jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kshitij Mehta | Massimiliano Lupo Pasini | Stephan Irle | Pilsun Yoo | Frédéric Suter | Dmitry Ganyushin | Scott Klasky
Source:  (article, 2024-05-27)
Field: Chemistry — Spectroscopy

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules Deep learning models are efficient computational tools that can accelerate the inverse design of molecules with desired functional properties by generating predictions at a fraction of the time required by traditional quantum chemical approaches. To ensure that a model maintains accuracy and transferability across broad regions of the chemical space explored during the inverse design, it must be trained on massively large volumes of simulation data. This requires running large-scale ensemble quantum chemical calculations on high-performance computing (HPC) systems for data collection. However, the efficient execution of such large ensemble calculations and the management of large volumes of output data require tools that can judiciously utilize computational resources and manage metadata overhead on the file system. Therefore, we present a high-performance, scalable, ensemble management framework for performing data-intensive quantum chemical electronic structure calculations for organic molecules. This framework provides abstractions to plug different ab initio, first principles, and first principles-based semi-empirical methods and executes them efficiently at large scale on HPC systems. It dynamically distributes tasks to resources and uses tiered storage for managing large collections of files. We employed this framework to process over ten million organic molecules and generate open-source datasets that provide UV-vis absorption spectra by running time-dependent density-functional tight-binding calculations. It is the largest database containing molecular optical spectra that were simulated with quantum chemical methods in a consistent manner. Scaling ; Quantum chemical ; Computer science ; Quantum ; Statistical physics ; Molecule ; Physics ; Quantum mechanics ; Mathematics Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Machine Learning in Materials Science
Authors: Shusuke Ito | Koki Muraoka | Akira Nakayama
Source: ChemRxiv (preprint, 2024-03-21)
Field: Materials Science — Materials Chemistry

De Novo Design of Organic Structure-Directing Agents for Zeolites Using a General-Purpose Large Language Model Designing organic molecules lies at the heart of solving numerous chemistry-related challenges, necessitating effective collaboration between human intuition and computational power. This study demonstrates how general-purpose Large Language Models (LLMs) such as GPT-4 can facilitate the design of potent molecules, leveraging feedback from experiments and empirical knowledge through natural language. We used this approach to design organic structure-directing agents (OSDAs) that guide the crystallization of zeolites. A computational workflow was developed, wherein the LLM proposed novel OSDAs to stabilize targeted zeolites. The suggested candidates underwent evaluation through empirical screening criteria and atomistic simulation. Feedback was then provided to the LLM in natural language to refine subsequent proposals, thus progressively enhancing the proposed OSDAs and promoting the exploration of chemical space. The predicted candidates encompassed experimentally validated OSDAs, structurally analogous ones, and novel ones with superior affinity scores, underscoring the robust capability of the LLM. The collaborations between humans and machines, utilizing natural language as the communication interface, hold potential for application in other molecular design tasks, including drug design. Workflow ; Computer science ; Intuition ; Chemical space ; Natural language ; Artificial intelligence ; Software engineering ; Human–computer interaction ; Drug discovery ; Chemistry ; Cognitive science ; Psychology Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Asymmetric Hydrogenation and Catalysis
Authors: Shusuke Ito | Koki Muraoka | Akira Nakayama
Source: ChemRxiv (preprint, 2024-03-20)
Field: Materials Science — Materials Chemistry

De Novo Design of Organic Structure-Directing Agents for Zeolites Using a General-Purpose Large Language Model Designing organic molecules lies at the heart of solving numerous chemistry-related challenges, necessitating effective collaboration between human intuition and computational power. This study demonstrates how general-purpose Large Language Models (LLMs) such as GPT-4 can facilitate the design of potent molecules, leveraging feedback from experiments and empirical knowledge through natural language. We used this approach to design organic structure-directing agents (OSDAs) that guide the crystallization of zeolites. A computational workflow was developed, wherein the LLM proposed a novel OSDA to stabilize targeted zeolites. The suggested candidate underwent evaluation through empirical screening criteria and atomistic simulation. Feedback was then provided to the LLM in natural language to refine subsequent proposals, thus progressively enhancing the proposed OSDAs and promoting the exploration of chemical space. The predicted candidates encompassed experimentally validated OSDAs, structurally analogous ones, and novel ones with superior affinity scores, underscoring the robust capability of the LLM. The collaborations between humans and machines, utilizing natural language as the communication interface, hold potential for application in other molecular design tasks, including drug design. Workflow ; Intuition ; Chemical space ; Computer science ; Natural language ; Space (punctuation) ; Artificial intelligence ; Software engineering ; Biochemical engineering ; Drug discovery ; Engineering ; Cognitive science ; Bioinformatics ; Psychology ; Biology Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Asymmetric Hydrogenation and Catalysis
Authors: Mantas Vaškevičius | Jurgita Kapočiūtė-Dzikienė
Source: Preprints.org (preprint, 2024-11-25)
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 ; Natural language processing Machine Learning in Materials Science
Authors: Joren Van Herck | M.V. Gil | Kevin Maik Jablonka | Alex Abrudan | Andy S. Anker | Mehrdad Asgari | Ben Blaiszik | Leander Choudhury | Clémence Corminbœuf | Hilal Daglar | Ian Foster | Susana García | Matthew Garvin | Guillaume Godin | Lydia L. Good | Jianan Gu | N.‐X. Hu | Xin Jin | Tanja Junkers | Seda Keskın | Tuomas P. J. Knowles | Rubén Laplaza | Sauradeep Majumdar | Hossein Mashhadimoslem | Ruaraidh D. McIntosh | Seyed Mohamad Moosavi | Beatriz Mouriño | Francesca Nerli | C. Pevida | Neda Poudineh | Mahyar Rajabi-Kochi | Kadi L. Saar | Fahimeh Hooriabad Saboor | Morteza Sagharichiha | K. J. Schmidt | Jiale Shi | Dennis Svatunek | Marco Taddei | Igor V. Tetko | D. Tolnai | Sahar Vahdatifar | Jonathan K. Whitmer | D. C. Florian Wieland | Regine Willumeit‐Römer | Andreas Züttel | Berend Smit
Source: ChemRxiv (preprint, 2024-07-30)
Field: Materials Science — Materials Chemistry

Assessment of Fine-Tuned Large Language Models for Real-World Chemistry and Material Science Applications The current generation of large language models (LLMs), like ChatGPT, have limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. In this work, we explore the potential and limitations of this approach. We studied the performance of fine-tuning GPT-J-6B, a public-domain version of the GPT family, for a range of different chemical questions. We find that in most, if not all, cases, this approach outperforms the benchmark (random guessing) for a simple classification problem. Depending on the size of the dataset and the type of questions, we can also address more sophisticated problems. The most important conclusions of this work are that, for all datasets considered, their conversion into an LLM fine-tuning training set is straightforward and that fine-tuning with even relatively small datasets leads to predictive models. These results suggest that the systematic use of LLMs to guide experiments and simulations will be a powerful technique in any research study, significantly reducing unnecessary experiments or computations. Benchmark (surveying) ; Set (abstract data type) ; Computer science ; Range (aeronautics) ; Computation ; Work (physics) ; Simple (philosophy) ; Fine-tuning ; Machine learning ; Artificial intelligence ; Algorithm ; Materials science ; Epistemology ; Engineering ; Geography Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Jonas Elsborg | Arghya Bhowmik
Source: Machine Learning Science and Technology (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

ArtiSAN: navigating the complexity of material structures with deep reinforcement learning Abstract Finding low-energy atomic ordering in compositionally complex materials is one of the hardest problems in materials discovery, the solution of which can lead to breakthroughs in functional materials—from alloys to ceramics. In this work, we present the Arti ficial S tructure A rranging N et ( ArtiSAN )—a reinforcement learning agent utilizing graph representation that is trained to find low-energy atomic configurations of multicomponent systems through a series of atomic switch operations. ArtiSAN is trained on small alloy supercells ranging from binary to septenary. Strikingly, ArtiSAN generalizes to much larger systems of more than a thousand atoms, which are inaccessible with state-of-the-art methods due to the combinatorially larger search space. The performance of the current ArtiSAN agent is tested and deployed on several compositions that can be correlated with known experimental and high-fidelity computational structures. ArtiSAN demonstrates transfer across size and composition and finds physically meaningful structures using no energy evaluation calls once fully trained. While ArtiSAN will require further modifications to capture all variability in structure search, it is a remarkable step towards solving the structural part of the problem of disordered materials discovery. Reinforcement learning ; Reinforcement ; Computer science ; Artificial intelligence ; Engineering ; Structural engineering Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Robot Manipulation and Learning
Authors: Janosh Riebesell | T. Wesley Surta | Rhys E. A. Goodall | Michael W. Gaultois | Alpha A. Lee
Source: arXiv (Cornell University) (preprint, 2024-01-11)
Field: Materials Science — Materials Chemistry

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials Materials with high-dielectric constant easily polarize under external electric fields, allowing them to perform essential functions in many modern electronic devices. Their practical utility is determined by two conflicting properties: high dielectric constants tend to occur in materials with narrow band gaps, limiting the operating voltage before dielectric breakdown. We present a high-throughput workflow that combines element substitution, ML pre-screening, ab initio simulation and human expert intuition to efficiently explore the vast space of unknown materials for potential dielectrics, leading to the synthesis and characterization of two novel dielectric materials, CsTaTeO6 and Bi2Zr2O7. Our key idea is to deploy ML in a multi-objective optimization setting with concave Pareto front. While usually considered more challenging than single-objective optimization, we argue and show preliminary evidence that the $1/x$-correlation between band gap and permittivity in fact makes the task more amenable to ML methods by allowing separate models for band gap and permittivity to each operate in regions of good training support while still predicting materials of exceptional merit. To our knowledge, this is the first instance of successful ML-guided multi-objective materials optimization achieving experimental synthesis and characterization. CsTaTeO6 is a structure generated via element substitution not present in our reference data sources, thus exemplifying successful de-novo materials design. Meanwhile, we report the first high-purity synthesis and dielectric characterization of Bi2Zr2O7 with a band gap of 2.27 eV and a permittivity of 20.5, meeting all target metrics of our multi-objective search. Dielectric ; Permittivity ; Band gap ; Workflow ; Characterization (materials science) ; Materials science ; Computer science ; Optoelectronics ; Nanotechnology Machine Learning in Materials Science ; Delphi Technique in Research ; BIM and Construction Integration
Authors: Essam Mohamed Sharshira | Ahmed A. Ataalla | Khaled O. Sebakhy | Mohamed Hagar
Source: Results in Chemistry (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Molecular conjugation and crystallinity impact of designed novel chalcone based polyesters and polyesteramides on their thermal-electrical conductivity Two polyesteramides were synthesized via polycondensation reactions involving 4′-amino-4-hydroxychalcone 3a with succinic acid (4) and terephthalic acid (5), utilizing phosphoryl chloride as a chlorinating agent. A parallel reaction with (E)-1,3-bis(4-hydroxyphenyl) prop-2-en-1-one chalcone (3b) yielded two polyesters under identical conditions. The resultant polymers were obtained with excellent yields ranging from 93 % to 98 %. Characterization of the obtained polyesteramides and polyesters was conducted using various analytical techniques. Fourier-transform infrared spectroscopy (FTIR) evidenced the disappearance of the OH group's broad band from the initial monomers, providing strong support for polymer formation. Thermal gravimetric analysis (TGA) results indicated high thermal stability of all prepared polymers at elevated temperatures, designating them as potential thermoplastic polymers. Powder X-ray diffraction (XRD) outcomes demonstrated a significant amorphous nature for all synthesized polymers, with degrees ranging from 67.56 % to 74.75 %. Analysis of current/voltage (I-V) curves revealed a wide spectrum of electrical conductivity for polymers containing chalcone in their repeating units, ranging from negligibly low values of 0.08 µS/cm to 0.3 μS/cm, indicating their potential as semiconducting materials. Variations in conductivity across synthesized polymers were attributed to the degree of conjugation in polymeric chains and their crystallinity levels. Crystallinity ; Polyester ; Chalcone ; Materials science ; Thermal conductivity ; Composite material ; Thermal ; Chemical engineering ; Organic chemistry ; Chemistry ; Thermodynamics ; Engineering ; Physics Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Synthesis and properties of polymers
Authors: Junkil Park | Youhan Lee | Jihan Kim
Source: ChemRxiv (preprint, 2024-07-05)
Field: Chemistry — Inorganic Chemistry

Multi-modal conditioning for metal-organic frameworks generation using 3D modeling techniques The design of porous materials with user-desired properties has been a great interest for the last few decades. However, the flexibility of target properties has been highly limited, and targeting multiple properties of diverse modalities simultaneously has been scarcely explored. Furthermore, although deep generative models have opened a new paradigm in materials generation, their incorporation into porous materials such as metal-organic frameworks (MOFs) has not been satisfactory due to their structural complexity. In this work, we introduce MOFFUSION, a latent diffusion model that addresses the aforementioned challenges. Signed distance functions (SDFs) were employed for the input representation of MOFs, marking their first usage in representing porous materials for generative models. Using the suitability of SDFs in describing complicated pore structures, MOFFUSION exhibited exceptional generation performance, and demonstrated its versatile capability of conditional generation with handling diverse modalities of data, including numeric, categorical, text data, and their combinations. Modal ; Conditioning ; Computer science ; Environmental science ; Materials science ; Mathematics ; Polymer chemistry ; Statistics Metal-Organic Frameworks: Synthesis and Applications
Authors: Soumyakanti Adhikari | Anup Subedee | Deval Prasad Bhattarai | Hari Bhakta Oli | Ram Lal Shrestha
Source: International Research Journal of Pure and Applied Chemistry (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

Green Synthesis and Characterization of Silver-copper Bimetallic Nanoparticles Using Alternanthera brasiliana Stem Extract The green synthesis of bimetallic silver copper nanoparticles (Ag-CuNPs) has gained attention due to their broad applications. Here, Ag-Cu bimetallic nanoparticles were synthesized using an aqueous extract from the stem of Alternanthera brasiliana. The phytochemical composition of the extract was evaluated through standard phytochemical tests, and the formation of nanoparticles was tracked using UV-visible spectroscopy. The resulting nanoparticles were further characterized using Fourier Transform Infrared (FTIR) spectroscopy, and X-ray diffraction (XRD). Using the Debye-Scherrer formula to interpret XRD data, the estimated sizes of the silver, copper, and silver-copper nanoparticles were 11.81 nm, 55.33 nm, and 12.45 nm, respectively, all synthesized under comparable conditions. Additionally, the antimicrobial properties of these nanoparticles were evaluated, revealing significant activity against various selected microorganisms. The study highlights an eco-friendly approach for synthesizing Ag-CuNPs with promising antimicrobial potential. Bimetallic strip ; Chemistry ; Nanoparticle ; Copper ; Fourier transform infrared spectroscopy ; Silver nanoparticle ; Nuclear chemistry ; Nanotechnology ; Chemical engineering ; Catalysis ; Organic chemistry ; Materials science Nanoparticles: synthesis and applications ; Moringa oleifera research and applications
Authors: Ruifeng Li | Li, Mingqian | Liu, Wei | Chen, Hongyang
Source: arXiv (Cornell University) (preprint, 2024-08-02)
Field: Materials Science — Materials Chemistry

GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs Effective molecular representation learning is crucial for advancing molecular property prediction and drug design. Mainstream molecular representation learning approaches are based on Graph Neural Networks (GNNs). However, these approaches struggle with three significant challenges: insufficient annotations, molecular diversity, and architectural limitations such as over-squashing, which leads to the loss of critical structural details. To address these challenges, we introduce a new class of GNNs that integrates the Kolmogorov-Arnold Networks (KANs), known for their robust data-fitting capabilities and high accuracy in small-scale AI + Science tasks. By incorporating KANs into GNNs, our model enhances the representation of molecular structures. We further advance this approach with a variant called SwallowKAN (SKAN), which employs adaptive Radial Basis Functions (RBFs) as the core of the non-linear neurons. This innovation improves both computational efficiency and adaptability to diverse molecular structures. Building on the strengths of SKAN, we propose a new class of GNNs, GNN-SKAN, and its augmented variant, GNN-SKAN+, which incorporates a SKAN-based classifier to further boost performance. To our knowledge, this is the first work to integrate KANs into GNN architectures tailored for molecular representation learning. Experiments across 6 classification datasets, 6 regression datasets, and 4 few-shot learning datasets demonstrate that our approach achieves new state-of-the-art performance in terms of accuracy and computational cost. Representation (politics) ; Computer science ; Political science ; Law Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Genomics and Phylogenetic Studies
Authors: Santosh Kumar Kundara | Kanhaiya Chawla | Dinesh Kumar Yadav | Chhagan Lal | Balram Tripathi | Narendra Jakhar
Source: Open Journal of Composite Materials (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Study of Structural, Optical, Electrical and Dielectric Properties of Gd-Doped ZnO Composites Synthesized by Solid State Reaction Method In this manuscript, we are reporting structural, bonding, optical, dielectric, and electrical properties of Gd-doped ZnO composite samples (Zn1−xGdxO, x = 0, 0.05, 0.10) prepared by solid-state reaction method. XRD spectra confirm the wurtzite hexagonal phase with a grain size distribution of 42 - 47 nm. The FT-IR spectra confirm bonding behavior like Zn-O, O=C=O, and O-H stretching modes. FESEM micrographs show that the grains of crystallites possess nearly spherical morphology. Optical absorption spectra confirm that the optical band gap decreases systematically from 3.19 eV to 3.15 eV for x = 0.0 to x = 0.10 samples. For all samples, PL spectra exhibited near-band emission, blue emission, and green emission peaks. The dielectric constant decreases as the applied frequency increases. Hall effect results show that with increasing doping concentration of Gd, mobility and resistivity increase while bulk concentration decreases. Current-Voltage study shows that current increases when temperature is increased. Rare earth-doped ZnO is potential material used for optoelectronics and spintronics device applications. Properties of Gd-doped ZnO are studied by various research groups, but dielectric studies are limitedly reported. Therefore, the present research work aims to study the change of electrical, optical, and dielectric properties of Gd-doped ZnO for device applications. Materials science ; Wurtzite crystal structure ; Dielectric ; Doping ; Crystallite ; Band gap ; Analytical Chemistry (journal) ; Photoluminescence ; Electrical resistivity and conductivity ; Optoelectronics ; Zinc ; Metallurgy ; Chemistry ZnO doping and properties ; Copper-based nanomaterials and applications ; Magnesium Oxide Properties and Applications