jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuan Lin | Long Chen | Yile Wang | Xiangxiang Zeng | Philip S. Yu
Source: arXiv (Cornell University) (preprint, 2024-12-24)
Field: Materials Science — Materials Chemistry

Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models Large language models (LLMs) are widely applied in various natural language processing tasks such as question answering and machine translation. However, due to the lack of labeled data and the difficulty of manual annotation for biochemical properties, the performance for molecule generation tasks is still limited, especially for tasks involving multi-properties constraints. In this work, we present a two-step framework PEIT (Property Enhanced Instruction Tuning) to improve LLMs for molecular-related tasks. In the first step, we use textual descriptions, SMILES, and biochemical properties as multimodal inputs to pre-train a model called PEIT-GEN, by aligning multi-modal representations to synthesize instruction data. In the second step, we fine-tune existing open-source LLMs with the synthesized data, the resulting PEIT-LLM can handle molecule captioning, text-based molecule generation, molecular property prediction, and our newly proposed multi-constraint molecule generation tasks. Experimental results show that our pre-trained PEIT-GEN outperforms MolT5 and BioT5 in molecule captioning, demonstrating modalities align well between textual descriptions, structures, and biochemical properties. Furthermore, PEIT-LLM shows promising improvements in multi-task molecule generation, proving the scalability of the PEIT framework for various molecular tasks. We release the code, constructed instruction data, and model checkpoints in https://github.com/chenlong164/PEIT. Task (project management) ; Computer science ; Property (philosophy) ; Engineering ; Systems engineering Machine Learning in Materials Science
Authors: Yong‐Liang Su | Siyuan Yang | Ming Liu | Aiting Kai | Linjiang Chen
Source: Digital Discovery (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Knowledge discovery from porous organic cage literature using a large language model A GPT-4-based text mining workflow was developed for rapid information extraction from porous organic cages (POCs) literature. A chatbot was also created to answer POCs-related questions using the knowledge database generated from the workflow. Computer science ; Natural language processing ; Data science Machine Learning in Materials Science
Authors: Jongsun Jung | Taeseob Lee | Jonathan Witztum | Hwapyung Jung | Ahnjae Jung | Jae-Woong Min | Jong Wook Hong | Bin Zhang | Junsik An | Woon-Young Hong
Source: Research Square (preprint, 2024-01-12)
Field: Materials Science — Materials Chemistry

3bmGPT: A novel language model for transforming and analyzing protein-ligand binding interactions in drug discovery. Computer science ; Context (archaeology) ; Drug discovery ; Language model ; Computational biology ; Artificial intelligence ; Bioinformatics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Fanjie Xu | Wentao Guo | Feng Wang | Yao Lin | Hongshuai Wang | Fujie Tang | Zhifeng Gao | Linfeng Zhang | E Weinan | Zhong‐Qun Tian | Jun Cheng
Source: arXiv (Cornell University) (preprint, 2024-08-28)
Field: Chemistry — Spectroscopy

Towards a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts The study of structure-spectrum relationships is essential for spectral interpretation, impacting structural elucidation and material design. Predicting spectra from molecular structures is challenging due to their complex relationships. Herein, we introduce NMRNet, a deep learning framework using the SE(3) Transformer for atomic environment modeling, following a pre-training and fine-tuning paradigm. To support the evaluation of NMR chemical shift prediction models, we have established a comprehensive benchmark based on previous research and databases, covering diverse chemical systems. Applying NMRNet to these benchmark datasets, we achieve state-of-the-art performance in both liquid-state and solid-state NMR datasets, demonstrating its robustness and practical utility in real-world scenarios. This marks the first integration of solid and liquid state NMR within a unified model architecture, highlighting the need for domainspecific handling of different atomic environments. Our work sets a new standard for NMR prediction, advancing deep learning applications in analytical and structural chemistry. Benchmark (surveying) ; Deep learning ; Chemical shift ; Nuclear magnetic resonance ; Magnetic resonance imaging ; Computer science ; Artificial intelligence ; Physics ; Medicine ; Geography ; Cartography Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Various Chemistry Research Topics
Authors: Arda Genç | Justin Marlowe | Aishah Abdul Jalil | Libor Kovarik | Phillip Christopher
Source: arXiv (Cornell University) (preprint, 2024-10-02)
Field: Materials Science — Materials Chemistry

A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationships and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology was applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalysts. The model's performance in detecting and segmenting NPs was validated across diverse heterogeneous catalyst systems, including various metals (Cu, Ru, Pt, and PtCo), supports (silica ($\text{SiO}_2$), $γ$-alumina ($γ$-$\text{Al}_2\text{O}_3$), and carbon black), and particle diameter size distributions with means and standard deviations of 2.9 $\pm$ 1.1 nm, 1.6 $\pm$ 0.2 nm, 9.7 $\pm$ 4.6 nm, and 4 $\pm$ 1.0 nm. Additionally, the proposed machine learning (ML) approach successfully detects and segments overlapping NPs anchored on non-uniform catalytic support materials, providing critical insights into their spatial arrangements and interactions. Our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining. Workflow ; Throughput ; Catalysis ; Computer science ; Chemistry ; Operating system ; Database Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Zahid Nawaz | Khalid M. Elhindi | Muhammad Imran Arshad | S. Mumtaz | Nasir Amin
Source: Journal of Ovonic Research (article, 2024-12-15)
Field: Materials Science — Materials Chemistry

Study of structural and electrical properties of Cd+2 doped Mn-Zn ferrites synthesized by co-precipitation method The main purpose of this work was to study the electrical, structural and optical properties of Cd-doped Zn based soft ferrites. A series of nano-particles with composition Zn0.5Mn0.5- xCdxFe2O4 (x= 0.00, 0.01, 0.03, 0.05, 0.07, 0.09) prepared by the Co-precipitation route. After preparing, the samples sintered at temperature 900⁰C for 6 hours. Different characterization techniques like XRD (X-Ray-Diffraction), FTIR (Fourier-TransformInfrared-spectroscopy), UV-vis. and IV-characteristics were used to explore the effect of doping element (Cd) on the electrical, structural and optical properties of Nano-particles. XRD data confirmed the single phase of material with second phase of Fe2O3 and average crystalline size in range from 38.09-45.15 nm. The value of average lattice constant for the prepared material found in range from 8.4471 Å to 8.4763 Å. In FTIR data one prominent band is found in all sample and in some sample second band was found in range from 400- 4000cm-1. IV-observation revealed the dependence of DC-resistivity on temperature and the value for activation energy (∆𝐸𝐸) found in range from 0.1365 to 0.4332 eV/1000K. The UV-vis. analysis confirm the absorption peak for all samples at average wavelength 286 nm. At this wavelength absorption for all samples was in range from 2.8722-3.2956 (a.u). Cd concentration is responsible for the decrease of saturation magnetizations and losses. Due to suitable properties, these materials are useful in different fields like Recording-Media, high-frequency applications and many branches of electronic engineering etc. Precipitation ; Doping ; Coprecipitation ; Materials science ; Manganese ; Metallurgy ; Zinc ; Cadmium ; Chemical engineering ; Chemistry ; Inorganic chemistry ; Optoelectronics ; Physics Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Magneto-Optical Properties and Applications
Authors: Abhinav Babu | Aniket Bhardwaj | Saurabh Verma
Source: International Journal of Research in Pharmaceutical Sciences (article, 2024-08-31)
Field: Materials Science — Biomaterials

Magnetic nanoparticles (MNPs): Design, characterization, release mechanism and remote-controlled application for targeted therapeutics MNPs as novel drug delivery system (NDDS) approach possess several magnetic properties for targeted and controlled delivery in various biomedical application. Unlike normal nanoparticles, MNPs enable to respond to external magnetic fields, allowing for manipulation, guidance, and functionalisation within the body. This review encompasses a wide range of scientific and technological goals aimed in understanding their properties, synthesis methods, characterization techniques, surface functionalisation strategies and application. Drug delivery based on magnetic properties has advanced dramatically to enhance therapeutic bioavailability, stability, and targeted delivwery. Converting nanoparticles into MNPs involves introducing magnetic properties to the nanoparticles through core-shell structures, doping or coating with magnetic materials, physical or chemical reactions, or biological synthesis methods. However, this study demonstrates the versatility and potential of magnetic nanoparticles (MNPs) for various biomedical applications. Moreover, the in vitro and in vivo studies highlight MNPs' promising role in targeted drug delivery, imaging contrast enhancement, and magnetic hyperthermia for cancer therapy. Moving forward, further research is warranted to optimize MNPs' design, explore novel functionalization strategies, and address challenges related to biocompatibility, scalability, and regulatory approval. Therefore, MNPs reflects transformative impact across various fields, emphasizing their potential to drive advancements in healthcare, environmental protection, and technology. Continued research and innovation in this field are expected to unlock new opportunities and address global challenges, driving progress towards a healthier, cleaner, and more technologically advanced future. Mechanism (biology) ; Characterization (materials science) ; Nanotechnology ; Magnetic nanoparticles ; Nanoparticle ; Materials science ; Physics Nanoparticle-Based Drug Delivery
Authors: Martin Seifrid | Stanley Lo | Dylan G. Choi | Gary Tom | My Linh Le | Kunyu Li | Rahul Sankar | Hoai‐Thanh Vuong | Hiba Wakidi | Ahra Yi | Ziyue Zhu | Nora Schopp | Aaron Peng | Benjamin R. Luginbuhl | Thuc‐Quyen Nguyen | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2024-03-31)
Field: Materials Science — Materials Chemistry

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices Our study explores the current state of machine learning (ML) as applied to predicting and designing organic photovoltaic (OPV) devices. We outline key considerations for selecting the method of encoding a molecular structure and selecting the algorithm while also emphasizing important aspects of training and rigorously evaluating ML models. This work presents the first dataset of OPV device fabrication data mined from the literature. The top models achieve state-of-the-art predictive performance. In particular, we identify an algorithm that is used less frequently, but may be particularly well suited to similar datasets. However, predictive performance remains modest (R2 ≅ 0.6) overall. An in-depth analysis of the dataset attributes this limitation to challenges relating to the size of the dataset, as well as data quality and sparsity. These aspects are directly tied to difficulties imposed by current reporting and publication practices. Advocating for standardized reporting of OPV device fabrication data reporting in publications emerges as crucial to streamline literature mining and foster ML adoption. This comprehensive investigation emphasizes the critical role of both data quantity and quality, and highlights the need for collective efforts to unlock ML's potential to drive advancements in OPV. Computer science ; Quality (philosophy) ; Photovoltaic system ; Key (lock) ; Data science ; Machine learning ; Work (physics) ; State (computer science) ; Artificial intelligence ; Data mining ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Hieu Huynh | Khanh Le | Linh Vu | Trang T. Nguyen | Matthew Holcomb | Stefano Forli | Hung Phan
Source: ChemRxiv (preprint, 2024-01-31)
Field: Chemistry — Physical and Theoretical Chemistry

Synergy of Machine Learning and Density Functional Theory Calculations for Predicting Experimental Lewis Base Affinity and Lewis Polybase Binding Atoms Investigation of Lewis acid-base interactions has been conducted by ab initio calculations and Machine Learning (ML) models. This study aims to resolve two critical tasks that have not been quantitatively investigated. First, ML models developed from Density Functional Theory (DFT) calculations predict experimental BF3 affinity with Pearson correlation coefficients around 0.9 and mean absolute errors around 10 kJ mol-1. The ML models are trained by DFT-calculated BF3 affinity of more than 3000 adducts, with input features readily obtained by rdkit. Second, the ML models have the capability of predicting the relative strength of Lewis base binding atoms in Lewis polybases, which is either an extremely challenging task to conduct experimentally or a computationally expensive task for ab initio methods. The study demonstrates and solidifies the potential of combining DFT calculations and ML models to predict experimental properties, especially those that are scarce and impractical to empirically acquire. Lewis acids and bases ; Density functional theory ; Base (topology) ; Computational chemistry ; Chemistry ; Mathematics ; Mathematical analysis ; Organic chemistry ; Catalysis Various Chemistry Research Topics ; Machine Learning in Materials Science
Authors: Sachin S. Tirlapur | Vinut V. Tambe | Sushant S. Kakati | Shridhar N. Mathad | Sangamesh V. Angadi | Deepak B. Shirgaonkar | A. S. Pujar | Satyappa Laxman Galagali | P.R. Jeergal | Chidanandayya S. Hiremath | R. B. Pujar
Source:  (article, 2024-08-27)
Field: Materials Science — Materials Chemistry

Nanoparticle synthesis and characterization of Li–Mg–Al ferrites: Potential applications in capacitor and magnetic storage media Nanoparticle synthesis and characterization of Li–Mg–Al ferrites: Potential applications in capacitor and magnetic storage media. / S. S. Tirlapur, V. V. Tambe, S. S. Kakati, Sh. N. Mathad, S. V. Angadi, D. B. Shirgaonkar, A. S. Pujar, S. L. Galagali, P. R. Jeergal, C. S. Hiremath, R. B. Pujar. Nano Studies. – 2023–2024. – # 23/24. – pp. 39-50. – eng. This study presents the synthesis and characterization of Li0.50Mg0.75AlxFe2–xO4 ferrite nanoparticles, where x varies from 0.15 to 0.60. The synthesized nanoparticles were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy-dispersive X-ray analysis (EDX), Fourier-transform infrared (FTIR) spectroscopy, dielectric properties and vibrating sample magnetometry (VSM). The XRD analysis showed that all the samples have a single-phase fcc spinel structure. The SEM images revealed the formation of spherical nanoparticles with a size range of 11–39 nm. EDX analysis confirmed the presence of constituent elements and Al substitution. FTIR spectra indicated the characteristic vibrational frequencies corresponding to metal–oxygen bonds in the spinel structure. The dielectric properties of the samples were studied as a function of frequency and temperature. The dielectric constant and dielectric loss showed a decrease with an increase in frequency. The VSM measurements showed that all the samples exhibit ferrimagnetic behavior. The magnetic properties were observed to be dependent on Al substitution with a decrease in saturation magnetization. The results indicate that Li0.50Mg0.75AlxFe2–xO4 nanoparticles have potential applications in various fields such as capacitors, and magnetic storage media. Fig. 4, Tab. 5, Ref. 28. Characterization (materials science) ; Materials science ; Nanoparticle ; Capacitor ; Magnetic nanoparticles ; Nanotechnology ; Optoelectronics ; Electrical engineering ; Engineering ; Voltage Magnetic Properties and Synthesis of Ferrites ; Advancements in Battery Materials ; Electromagnetic wave absorption materials
Authors: G. Chandana | Murudagalli Basavaraju Shivaswamy | M. Asha | S. Jeevitha | Honnegowdanahalli Shivabasappa Nagendra Prasad | Babitha Hemanth | Sangamesha Madanahalli Ankanathappa | B. S. Madhukar | L. Mallesha | J. G. Manjunatha
Source: Journal of Electrochemical Science and Engineering (article, 2024-12-11)
Field: Chemistry — Electrochemistry

Ecofriendly synthesis of NiZnFe2O5 nanoparticle by papaya leaf extract for electrochemical detection of ascorbic acid in orange juice and pharmaceuticals A green technique has been employed to synthesize the NiZnFe2O5 nanoparticles using papaya leaf extract. The X-ray diffraction investigated the structural characteristics and crystalline size. The morphology of the synthesised nanoparticle is analysed by scanning electron microscopy. The average diameter of the nanoparticle is 47.93 nm, which was determined using the dynamic light scattering method. UV/Vis diffuse reflectance spectrum results revealed that the NiZnFe2O5 NPs band gap is 1.8 eV. It was calculated using the Kubelka-Monk function and energy dispersion X-ray spectroscopy analysis was used to identify the synthesized nanoparticle's elements. The electrochemical behaviour of NiZnFe2O5 modified glassy carbon electrode (GCE) nd bare GCE was studied to detect ascorbic acid using cyclic voltammetry and differential pulse voltammetry. Compared to unmodified GCE, NiZnFe2O5 nanoparticles modified GCE exhibit excellent electrocatalytic activity towards the AA oxidation, which was proved by the increase in peak current and decrease in peak potential. Electrochemical impedance analysis suggests that the NiZnFe2O5 nanoparticles significantly enhance the charge transfer rate. The linear response of the peak current on the concentration of AA was obtained in the range of 0.2-50 µM. The detection limit was found to be 1.038 µM. The present work serves as a systematic benchmark to assess the electrochemical sensing potential of NiZnFe2O5 NPs towards AA in orange juice and pharmaceuticals. Ascorbic acid ; Orange juice ; Cyclic voltammetry ; Nanoparticle ; Detection limit ; Differential pulse voltammetry ; Dielectric spectroscopy ; Electrochemistry ; Dynamic light scattering ; Nuclear chemistry ; Materials science ; Analytical Chemistry (journal) ; Electrode ; Chemistry ; Nanotechnology ; Chromatography Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: Ji-Yoon Kim | Doğancan Sarı | Qian Chen | Gerbrand Ceder | Kristin A. Persson
Source: Chemistry of Materials (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Evaluating Material Design Principles for Calcium-Ion Mobility in Intercalation Cathodes Multivalent-ion batteries offer an alternative to Li-based technologies, with the potential for greater sustainability, improved safety, and higher energy density, primarily due to their rechargeable system featuring a passivating metal anode. Although a system based on the Ca<sup>2+</sup>/Ca couple is particularly attractive given the low electrochemical plating potential of Ca<sup>2+</sup>, the remaining challenge for a viable rechargeable Ca battery is to identify Ca cathodes with fast ion transport. In this work, a high-throughput computational pipeline is adapted to (1) discover novel Ca cathodes in a largely unexplored space of "empty intercalation hosts" and (2) develop material design rules for Ca-ion mobility. One candidate from the screening, W<sub>2</sub>O<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub>, is confirmed to have a low Nudged Elastic Band (NEB) barrier of 168 meV within a one-dimensional (1D) ion percolation topology. This candidate is subsequently synthesized and electrochemically tested, achieving reversible Ca cycling with a capacity of 25 mA h/g. To further accelerate the screening for promising Ca intercalation electrodes, machine learning (ML) Random Forest (RF) and Extreme Gradient Boosting (XGB) classification models are created with local environment descriptors based on a large, structurally and chemically diverse dataset of minimum energy pathways, spanning over 5,000 density functional theory (DFT) site energy calculations. Accuracies of 92% are achieved, material design metrics are quantified, ML force-fields are leveraged in an accelerated iteration of the screening, and a total of 27 novel Ca cathode materials are highlighted for further investigation. Cathode ; Intercalation (chemistry) ; Anode ; Materials science ; Density functional theory ; Ion ; Electrochemistry ; Electrode ; Nanotechnology ; Computer science ; Chemistry ; Inorganic chemistry ; Computational chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research