zaki | Dharmashankar Subramanian
Source: arXiv (Cornell University) (preprint, 2024-02-07)
Field: Materials Science — Materials Chemistry

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers Graph transformers typically lack third-order interactions, limiting their geometric understanding which is crucial for tasks like molecular geometry prediction. We propose the Triplet Graph Transformer (TGT) that enables direct communication between pairs within a 3-tuple of nodes via novel triplet attention and aggregation mechanisms. TGT is applied to molecular property prediction by first predicting interatomic distances from 2D graphs and then using these distances for downstream tasks. A novel three-stage training procedure and stochastic inference further improve training efficiency and model performance. Our model achieves new state-of-the-art (SOTA) results on open challenge benchmarks PCQM4Mv2 and OC20 IS2RE. We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning. We also demonstrate the generality of TGT with SOTA results on the traveling salesman problem (TSP). Transformer ; Graph ; Computer science ; Theoretical computer science ; Engineering ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: В. А. Мошников | Е. Н. Муратова | А. Н. Алешин | Alexandr Maksimov | Gregory Nenashev | Igor Vrublevsky | Н. В. Лушпа | Alexandr Tuchkovsky | Anton Zhilenkov | Olga Kichigina
Source: Preprints.org (preprint, 2024-03-17)
Field: Chemistry — Inorganic Chemistry

Controlled Crystallization of Hybrid Perovskite Films from Solution Using Prepared Crystal Centers : The crystallization conditions from the solution play an important role in determining the mor-phology, phase composition, and photovoltaic properties of perovskite films. Post-processing of the obtained films can have a crucial role in increasing the grain size of perovskite and enhancing its crystallinity. It has been shown that the formation of crystal nuclei can be utilized to accelerate crystallization. In this case, crystallization occurs through the growth of seed crystals created in the solution, enabling the formation of relatively large crystals. For the deposition of CH3NH3PbI3 hybrid halide perovskite films from a solution of the perovskite in dimethylformamide the spin coating technique was employed. Pre-crystallization was achieved by annealing the films at a temperature of 100 or 110 °C. The dissolution process involved adding a drop of dimethylformamide onto the substrate surface and allowing it to partially dissolve the perovskite film. Subsequently, residual solvent was removed through spin coating. The mor-phological analysis of the perovskite film surface after recrystallization at temperatures ranging from 80 to 130 °C was performed. The infrared transmission spectra of the obtained perovskite films were investigated, and their light absorption characteristics were studied through transmission spectra. The perovskite structure in the obtained films was confirmed by the peaks observed in the X-ray diffraction patterns. Crystallization ; Perovskite (structure) ; Materials science ; Chemical engineering ; Crystal (programming language) ; Computer science ; Engineering Pigment Synthesis and Properties ; Gas Sensing Nanomaterials and Sensors ; Catalytic Processes in Materials Science
Authors: Zonglin Yang | Wei Liu | Ben Gao | Xie Tong | Yuqiang Li | Wanli Ouyang | Soujanya Poria | Erik Cambria | Dongzhan Zhou
Source: arXiv (Cornell University) (preprint, 2024-10-09)
Field: Materials Science — Materials Chemistry

MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses Scientific discovery plays a pivotal role in advancing human society, and recent progress in large language models (LLMs) suggests their potential to accelerate this process. However, it remains unclear whether LLMs can autonomously generate novel and valid hypotheses in chemistry. In this work, we investigate whether LLMs can discover high-quality chemistry hypotheses given only a research background-comprising a question and/or a survey-without restriction on the domain of the question. We begin with the observation that hypothesis discovery is a seemingly intractable task. To address this, we propose a formal mathematical decomposition grounded in a fundamental assumption: that most chemistry hypotheses can be composed from a research background and a set of inspirations. This decomposition leads to three practical subtasks-retrieving inspirations, composing hypotheses with inspirations, and ranking hypotheses - which together constitute a sufficient set of subtasks for the overall scientific discovery task. We further develop an agentic LLM framework, MOOSE-Chem, that is a direct implementation of this mathematical decomposition. To evaluate this framework, we construct a benchmark of 51 high-impact chemistry papers published and online after January 2024, each manually annotated by PhD chemists with background, inspirations, and hypothesis. The framework is able to rediscover many hypotheses with high similarity to the groundtruth, successfully capturing the core innovations-while ensuring no data contamination since it uses an LLM with knowledge cutoff date prior to 2024. Finally, based on LLM's surprisingly high accuracy on inspiration retrieval, a task with inherently out-of-distribution nature, we propose a bold assumption: that LLMs may already encode latent scientific knowledge associations not yet recognized by humans. Chemistry ; Epistemology ; Computer science ; Philosophy Machine Learning in Materials Science ; Advanced Text Analysis Techniques
Authors: Yu Liu | Raghavan, Aditya | Pratiush, Utkarsh | Maxim Ziatdinov | Lee, Chih-Yu | Rohit Pant | Ichiro Takeuchi | Hsieh, Pochun | Suceava, Albert | Dimitrov, Edgar | Terrones, Mauricio | Gopalan, Venkatraman | Mercer, Ian | R. Jackson Spurling | Jon‐Paul Maria | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2024-12-24)
Field: Materials Science — Materials Chemistry

Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries Combinatorial materials libraries provide a powerful platform for mapping how physical properties evolve across binary and ternary cross-sections of multicomponent phase diagrams. While synthesis of such libraries has advanced since the 1960s and been accelerated by laboratory automation, their broader utility depends on rapid, quantitative measurements of composition-dependent structures and functionalities. Scanning probe microscopies (SPM), including piezoresponse force microscopy (PFM), offer unique potential for providing these functionally relevant, spatially resolved readouts. Here, we demonstrate a fully automated SPM framework for exploring ferroelectric properties across combinatorial libraries, focusing on binary Sm-doped BiFeO3 (SmBFO) and ternary Al$_{1-x-y}$Sc$_x$B$_y$N (Al,Sc,B)N systems. In SmBFO, automated exploration identifies the known morphotropic phase boundary with enhanced ferroelectric response and reveals a previously unreported double-peak fine structure. In the (Al,Sc,B)N library, ferroelectric behavior emerges at the phase-stability boundary, correlating with variations in morphology and defect concentration. By integrating automated SPM with wavelength-dispersive spectroscopy (WDS) and photoluminescence mapping, we resolve the composition-morphology-defect-property relationships underlying ferroelectric response and demonstrate a pathway toward a multi-tool, high-throughput characterization platform. Finally, we implement Gaussian-process-based single- and multi-objective Bayesian optimization to enable autonomous exploration, highlighting the Pareto front as a powerful framework for balancing competing physical rewards and accelerating data-driven physics discovery. Nanotechnology ; Microscopy ; Scanning probe microscopy ; Computer science ; Materials science ; Physics ; Optics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Diatoms and Algae Research
Authors: Janghoon Ock | Badrinarayanan, Srivathsan | Rishikesh Magar | Akshay Antony | Amir Barati Farimani
Source: arXiv (Cornell University) (preprint, 2024-01-15)
Field: Materials Science — Materials Chemistry

Multimodal Language and Graph Learning of Adsorption Configuration in Catalysis Adsorption energy is a reactivity descriptor that must be accurately predicted for effective machine learning (ML) application in catalyst screening. This process involves determining the lowest energy across various adsorption configurations on a catalytic surface, which can exhibit very similar energy values. While graph neural networks (GNNs) have shown great success in computing the energy of catalyst systems, they rely heavily on atomic spatial coordinates. In contrast, transformer-based language models can directly use human-readable text inputs, potentially bypassing the need for detailed atomic positions. However, these language models often struggle with accurately predicting the energy of adsorption configurations. Our study addresses this limitation by introducing a self-supervised multi-modal learning approach called graph-assisted pretraining, which connects well-established GNNs with emerging language model applications. This method reduces the MAE of energy prediction for adsorption configurations by about 10%. Furthermore, our findings demonstrate that graph-assisted pretraining enhances fine-tuning with different datasets, indicating the transferability of this approach. This method also redirects the model's attention toward adsorption configuration, rather than individual adsorbate and catalyst information, similar to common domain knowledge. Building on this, we propose using generative large language models to create text inputs for the predictive model, based solely on chemical composition and surface orientation, without relying on exact atomic positions. This demonstrates a potential use case of language models in energy prediction without geometric information. Computer science ; Interpretability ; CLARITY ; Machine learning ; Artificial intelligence ; Graph ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Electrocatalysts for Energy Conversion
Authors: Mantas Vaškevičius | Jurgita Kapočiūtė-Dzikienė
Source: Applied Sciences (article, 2024-12-11)
Field: Materials Science — Materials Chemistry

Language Models for Predicting Organic Synthesis Procedures In optimizing organic chemical synthesis, researchers often face challenges in efficiently generating viable synthesis procedures that conserve time and resources in laboratory settings. This paper systematically analyzes multiple approaches to efficiently generate synthesis procedures for a wide variety of organic synthesis reactions, aiming to decrease time and resource consumption in laboratory work. We investigated the suitability of different sizes of BART, T5, FLAN-T5, molT5, and classic sequence-to-sequence transformer models for our text-to-text task and utilized a large dataset prepared specifically for the task. Experimental investigations demonstrated that a fine-tuned molT5-large model achieves a BLEU score of 47.75. The results demonstrate the capability of LLMs to predict chemical synthesis procedures involving 24 possible distinct actions, many of which include various parameters like solvents, reaction agents, temperature, duration, solvent ratios, and other specific parameters. Our findings show that only when the core reactants are used as input, the models learn to correctly predict what ancillary components need to be included in the resulting procedure. These results are valuable for AI researchers and chemists, suggesting that curated datasets and large language model fine-tuning techniques can be tailored for specific reaction classes and practical applications. This research contributes to the field by demonstrating how deep-learning-based methods can be customized to meet the specific requirements of chemical synthesis, leading to more intelligent and resource-efficient laboratory processes. Computer science Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Maicon Pierre Lourenço | Hadi Zadeh‐Haghighi | Jiří Hostaš | Mosayeb Naseri | Daya Ram Gaur | Christoph Simon | Dennis R. Salahub
Source: arXiv (Cornell University) (preprint, 2024-07-26)
Field: Materials Science — Materials Chemistry

Exploring Quantum Active Learning for Materials Design and Discovery The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our previous studies of materials discovery using classical active learning (AL), which showed remarkable economy of data, to explore the use of quantum algorithms within the AL framework (QAL) as implemented in the MLChem4D and QMLMaterials codes. The proposed QAL uses quantum support vector regressor (QSVR) or a quantum Gaussian process regressor (QGPR) with various quantum kernels and different feature maps. Data sets include perovskite properties (piezoelectric coefficient, band gap, energy storage) and the structure optimization of a doped nanoparticle (3Al@Si11) chosen to compare with classical AL results. Our results revealed that the QAL method improved the searches in most cases, but not all, seemingly correlated with the roughness of the data. QAL has the potential of finding optimum solutions, within chemical space, in materials science and elsewhere in chemistry. Quantum ; Computer science ; Nanotechnology ; Data science ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Nabil Amer | Gehan Nagy
Source: IOP Conference Series Earth and Environmental Science (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Smart Materials for Modern Facades: An AI-Powered Selection Process Abstract According to the mid-21st century’s technological advancements, artificial intelligence has gained popularity as a transformative tool due to its immense integration in the evolving field of architecture. Evidently, it is being used in the building skin design to enhance the energy efficiency and to reduce the continuously rising energy waste and consumption problem in office buildings. Despite AI’s immense advancements, it lacks sufficient data with regards to the turf of smart skin design in office building for energy optimisation. Hence, this research aims to develop a framework for the optimal integration of artificial intelligence in the selection process of smart materials for energy efficient design for office buildings’ skin. To reach this aim, a mixed-method approach using quantitative and qualitative analysis will be implemented. First, theoretical research utilising archives and case study analysis is conducted to determine the various applications of AI in architecture, the components of a smart office building skin focusing on smart materials, the definition of energy efficiency and its measurement tools and the miscellaneous AI tools that are inaugurated to design an office building skin. As a result, a framework will be created by integrating the optimal AI tools in the selection process of smart materials for energy efficient smart skin design. Second, the applied studies are performed using empirical and experimental analysis to test and support the framework through case study analysis and comparison to test the framework obtained from the literature review aiming to cover the gap identified in the selection process of the materials. Architectural engineering ; Selection (genetic algorithm) ; Process (computing) ; Computer science ; Engineering ; Construction engineering ; Artificial intelligence ; Programming language Material Selection and Properties ; BIM and Construction Integration ; Additive Manufacturing and 3D Printing Technologies
Authors: Ryan Cohn | Elizabeth A. Holm
Source: Scientific Reports (article, 2024-12-04)
Field: Materials Science — Materials Chemistry

Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution Recent developments in graph neural networks show promise for predicting the occurrence of abnormal grain growth, which has been a particularly challenging area of research due to its apparent stochastic nature. In this study, we generate a large dataset of Monte Carlo simulations of abnormal grain growth. We train simple graph convolution networks to predict which initial microstructures will exhibit abnormal grain growth, and compare the results to a standard computer vision approach for the same task. The graph neural network outperformed the computer vision method and achieved 73% prediction accuracy and fewer false positives. It also provided some physical insight into feature importance and the relevant length scale required to maximize predictive performance. Analysis of the uncertainty in the Monte Carlo simulations provides additional insights for ongoing work in this area. Computer science ; Monte Carlo method ; False positive paradox ; Convolutional neural network ; Graph ; Artificial intelligence ; Feature (linguistics) ; Machine learning ; Pattern recognition (psychology) ; Algorithm ; Theoretical computer science ; Statistics ; Mathematics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Advanced Neural Network Applications