jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuyan Liu | Sirui Ding | Zhou Sheng | Wenqi Fan | Qiaoyu Tan
Source: arXiv (Cornell University) (preprint, 2024-06-18)
Field: Materials Science — Materials Chemistry

MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction Molecular property prediction (MPP) is a fundamental and crucial task in drug discovery. However, prior methods are limited by the requirement for a large number of labeled molecules and their restricted ability to generalize for unseen and new tasks, both of which are essential for real-world applications. To address these challenges, we present MolecularGPT for few-shot MPP. From a perspective on instruction tuning, we fine-tune large language models (LLMs) based on curated molecular instructions spanning over 1000 property prediction tasks. This enables building a versatile and specialized LLM that can be adapted to novel MPP tasks without any fine-tuning through zero- and few-shot in-context learning (ICL). MolecularGPT exhibits competitive in-context reasoning capabilities across 10 downstream evaluation datasets, setting new benchmarks for few-shot molecular prediction tasks. More importantly, with just two-shot examples, MolecularGPT can outperform standard supervised graph neural network methods on 4 out of 7 datasets. It also excels state-of-the-art LLM baselines by up to 15.7% increase on classification accuracy and decrease of 17.9 on regression metrics (e.g., RMSE) under zero-shot. This study demonstrates the potential of LLMs as effective few-shot molecular property predictors. The code is available at https://github.com/NYUSHCS/MolecularGPT. Shot (pellet) ; Property (philosophy) ; One shot ; Computer science ; Natural language processing ; Artificial intelligence ; Chemistry ; Philosophy ; Engineering ; Epistemology ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Haoqiang Guo | Sendong Zhao | Haochun Wang | Yanrui Du | Bing Qin
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2024-03-24)
Field: Materials Science — Materials Chemistry

MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts Deep learning is now widely used in drug discovery, providing significant acceleration and cost reduction. As the most fundamental building block, molecular representation is essential for predicting molecular properties to enable various downstream applications. Most existing methods attempt to incorporate more information to learn better representations. However, not all features are equally important for a specific task. Ignoring this would potentially compromise the training efficiency and predictive accuracy. To address this issue, we propose a novel approach, which treats language models as an agent and molecular pretraining models as a knowledge base. The agent accentuates task-relevant features in the molecular representation by understanding the natural language description of the task, just as a tailor customizes clothes for clients. Thus, we call this approach MolTailor. Evaluations demonstrate MolTailor's superior performance over baselines, validating the efficacy of enhancing relevance for molecular representation learning. This illustrates the potential of language model guided optimization to better exploit and unleash the capabilities of existing powerful molecular representation methods. Our code and appendix are available at https://github.com/SCIR-HI/MolTailor. Representation (politics) ; Computer science ; Natural language processing ; Human–computer interaction ; Chemistry Machine Learning in Materials Science ; Various Chemistry Research Topics ; Advanced Text Analysis Techniques
Authors: Ruoxun Fan | Beichen Xue | Pengfei Tian | Xuesong Zhang | Xiangzhou Yuan | Huiyan Zhang
Source: Chemical Communications (review, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sustainably transforming biomass into advanced carbon materials for solid-state supercapacitors: a review Biomass-derived carbon materials (BDCMs) are widely considered as promising and practical candidates for electrode materials of solid-state supercapacitors (SSCs), due to their low cost, good chemical and mechanical stabilities, excellent electrical conductivity, and high deployment feasibility. Numerous investigations have recently been conducted for sustainably transforming biomass into electrode materials with high electrochemical performance in SSCs, even guided by data-driven approaches. Therefore, this review addresses conventional and emerging synthesis routes for BDCM-based electrode materials and discusses recent advances in energy storage mechanisms and electrochemical performance enhancement of BDCMs for SSCs, improving electrode preparation and performance optimization of BDCMs in a practical and efficient manner. As two of the most powerful tools for novel material discovery and design, machine learning (ML) and 3D printing technologies are introduced to provide closed-loop guidelines for accurately and efficiently producing BDCMs with excellent electrochemical performance; main challenges for successfully applying ML and 3D printing methodologies are also addressed, providing critical guidelines for potential innovation and future development of BDCM-based SSCs. In this review, from life-cycle perspective, environmental benefits are assessed for BDCM-based SSCs, being highlighted as a promising and practical alternative to solidify energy security and achieve sustainable biomass management. The concluding remarks and future prospects are finally discussed to provide valuable insights for academic researchers and governmental policymakers. With concerted efforts, sustainably transforming biomass into high-performance electrode materials for SSCs is beneficial to achieving UN Sustainable Development Goals 7, 11-13. Supercapacitor ; Biomass (ecology) ; Carbon fibers ; Solid-state ; Materials science ; Sustainability ; Nanotechnology ; Chemical engineering ; Process engineering ; Environmental science ; Electrode ; Waste management ; Engineering physics ; Chemistry ; Electrochemistry ; Composite material ; Engineering ; Ecology Supercapacitor Materials and Fabrication ; Biodiesel Production and Applications ; Catalysis for Biomass Conversion
Authors: Mohamed Lamjed Bouazizi | Sobhi Hcini | K. Khirouni | Michel Boudard
Source: Optical Materials (article, 2024-02-19)
Field: Materials Science — Materials Chemistry

Sol-gel synthesis and property studies of Cu0.4Mg0.4Co0.2FeCrO4 spinel ferrite for optoelectronic, magnetic, and electrical applications Materials science ; Spinel ; Dielectric ; Electrical resistivity and conductivity ; Molar absorptivity ; Rietveld refinement ; Ferrite (magnet) ; Attenuation coefficient ; Absorption edge ; Band gap ; Refractive index ; Analytical Chemistry (journal) ; Optoelectronics ; Optics ; Chemistry ; Crystal structure ; Composite material ; Metallurgy ; Crystallography Magnetic Properties and Synthesis of Ferrites ; Copper-based nanomaterials and applications ; Multiferroics and related materials
Authors: Hongnian Long | Wenlong Liao | Rui Liu | Ruichi Zeng | Qihan Li | Lihua Zhao
Source: Nanomaterials (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Significantly Improve the Thermal Conductivity and Dielectric Performance of Epoxy Composite by Introducing Boron Nitride and POSS Dielectric materials with superb thermal and electrical properties are highly desired for high-voltage electrical equipment and advanced electronics. Here, we propose a novel strategy to improve the performance of epoxy composites by employing boron nitride nanosheets (BNNSs) and γ-glycidyl ether oxypropyl sesimoxane (G-POSS) as functional fillers. The resultant ternary epoxy composites exhibit high electrical resistivity (1.63 × 10<sup>13</sup> Ω·cm) and low dielectric loss (<0.01) due to the ultra-low dielectric constants of cage-structure of G-POSS. In addition, a high thermal conductivity of 0.3969 W·m<sup>-1</sup>·K<sup>-1</sup> is achieved for the epoxy composites, which is 114.66% higher than that of pure epoxy resin. This can be attributed to the high aspect ratio and excellent thermally conductive characteristics of BNNSs, promoting phonon propagation in the composites. Moreover, the epoxy composite simultaneously possesses remarkable dynamic mechanical properties and thermal stability. It is believed that this work provides a universal strategy for designing and fabricating multifunctional composites using a combination of different functional fillers. Epoxy ; Materials science ; Composite material ; Boron nitride ; Dielectric ; Composite number ; Thermal conductivity ; Thermal stability ; Dielectric loss ; Electrical conductor ; Electrical resistivity and conductivity ; Ternary operation ; Chemical engineering ; Optoelectronics ; Electrical engineering Thermal properties of materials ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Caroline Vibert | Anne‐Laurence Dupont | Justin Dirrenberger | Raphaël Passas | Denise Ricard | Bruno Fayolle
Source: Cellulose (article, 2024-01-05)
Field: Materials Science — Biomaterials

Relationship between chemical and mechanical degradation of aged paper: fibre versus fibre–fibre bonds Embrittlement ; Materials science ; Microstructure ; Degradation (telecommunications) ; Cellulose ; Composite material ; Hydrolysis ; Accelerated aging ; Chemistry ; Biochemistry Advanced Cellulose Research Studies ; Material Properties and Processing ; Cultural Heritage Materials Analysis
Authors: Bryce D. Shirk | Danielle L. Heichel | Lauren E. Eccles | L. Rodgers | Ali H. Lateef | Kelly A. Burke | Whitney L. Stoppel
Source: ACS Biomaterials Science & Engineering (review, 2024-09-11)
Field: Materials Science — Biomaterials

Modifying Naturally Occurring, Nonmammalian-Sourced Biopolymers for Biomedical Applications Natural biopolymers have a rich history, with many uses across the fields of healthcare and medicine, including formulations for wound dressings, surgical implants, tissue culture substrates, and drug delivery vehicles. Yet, synthetic-based materials have been more successful in translation due to precise control and regulation achievable during manufacturing. However, there is a renewed interest in natural biopolymers, which offer a diverse landscape of architecture, sustainable sourcing, functional groups, and properties that synthetic counterparts cannot fully replicate as processing and sourcing of these materials has improved. Proteins and polysaccharides derived from various sources (crustaceans, plants, insects, etc.) are highlighted in this review. We discuss the common types of polysaccharide and protein biopolymers used in healthcare and medicine, highlighting methods and strategies to alter structures and intra- and interchain interactions to engineer specific functions, products, or materials. We focus on biopolymers obtained from natural, nonmammalian sources, including silk fibroins, alginates, chitosans, chitins, mucins, keratins, and resilins, while discussing strategies to improve upon their innate properties and sourcing standardization to expand their clinical uses and relevance. Emphasis will be placed on methods that preserve the structural integrity and native biological functions of the biopolymers and their makers. We will conclude by discussing the untapped potential of new technologies to manipulate native biopolymers while controlling their secondary and tertiary structures, offering a perspective on advancing biopolymer utility in novel applications within biomedical engineering, advanced manufacturing, and tissue engineering. Nanotechnology ; Materials science ; Computational biology ; Biochemical engineering ; Biology ; Engineering Silk-based biomaterials and applications ; Nanofabrication and Lithography Techniques ; Advanced Materials and Mechanics
Authors: Yunxiao Zhang | Wenxin Lin | Yuanxiang Zhou | Weiwei Xing | Jiayu Cheng | Chenyuan Teng
Source: High Voltage (article, 2024-02-19)
Field: Materials Science — Materials Chemistry

Electrical tree degradation of MgO/epoxy resin composites at different voltage frequencies Abstract Electrical tree degradation is one of the main causes of insulation failure in high‐frequency transformers. Electrical tree degradation is studied on pure epoxy resin (EP) and MgO/EP composites at frequencies ranging from 50 Hz to 130 kHz. The results show that the tree initiation voltage of EP decreases, while the growth rate and the expansion coefficient increase with frequency. Moreover, the bubble phenomenon at high frequencies in EP composites is discussed. Combined with trap distribution characteristics within the material, the intrinsic mechanism of epoxy composites to inhibit the growth of the electrical tree at different frequencies is discussed. It can be concluded that more deep traps and blocking effect are introduced by doping nano‐MgO into EP bulks, which can improve the electrical tree resistance performance of EP composites in a wide frequency range. Epoxy ; Composite material ; Materials science ; Degradation (telecommunications) ; Voltage ; Electronic engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Smart Materials for Construction ; Electrical Fault Detection and Protection
Authors: Zakariya El‐Machachi | Damyan Frantzov | A. Nijamudheen | Tigany Zarrouk | A. Miguel | Volker L. Deringer
Source: Angewandte Chemie International Edition (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

Accelerated First‐Principles Exploration of Structure and Reactivity in Graphene Oxide Graphene oxide (GO) materials are widely studied, and yet their atomic-scale structures remain to be fully understood. Here we show that the chemical and configurational space of GO can be rapidly explored by advanced machine-learning methods, combining on-the-fly acceleration for first-principles molecular dynamics with message-passing neural-network potentials. The first step allows for the rapid sampling of chemical structures with very little prior knowledge required; the second step affords state-of-the-art accuracy and predictive power. We apply the method to the thermal reduction of GO, which we describe in a realistic (ten-nanometre scale) structural model. Our simulations are consistent with recent experimental findings, including X-ray photoelectron spectroscopy (XPS), and help to rationalise them in atomistic and mechanistic detail. More generally, our work provides a platform for routine, accurate, and predictive simulations of diverse carbonaceous materials. Graphene ; Oxide ; X-ray photoelectron spectroscopy ; Computer science ; Molecular dynamics ; On the fly ; Reactivity (psychology) ; Nanotechnology ; Acceleration ; Materials science ; Nanometre ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Muratahan Aykol | Amil Merchant | Simon Batzner | Jennifer N. Wei | Ekin D. Cubuk
Source: Nature Computational Science (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Predicting emergence of crystals from amorphous precursors with deep learning potentials Crystallization of amorphous precursors into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to the synthesis and development of new materials in the laboratory. Reliably predicting the outcome of such a process would enable new research directions in these areas, but has remained beyond the reach of molecular modeling or ab initio methods. Here we show that candidates for the crystallization products of amorphous precursors can be predicted in many inorganic systems by sampling the local structural motifs at the atomistic level using universal deep learning interatomic potentials. We show that this approach identifies, with high accuracy, the most likely crystal structures of the polymorphs that initially nucleate from amorphous precursors, across a diverse set of material systems, including polymorphic oxides, nitrides, carbides, fluorides, chlorides, chalcogenides and metal alloys. Metastability ; Amorphous solid ; Crystallization ; Nucleation ; Chemical physics ; Materials science ; Amorphous metal ; Ab initio ; Crystal (programming language) ; Nitride ; Interatomic potential ; Nanotechnology ; Molecular dynamics ; Computational chemistry ; Chemistry ; Crystallography ; Computer science ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Han Yu | Mingjing Deng | Ke Liu | Jia Chen | Yuting Wang | Yongsong Xu | Longyang Dian
Source: Chemistry - A European Journal (review, 2024-07-31)
Field: Materials Science — Materials Chemistry

Computer‐Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions Computer-aided synthesis planning (CASP) has garnered increasing attention in light of recent advancements in machine learning models. While the focus is on reverse synthesis or forward outcome prediction, optimizing reaction conditions remains a significant challenge. For datasets with multiple variables, the choice of descriptors and models is pivotal. This selection dictates the effective extraction of conditional features and the achievement of higher prediction accuracy. This review delineates the origins of data in conditional optimization, the criteria for descriptor selection, the response models, and the metrics for outcome evaluation, aiming to acquaint readers with the latest research trends and facilitate more informed research in this domain. CASP ; Computer science ; Machine learning ; Outcome (game theory) ; Artificial intelligence ; Selection (genetic algorithm) ; Computer-aided ; Model building ; Protein structure prediction ; Programming language ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Peng Yan | Mirko Fischer | Harrison Martin | Christian Wölke | Anand Narayanan Krishnamoorthy | Isidora Cekić-Lasković | Diddo Diddens | Martin Winter | Andreas Heuer
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Non-aqueous battery electrolytes: high-throughput experimentation and machine learning-aided optimization of ionic conductivity We study the ionic conductivity of a bi-salt (LiPF 6 , LiFSI) and ternary solvent (EC, EMC, PC) liquid battery electrolyte with high throughput experimentation and the open source Liquid Electrolyte Composition Analysis (LECA) Machine-Learning library. Electrolyte ; Ternary operation ; Ionic liquid ; Battery (electricity) ; Salt (chemistry) ; Conductivity ; Throughput ; Solvent ; Ionic conductivity ; Aqueous solution ; Materials science ; Chemical engineering ; Inorganic chemistry ; Computer science ; Chemistry ; Electrode ; Organic chemistry ; Engineering ; Catalysis ; Telecommunications ; Thermodynamics ; Physics ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Yuxian Cui | Jian Qu Zhu | Wei Zhou | Huaijuan Zang | Yongsheng Ren | Jiajia Xu | Shu Zhan | Wenhui Ma
Source: AIP Advances (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

SA-GNN: Prediction of material properties using graph neural network based on multi-head self-attention optimization With the development of science and technology and the improvement of hardware computing power, the application of large models in the field of artificial intelligence (AI) has become a current research hotspot Among the focal points in the field of deep learning, AI for science is one of the highlighted areas, utilizing deep learning methods for pattern recognition, anomaly detection, predictive analysis, and more on a large scale of scientific data. In the realm of materials science, the structure of crystals is composed of edges and nodes, making it readily representable as a graph. In previous research, some typical models, such as the MEGNet model, utilized their graph neural network features to fit computational results based on density functional theory for predicting various material properties. Building on this concept, the authors propose a novel graph neural network (GNN) model, optimized with a Multi-Head Self-Attention (MHSA) mechanism, for predicting materials data with crystal structures. This model is named self-attention enhanced graph neural network. The model segments the input data into three parts: edges, nodes, and global features. The graph convolutional layer module is primarily used for aggregating node, edge, and global features, learning node representations, and capturing higher-order neighborhood information through multiple layers of GNN. The MHSA component allows nodes to learn global dependencies, providing different representation subspaces for the nodes. In comparison with other machine learning and deep learning models, the results indicate an improvement in the predictive accuracy of this model. A new graph neural network (GNN) model called Self-Attention Enhanced Graph Neural Network (SA-GNN) is proposed for predicting the properties of materials with crystal structures. This model incorporates multi-head self-attention to allow nodes to learn global dependencies and generate different representational subspaces. Compared to other machine learning and deep learning models, the results show improved predictive accuracy, demonstrating the potential of graph networks combined with self-attention for modeling crystal material data. Computer science ; Artificial neural network ; Head (geology) ; Graph ; Artificial intelligence ; Theoretical computer science ; Geology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yufan Chen | Ching Ting Leung | Yong Huang | Jianwei Sun | Hao Chen | Hanyu Gao
Source: Journal of Cheminformatics (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

MolNexTR: a generalized deep learning model for molecular image recognition In the field of chemical structure recognition, the task of converting molecular images into machine-readable data formats such as SMILES string stands as a significant challenge, primarily due to the varied drawing styles and conventions prevalent in chemical literature. To bridge this gap, we proposed MolNexTR, a novel image-to-graph deep learning model that collaborates to fuse the strengths of ConvNext, a powerful Convolutional Neural Network variant, and Vision-TRansformer. This integration facilitates a more detailed extraction of both local and global features from molecular images. MolNexTR can predict atoms and bonds simultaneously and understand their layout rules. It also excels at flexibly integrating symbolic chemistry principles to discern chirality and decipher abbreviated structures. We further incorporate a series of advanced algorithms, including an improved data augmentation module, an image contamination module, and a post-processing module for getting the final SMILES output. These modules cooperate to enhance the model's robustness to diverse styles of molecular images found in real literature. In our test sets, MolNexTR has demonstrated superior performance, achieving an accuracy rate of 81-97%, marking a significant advancement in the domain of molecular structure recognition.Scientific contributionMolNexTR is a novel image-to-graph model that incorporates a unique dual-stream encoder to extract complex molecular image features, and combines chemical rules to predict atoms and bonds while understanding atom and bond layout rules. In addition, it employs a series of novel augmentation algorithms to significantly enhance the robustness and performance of the model. Computer science ; Deep learning ; Artificial intelligence ; Image (mathematics) ; Pattern recognition (psychology) ; Data science ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques