jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prashant Govindarajan | Santiago Miret | Jarrid Rector-Brooks | Mariano Phielipp | Janarthanan Rajendran | Sarath Chandar
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Learning conditional policies for crystal design using offline reinforcement learning Conservative Q-learning for band-gap conditioned crystal design with DFT evaluations – the model is trained on trajectories constructed from crystals in the Materials Project. Results indicate promising performance for lower band gap targets. Reinforcement learning ; Reinforcement ; Computer science ; Artificial intelligence ; Psychology ; Social psychology Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition
Authors: Frank Hu | Michael S. Chen | Grant M. Rotskoff | Matthew W. Kanan | Thomas E. Markland
Source: arXiv (Cornell University) (preprint, 2024-08-15)
Field: Chemistry — Spectroscopy

Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning Rapid determination of molecular structures can greatly accelerate workflows across many chemical disciplines. However, elucidating structure using only one-dimensional (1D) NMR spectra, the most readily accessible data, remains an extremely challenging problem because of the combinatorial explosion of the number of possible molecules as the number of constituent atoms is increased. Here, we introduce a multitask machine learning framework that predicts the molecular structure (formula and connectivity) of an unknown compound solely based on its 1D 1H and/or 13C NMR spectra. First, we show how a transformer architecture can be constructed to efficiently solve the task, traditionally performed by chemists, of assembling large numbers of molecular fragments into molecular structures. Integrating this capability with a convolutional neural network (CNN), we build an end-to-end model for predicting structure from spectra that is fast and accurate. We demonstrate the effectiveness of this framework on molecules with up to 19 heavy (non-hydrogen) atoms, a size for which there are trillions of possible structures. Without relying on any prior chemical knowledge such as the molecular formula, we show that our approach predicts the exact molecule 69.6% of the time within the first 15 predictions, reducing the search space by up to 11 orders of magnitude. NMR spectra database ; Computer science ; Artificial intelligence ; Machine learning ; Spectral line ; Physics Molecular spectroscopy and chirality
Authors: V. Diana Rakotonirina | Marco Bragato | Stefan Heinen | O. Anatole von Lilienfeld
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Combining Hammett <i>σ</i> constants for Δ-machine learning and catalyst discovery We present a simple and fast linear model for discovering organometallic catalysts for the Suzuki–Miyaura cross-coupling reaction, using a combinatorial approach. Catalysis ; Hammett equation ; Chemistry ; Psychology ; Physics ; Reaction rate constant ; Organic chemistry ; Quantum mechanics ; Kinetics Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Catalysis and Oxidation Reactions
Authors: Seong-Min Kim | Yousung Jung | Joshua Schrier
Source: ChemRxiv (preprint, 2024-04-18)
Field: Materials Science — Materials Chemistry

Large Language Models for Inorganic Synthesis Predictions We evaluate the effectiveness of pre-trained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to—and sometimes better than—recent bespoke machine learning models for these tasks, but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists. Bespoke ; Computer science ; Selection (genetic algorithm) ; Baseline (sea) ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Ge Lei | R. Docherty | Samuel J. Cooper
Source: arXiv (Cornell University) (preprint, 2024-03-11)
Field: Materials Science — Materials Chemistry

Materials science in the era of large language models: a perspective Large Language Models (LLMs) have garnered considerable interest due to their impressive natural language capabilities, which in conjunction with various emergent properties make them versatile tools in workflows ranging from complex code generation to heuristic finding for combinatorial problems. In this paper we offer a perspective on their applicability to materials science research, arguing their ability to handle ambiguous requirements across a range of tasks and disciplines mean they could be a powerful tool to aid researchers. We qualitatively examine basic LLM theory, connecting it to relevant properties and techniques in the literature before providing two case studies that demonstrate their use in task automation and knowledge extraction at-scale. At their current stage of development, we argue LLMs should be viewed less as oracles of novel insight, and more as tireless workers that can accelerate and unify exploration across domains. It is our hope that this paper can familiarise material science researchers with the concepts needed to leverage these tools in their own research. Perspective (graphical) ; Political science ; Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Badria M. Al‐Shehri | A. H. Korna | S. Fares | Ahmed H. Ali
Source: International Journal of Polymer Science (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Gamma‐Induced Transformations in PVC‐Based Nanocomposites for Tailored Optical Properties This study explores the effects of gamma irradiation on the properties of polyvinyl chloride (PVC):(50 nm)/CeO 2 :(10 nm)/TiO 2 nanocomposite films. Gamma irradiation significantly enhances the optical conductivity ( σ opt) of the films, signifying increased light‐to‐current conversion efficiency. This improvement is attributed to a reduction in the bandgap and increased light absorption induced by irradiation. X‐ray diffraction (XRD) analysis reveals a decrease in crystallinity due to disrupted atomic order within the nanocomposite. Williamson–Hall (W‐H) plots show a dose‐dependent reduction in crystallite size and a corresponding rise in lattice strain with increasing irradiation dose. Infrared (IR) spectroscopy suggests potential bond breaking of O‐H groups and modifications in C=O functional groups upon irradiation. The absorption coefficient and binding energy exhibit a nonmonotonic response with increasing dose, reaching an optimum at around 100 kGy. These findings demonstrate the potential of gamma irradiation for tailoring the optoelectronic properties of PVC:(50 nm)/CeO 2 :(10 nm)/TiO 2 nanocomposites. This work paves the way for the development of next‐generation optoelectronic devices with optimized functionalities. Materials science ; Nanocomposite ; Composite material Polymer Nanocomposite Synthesis and Irradiation ; Nanotechnology research and applications ; TiO2 Photocatalysis and Solar Cells
Authors: Yogesh Kumar | Maninder Kaur
Source: EPH - International Journal of Applied Science (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

EXPLORING THE GREEN SYNTHESIS OF NANOPARTICLES AND THEIR MULTIFACETED IMPACT ON BIOMEDICAL APPLICATIONS AND ENVIRONMENTAL REMEDIATION: A REVIEW PAPER Nanoparticles have attracted significant interest in recent years due to their distinctive characteristics and wide-ranging uses in different domains. Nanoparticle production using environmentally friendly technologies has become a sustainable and eco-friendly alternative to traditional chemical processes. This review comprehensively explores the various green synthesis techniques employed for the production of nanoparticles, including those utilizing plant extracts, microorganisms, enzymes, and agricultural waste. Additionally, the multifaceted impact of green-synthesized nanoparticles on biomedical and environmental applications is elucidated. In biomedicine, these eco-friendly nanoparticles exhibit promising potential in drug delivery, diagnostics, and targeted delivery capabilities. Furthermore, their applications in environmental remediation, including pollutant degradation, heavy metal ion removal, and wastewater treatment, highlight their significance in addressing contemporary environmental challenges. The review also discusses the key properties and mechanisms underlying the enhanced performance of green-synthesized nanoparticles in biomedical and environmental applications, emphasizing their sustainable nature and reduced environmental footprint. Moreover, challenges and future perspectives in the synthesis and utilization of green nanoparticles are critically examined, aiming to pave the way for the development of innovative and sustainable nanotechnologies. Environmental remediation ; Nanotechnology ; Nanoparticle ; Environmental science ; Engineering ethics ; Engineering ; Materials science ; Ecology ; Biology ; Contamination Nanoparticles: synthesis and applications
Authors: Yassine Beldjoudi
Source: Advanced Functional Materials (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Zwitterionic Cyclophane Molecules: Toward Novel Functional Materials Abstract For over 60 years, cyclophanes have inspired the design of novel molecular architectures of different sizes and geometries, unleashing significant advances in supramolecular chemistry and also in biomedical and materials sciences. Reported cyclophanes are countless and more complex molecular structures continue to be spawned. Herein, the study classifies cyclophanes into different families to emphasize and feature their structure‐properties relationships. While neutral cyclophanes have attracted more attention owing to their solid‐state physical properties, water‐soluble anionic cyclophanes have been explored predominantly in drug delivery. In the broad family of cationic cyclophanes, viologen‐based cyclophanes ( VBCs ) are widely investigated for designing molecular interlocked molecules (MIMs), molecular switches, and machines owing to their unique electrochemical properties. Despite decades of ground‐breaking research in academia, controlling the cationic cyclophanes and MIMs solid‐state structures is still a limiting factor when it comes to introducing them into functional materials. Here, the study draws attention to some current challenges in cyclophane chemistry from a materials sciences perspective and highlights processes that, if overcome, would lead to cyclophanes producing transformational materials. It is envisioned that zwitterionic VBCs can have a seminal impact on host–guest chemistry, MIMs, molecular machines, and supramolecular tessellations, which can pave the way toward next‐generation technologies. Cyclophane ; Molecule ; Chemistry ; Organic chemistry Porphyrin and Phthalocyanine Chemistry ; Supramolecular Self-Assembly in Materials ; Photochromic and Fluorescence Chemistry
Authors: Mário Fabrício Fleury Rosa | Leonor Maria Pacheco Santos | Carlos Augusto Grabois Gadelha | Aline Martins de Toledo | Rodrigo Luiz Carregaro | Ana Karoline Almeida da Silva | Lindemberg Barreto Mota da Costa | Adson Ferreira da Rocha | Suélia de Siqueira Rodrigues Fleury Rosa
Source: Frontiers in Nanotechnology (article, 2024-04-22)
Field: Materials Science — Biomaterials

Translational pathway of a novel PFF2 respirator with chitosan nanotechnology: from the concept to the practical applications Introduction: Translational Health Research (THR) is a tool aimed at assisting in the transformation of basic and/or applied scientific research into a health technology ready for commercialization. The aim of this study is to present the translational pathway in wich our research group developed a Personal Protective Equipment (PPE) called VESTA ® Facial Respirator with chitosan nanotechnology for protection against viruses, bacteria, and fungi. The aim of this study is to present the process of THR applied to a health technology research. Methods: The theoretical-methodological process of THR was applied to the Research and Development (R&amp;amp;D) of the respirator. This method is characterized by subsequent phases, as follow: T (0)—Concept, T (1) Pre-Clinical, T (2) Clinical, T (3) Industrial Scale Production, and T (4) Characterized by subsequent phases, as follow: Technological Evaluation. Results: Applying the THR process in the development and production of the Particulate Filtering Facepiece class 2 (PFF2) respirator with chitosan nanotechnology, University of Brasilia was able to transform the research idea into a respirator approved by the National Regulatory Agency for industrial-scale production within 24 months. The THR process is not a linear action; this flexibility allows essential activities for transforming research into a marketable product. Discussion: The integration among various stakeholders right from the genesis of research is a driving force for the effective utilization of results. The maturity of the country’s industrial sector is crucial for converting university research into a marketable product, and governments need to prioritize these products in healthcare system incorporations. The academic culture of scientific research needs to intensify the technological transfer phase of its inventions. Conclusion: In less than 24 months, the University of Brasilia translated research on a new PPE into the market by applying the THR method. Respirator ; Chitosan ; Nanotechnology ; Engineering ; Materials science ; Chemical engineering Nanocomposite Films for Food Packaging ; Inhalation and Respiratory Drug Delivery ; Electrospun Nanofibers in Biomedical Applications
Authors: Mengshu Ge | Yue Pan | Xiaozhi Liu | Zhicheng Zhao | Dong Su
Source: Ultramicroscopy (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Automatic center identification of electron diffraction with multi-scale transformer networks Selected area diffraction ; Segmentation ; Computer science ; Robustness (evolution) ; Artificial intelligence ; Pattern recognition (psychology) ; Transmission electron microscopy ; Materials science ; Nanotechnology ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Corrosion Behavior and Inhibition
Authors: Gang Liu | Michael Sun | Wojciech Matusik | Meng Jiang | Jie Chen
Source: arXiv (Cornell University) (preprint, 2024-10-05)
Field: Materials Science — Materials Chemistry

Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug design. This difficulty stems from the need for coherent autoregressive generation across texts and graphs. To address this, we introduce Llamole, the first multimodal LLM capable of interleaved text and graph generation, enabling molecular inverse design with retrosynthetic planning. Llamole integrates a base LLM with the Graph Diffusion Transformer and Graph Neural Networks for multi-conditional molecular generation and reaction inference within texts, while the LLM, with enhanced molecular understanding, flexibly controls activation among the different graph modules. Additionally, Llamole integrates A* search with LLM-based cost functions for efficient retrosynthetic planning. We create benchmarking datasets and conduct extensive experiments to evaluate Llamole against in-context learning and supervised fine-tuning. Llamole significantly outperforms 14 adapted LLMs across 12 metrics for controllable molecular design and retrosynthetic planning. Inverse ; Retrosynthetic analysis ; Computer science ; Linguistics ; Chemistry ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Givi Papava | Ia Chitrekashvili | Tamara Tatrishvili | Marina Gurgenishvili | Ketevan Archvadze | Nora Dokhturishvili | Eter Gavashelidze | Nazi Gelashvili | Riva Liparteliani
Source: Chemistry & Chemical Technology (article, 2024-12-20)
Field: Materials Science — General Materials Science

Synthesis and Investigation of Properties of Epoxy-Novolac Copolymers Based on Polycyclic Bisphenols of Norbornane Type The purpose of this study was the synthesis of copolymers based on the novolac oligomer and diglycidal ether of polycyclic bisphenols of the norbornane type and the study of the curing process of the obtained copolymers. Copolymers were synthesized based on polycyclic bisphenols: 4,4'-(2-norbornilidene) diphenyl; 4,4'-(hexahydro-4,7-methylenindane)-di-o-cresol; 4,4′-(hexahydro-4,7-methylenindane-5-ylidene) diphenol; 4,4′-(decahydro-1,4,5,8 - dimethylennaft-2-ylidene)diphenol; and 2,2-bis-(4-oxyphenyl)propane. The synthesis was carried out in two stages. In the first stage, the main chemical process during copolymerization is the interaction of epoxy groups with phenolic hydroxyls of the novolac oligomer, which leads to the formation of a block copolymer. In the second stage of the process polymers with a three-dimensional structure are formed as a result of the curing process. The optimal curing mode was set. Fiberglass plastics were prepared from the obtained copolymers, which were characterized by good physico-mechanical and thermal properties. Norbornane ; Epoxy ; Copolymer ; Polymer chemistry ; Organic chemistry ; Polymer science ; Materials science ; Chemistry ; Polymer Material Properties and Applications ; Synthesis and properties of polymers ; Polymer Science and Applications
Authors: Junguo Gao | Liwei Liu | Guangwei Zhang | Ting‐Tai Lee | Uwe Schachtely | Hitoshi Kobayashi
Source: High Voltage (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

Effect of nanostructured SiO <sub>2</sub> on charge capture performance and electrical ageing life of PP nanocomposites Abstract To guarantee the reliability of high‐performance and environmentally friendly novel polypropylene (PP) for use as the primary insulation in cables, an assessment of the electrical ageing life of SiO 2 /PP composite materials was carried out using a step‐stress accelerated ageing test based on the inverse power law. The study involved an analysis of microstructural characteristics and performance testing to investigate the bonding between the SiO 2 under different surface treatment methods and matrixes. Furthermore, the impact of SiO 2 on charge capture performance in PP insulation was analysed through polarisation–depolarisation current testing. The results show that silica with long alkane chains and amino groups grafted on the surface has good compatibility with PP insulating layers, which can increase the trap energy level of captured electrons and thus reduce the damage of high‐energy electrons to the composites. SiO 2 increased the life index of the PP insulating layer by 19.95%, which contributed to the improvement of the electrical ageing life of the PP insulating layer. Nanocomposite ; Materials science ; Ageing ; Nanotechnology ; Charge (physics) ; Engineering physics ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Paula Carracedo-Reboredo | Eider Aranzamendi | Shan He | Sonia Arrasate | Cristian R. Munteanu | Carlos Fernández-Lozano | Nuria Sotomayor | Esther Lete | Humberto González‐Díaz
Source: Journal of Cheminformatics (article, 2024-01-22)
Field: Chemistry — Organic Chemistry

MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R<sup>2</sup> = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo . This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products. Intermolecular force ; Selection (genetic algorithm) ; Catalysis ; Computer science ; Biochemical engineering ; Chemistry ; Combinatorial chemistry ; Computational chemistry ; Organic chemistry ; Engineering ; Artificial intelligence ; Molecule Asymmetric Synthesis and Catalysis ; Chemical Synthesis and Analysis ; Asymmetric Hydrogenation and Catalysis
Authors: Remo Eugster | Markus Orsi | Giorgio Buttitta | Nicola Serafini | Mattia Tiboni | Luca Casettari | Jean‐Louis Reymond | Simone Aleandri | Paola Luciani
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-07-04)
Field: Materials Science — Biomaterials

Leveraging machine learning to streamline the development of liposomal drug delivery systems Abstract Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid bilayers, have become a powerful tool in this field, especially with the rise of microfluidic manufacturing during the COVID-19 pandemic. Despite its efficiency, microfluidic liposomal production poses challenges, often requiring laborious, optimization on a case-by-case basis. This is due to a lack of comprehensive understanding and robust methodologies, compounded by limited data on microfluidic production with varying lipids. Artificial intelligence offers promise in predicting lipid behaviour during microfluidic production, with the still unexploited potential of streamlining development. Herein we employ machine learning to predict critical quality attributes and process parameters for microfluidic-based liposome production. Validated models predict liposome formation, size, and production parameters, significantly advancing our understanding of lipid behaviour. Extensive model analysis enhanced interpretability and investigated underlying mechanisms, supporting the transition to microfluidic production. Unlocking the potential of machine learning in drug development can accelerate pharmaceutical innovation, making drug delivery systems more adaptable and accessible. Drug delivery ; Liposome ; Computer science ; Delivery system ; Development (topology) ; Artificial intelligence ; Machine learning ; Nanotechnology ; Engineering ; Biomedical engineering ; Materials science ; Mathematics Nanoparticle-Based Drug Delivery