jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhixiang Cheng | Hongxin Xiang | Pengsen Ma | Li Zeng | Xin Jin | Xixi Yang | Jianxin Lin | Yang Deng | Bosheng Song | Xinxin Feng | Changhui Deng | Xiangxiang Zeng
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-09-09)
Field: Materials Science — Materials Chemistry

MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs with Pixel Masking Abstract Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them. Our research indicates that as molecular similarity increases, graph-based methods struggle to capture these nuances, whereas imagebased approaches effectively retain the distinctions. Thus, we developed MaskMol, a knowledge-guided molecular image selfsupervised learning framework. MaskMol accurately learns the representation of molecular images by considering multiple levels of molecular knowledge, such as atoms, bonds, and substructures. By utilizing pixel masking tasks, MaskMol extracts fine-grained information from molecular images, overcoming the limitations of existing deep learning models in identifying subtle structural changes. Experimental results demonstrate MaskMol’s high accuracy and transferability in activity cliff estimation and compound potency prediction across 20 different macromolecular targets, outperforming 25 state-of-the-art deep learning and machine learning approaches. Visualization analyses reveal MaskMol’s high biological interpretability in identifying activity cliff-relevant molecular substructures. Notably, through MaskMol, we identified candidate EP4 inhibitors that could be used to treat tumors. This study not only raises awareness about activity cliffs but also introduces a novel method for molecular image representation learning and virtual screening, advancing drug discovery and providing new insights into structure-activity relationships (SAR). Code is available at https://github.com/ZhixiangCheng/MaskMol . Masking (illustration) ; Training (meteorology) ; Pixel ; Image (mathematics) ; Computer science ; Artificial intelligence ; Computer vision ; Geography ; Art ; Visual arts Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: Markus Orsi | Hippolyte Personne | Etienne Bonvin | Thierry Paschoud | Başak Olcay | Xiao Hu | Sacha Javor | Jean‐Louis Reymond
Source: CHIMIA International Journal for Chemistry (review, 2024-10-30)
Field: Chemistry — Organic Chemistry

Chemical Space for Peptide-based Antimicrobials Multidrug-resistant (MDR) bacteria represent a global public health threat, and antimicrobial peptides (AMPs), derived from naturally occurring linear or cyclic peptides, can provide the solution. However, most AMPs are sensitive to proteases and have poor pharmacokinetics. The EU-funded ERC Advanced Grant SPACE4AMPS aims to identify new AMPs by applying the concepts of chemical space and ligand-based virtual screening, which are well known for small molecule drug discovery, to the world of peptides. We create virtual libraries of peptides and related molecules and use these approaches to select a few tens of compounds for synthesis and detailed. evaluation of antibacterial, toxicity and stability effects. Recent results and prospects of this ongoing project are presented in this review. Virtual screening ; Antimicrobial peptides ; Chemical space ; Combinatorial chemistry ; Peptide ; Antibacterial peptide ; Computational biology ; Antimicrobial ; Chemistry ; Drug discovery ; Proteases ; Nanotechnology ; Bacteria ; Biochemistry ; Biology ; Antibacterial activity ; Materials science ; Organic chemistry ; Genetics Click Chemistry and Applications ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Seul Lee | Karsten Kreis | Srimukh Prasad Veccham | Meng Liu | Danny Reidenbach | Saee Paliwal | Arash Vahdat | Weili Nie
Source: arXiv (Cornell University) (preprint, 2024-11-18)
Field: Chemistry — Spectroscopy

Molecule Generation with Fragment Retrieval Augmentation Fragment-based drug discovery, in which molecular fragments are assembled into new molecules with desirable biochemical properties, has achieved great success. However, many fragment-based molecule generation methods show limited exploration beyond the existing fragments in the database as they only reassemble or slightly modify the given ones. To tackle this problem, we propose a new fragment-based molecule generation framework with retrieval augmentation, namely Fragment Retrieval-Augmented Generation (f-RAG). f-RAG is based on a pre-trained molecular generative model that proposes additional fragments from input fragments to complete and generate a new molecule. Given a fragment vocabulary, f-RAG retrieves two types of fragments: (1) hard fragments, which serve as building blocks that will be explicitly included in the newly generated molecule, and (2) soft fragments, which serve as reference to guide the generation of new fragments through a trainable fragment injection module. To extrapolate beyond the existing fragments, f-RAG updates the fragment vocabulary with generated fragments via an iterative refinement process which is further enhanced with post-hoc genetic fragment modification. f-RAG can achieve an improved exploration-exploitation trade-off by maintaining a pool of fragments and expanding it with novel and high-quality fragments through a strong generative prior. Fragment (logic) ; Information retrieval ; Computer science ; Algorithm Analytical Chemistry and Chromatography ; Various Chemistry Research Topics ; Mass Spectrometry Techniques and Applications
Authors: Matt Stallone | Vaibhav Saxena | Leonid Karlinsky | Bridget McGinn | Tim Bula | Mayank Mishra | Adriana Meza Soria | Gaoyuan Zhang | Aditya Prasad | Yikang Shen | Saptha Surendran | Shanmukha Guttula | Hima Patel | Parameswaran Selvam | Xuan-Hong Dang | Yan Koyfman | Atin Sood | Rogério Feris | Nirmit Desai | David Cox | Ruchir Puri | Rameswar Panda
Source: arXiv (Cornell University) (preprint, 2024-07-18)
Field: Materials Science — Materials Chemistry

Scaling Granite Code Models to 128K Context This paper introduces long-context Granite code models that support effective context windows of up to 128K tokens. Our solution for scaling context length of Granite 3B/8B code models from 2K/4K to 128K consists of a light-weight continual pretraining by gradually increasing its RoPE base frequency with repository-level file packing and length-upsampled long-context data. Additionally, we also release instruction-tuned models with long-context support which are derived by further finetuning the long context base models on a mix of permissively licensed short and long-context instruction-response pairs. While comparing to the original short-context Granite code models, our long-context models achieve significant improvements on long-context tasks without any noticeable performance degradation on regular code completion benchmarks (e.g., HumanEval). We release all our long-context Granite code models under an Apache 2.0 license for both research and commercial use. Context (archaeology) ; Code (set theory) ; Scaling ; Computer science ; Geology ; Mathematics ; Programming language ; Paleontology ; Geometry High-Velocity Impact and Material Behavior ; Advanced Surface Polishing Techniques ; Metallurgy and Material Forming
Authors: Fedor S. Avilov | Roman A. Eremin | Semen Budennyy | Innokentiy S. Humonen
Source: arXiv (Cornell University) (preprint, 2024-10-02)
Field: Materials Science — Materials Chemistry

On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants Despite an artificial intelligence-assisted modeling of disordered crystals is a widely used and well-tried method of new materials design, the issues of its robustness, reliability, and stability are still not resolved and even not discussed enough. To highlight it, in this work we composed a series of nested intermetallic approximants of quasicrystals datasets and trained various machine learning models on them correspondingly. Our qualitative and, what is more important, quantitative assessment of the difference in the predictions clearly shows that different reasonable changes in the training sample can lead to the completely different set of the predicted potentially new materials. We also showed the advantage of pre-training and proposed a simple yet effective trick of sequential training to increase stability. Quasicrystal ; Robustness (evolution) ; Statistical physics ; Computer science ; Mathematics ; Physics ; Geometry ; Chemistry Quasicrystal Structures and Properties ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Bin Cao | Yang Liu | Zinan Zheng | Ruifeng Tan | Jia Li | Tong‐Yi Zhang
Source: arXiv (Cornell University) (preprint, 2024-06-15)
Field: Materials Science — Materials Chemistry

SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification Spectroscopic data, particularly diffraction data, contain detailed crystal and microstructure information and thus are crucial for materials discovery. Powder X-ray diffraction (XRD) patterns are greatly effective in identifying crystals. Although machine learning (ML) has significantly advanced the analysis of powder XRD patterns, the progress is hindered by a lack of training data. To address this, we introduce SimXRD, the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations. We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD. The results indicate that existing neural networks struggle with low-frequency crystal classifications. The present work highlights the academic significance and the engineering novelty of simulated XRD patterns in this interdisciplinary field. Symmetry (geometry) ; Diffraction ; Physics ; Optics ; Materials science ; Geometry ; Mathematics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Enzyme Structure and Function
Authors: Yuqi Zhou | Sunhao Dai | Zhenhua Cao | Xiao Zhang | Jun Xu
Source: arXiv (Cornell University) (preprint, 2024-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Length-Induced Embedding Collapse in PLM-based Models Text embeddings from PLM-based models enable a wide range of applications, yet their performance often degrades on longer texts. In this paper, we introduce a phenomenon we call Length Collapse, where embeddings of longer texts tend to cluster together. This clustering results in a distributional inconsistency between the embeddings of short and long texts. We further investigate how these differences contribute to the performance decline observed with longer texts across various downstream tasks. Through a rigorous theoretical analysis of the self-attention mechanism, which acts as a low-pass filter in PLM-based models, we demonstrate that as text length increases, the strength of low-pass filtering intensifies, causing embeddings to retain more low-frequency components. As a result, input token features become more similar, leading to clustering and ultimately the collapse of embeddings for longer texts. To address this issue, we propose a simple method, TempScale, which mitigates the Length Collapse phenomenon. By narrowing the gap in low-pass filtering rates between long and short texts, TempScale ensures more consistent embeddings across different text lengths. This approach leads to performance improvements of 0.94% on MTEB and 1.10% on LongEmbed, which focuses specifically on long-context retrieval, providing strong evidence for the validity of our analysis. The source code is available at https://github.com/Yuqi-Zhou/Length_Collapse. Transformer ; Embedding ; Computer science ; Engineering ; Electrical engineering ; Artificial intelligence ; Voltage Magnetic Properties and Applications
Authors: Fatima Mohammad Kabir | A. U. Uduma | Maria B. Uduma
Source: Journal of Science Research and Reviews (article, 2024-12-31)
Field: Chemistry — Spectroscopy

An Overview of the Chemistry and Utilization of Detergents, both Soap and Non-Soap This analysis explores the fundamental contrasts between detergent soaps and non-soap detergents, concentrating on their chemical makeup, cleaning methods, environmental impact, and applicability for diverse uses. Traditional soaps are made from natural fats and oils, which results in a biodegradable cleaning agent. Non-soap detergents, on the other hand, are made synthetically and frequently have better cleaning capabilities, but their limited biodegradability may cause environmental issues. In order to determine the best option depending on particular cleaning requirements, this study examines the trade-offs between these two cleaning chemicals, taking into account variables including water hardness, skin sensitivity, and sustainability consequences. According to this overview, the dynamic development, formulation, and use of soaps and soapless detergents are primarily driven by chemistry and chemical principles. The aim of this study therefore, is to conduct extensive survey on the chemistry and utilization of detergents, both soap and non-soap. SOAP ; Chemistry ; Soap bubble ; Nanotechnology ; World Wide Web ; Computer science ; Materials science ; Physics Analytical Chemistry and Chromatography ; Chemistry and Chemical Engineering ; Biopolymer Synthesis and Applications
Authors: Shijie Rao | Kaiyu Cui | Yidong Huang | Jiawei Yang | Yali Li | Shengjin Wang | Xue Feng | Fang Liu | Wei Zhang
Source: arXiv (Cornell University) (preprint, 2024-04-09)
Field: Materials Science — Surfaces, Coatings and Films

Map Optical Properties to Subwavelength Structures Directly via a Diffusion Model Subwavelength photonic structures and metamaterials provide revolutionary approaches for controlling light. The inverse design methods proposed for these subwavelength structures are vital to the development of new photonic devices. However, most of the existing inverse design methods cannot realize direct mapping from optical properties to photonic structures but instead rely on forward simulation methods to perform iterative optimization. In this work, we exploit the powerful generative abilities of artificial intelligence (AI) and propose a practical inverse design method based on latent diffusion models. Our method maps directly the optical properties to structures without the requirement of forward simulation and iterative optimization. Here, the given optical properties can work as "prompts" and guide the constructed model to correctly "draw" the required photonic structures. Experiments show that our direct mapping-based inverse design method can generate subwavelength photonic structures at high fidelity while following the given optical properties. This may change the method used for optical design and greatly accelerate the research on new photonic devices. Diffusion ; Materials science ; Optics ; Physics Optical Coatings and Gratings
Authors: Xuhan Huang | Qingning Shen | Yan Hu | Anningzhe Gao | Benyou Wang
Source: arXiv (Cornell University) (preprint, 2024-05-21)
Field: Materials Science — Materials Chemistry

LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages Large Language Models (LLMs) have demonstrated strong performance across various natural language processing tasks, yet their proficiency in mathematical reasoning remains a key challenge. Addressing the gap between natural and mathematical language requires advanced reasoning capabilities, approaching those of Artificial General Intelligence (AGI). However, the evaluation remains challenging, as perfectly representing reality is inherently elusive, and traditional methods like manual or direct comparison of mathematical statements (Ramamonjison et al., 2023) are insufficient for assessing true modeling ability. We propose a process-oriented framework to evaluate LLMs' ability to construct mathematical models, using solvers to compare outputs with ground truth. Introducing Mamo, a benchmark with 1,209 questions covering ordinary differential equations, linear programming, and mixed-integer linear programming, we enable automatic evaluation of modeling accuracy. The results show that existing LLMs struggle with complex mathematical modeling tasks, with larger models demonstrating superior performance, while open-source models remain competitive in simpler cases but still fall short of proprietary models in more challenging problems. Benchmark (surveying) ; Computer science ; Geography ; Cartography Machine Learning in Materials Science ; Topic Modeling ; Multimodal Machine Learning Applications
Authors: Ganesan | Dhivakar J Manoj | Stefan Kornhuber | R. Sarathi | M. G. Danikas
Source: Journal of Engineering Science and Technology Review (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Investigation on the Role of Interface on Electrical, Thermal and Mechanical Properties of Silicone Rubber-BN NanocompositesK The current study investigates the impact of interfacial interactions between the silicone rubber base polymer matrix and the hexagonal boron nitride (hBN) nanofiller, incorporated at different weight percentages, on electrical, thermal, and mechanical properties.The adhesion of filler to the polymer is examined through swelling tests and Kraus plots and the elastic modulus calculated has a good agreement with the experimental data.The addition of 3 wt% of BN nanofiller with silicone rubber led to a 25.4 % increment in surface discharge inception voltage (SDIV).The Thermogravimetric analysis (TGA) shows that the BN filler included samples have high degradation temperature and thermal activation energy.The introduction of filler into the base polymer matrix significantly enhances mechanical properties, specifically tensile strength, tensile modulus, and percentage elongation at break. Silicone rubber ; Materials science ; Composite material ; Interface (matter) ; Thermal grease ; Natural rubber ; Silicone ; Thermal ; Polymer science ; Thermal conductivity ; Physics Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Smart Materials for Construction
Authors: Mahmoud A. Elsadd | R. A. Elsad | Maryam Al Huwayz | Shehab A. Mansour | Mohamed S. Zaky | Nagy I. Elkalashy | Mohamed A. Izzularab
Source: Scientific Reports (article, 2024-10-28)
Field: Materials Science — Materials Chemistry

Improving the distribution system capability by incorporating ZnO nanoparticles into high-density polyethylene cable materials This paper introduces a novel insulated cable designed to enhance the distribution system's capabilities. Accordingly, high-density polyethylene loaded with varying concentrations of zinc oxide (ZnO) nanoparticles (NPs) ranging from 0.0 to 5 wt% was prepared using the melt-blending technique. Zinc oxide (NPs) is synthesized by using sol-gel technique and their microstructure was examined by X-ray diffraction. The new insulated cable, HDPE nanocomposite loaded with 1 wt% of ZnO, demonstrates a 43% reduction in the relative dielectric constant and a 16.5% improvement in breakdown strength compared to pure HDPE. The observed changes in both the dielectric constant and breakdown strength offer several advantages in electrical applications. These benefits include a decrease in feeder current at the same loading level, mitigation of inrush transients during load switching, and a reduction in earth fault current values, particularly in unearthed distribution networks with ungrounded cables. A comparative study is conducted between the conventional insulating cable based on the original material (HDPE) and the new insulated cable incorporating ZnO nanomaterial at a ratio of 1.0 wt% of the total cable mass per unit length. This comparison utilizes data from two actual medium-voltage distribution feeders. Both actual feeders are simulated using the EMTP/ATP package. The obtained results prove the efficacy of the developed material cable (polymer doped with ZnO NPs) compared to the base material. The peak and duration of inrush current can be cut to 77.3% and 67% of their original values, respectively. The earth fault current can be reduced to 56.5% in ungrounded networks, while substation current under the normal operation can be cut to 84.3% with the same load currents. Nanoparticle ; Materials science ; Polyethylene ; Computer science ; Nanotechnology ; Composite material High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Aerosol Filtration and Electrostatic Precipitation
Authors: Eko Waluyo | Happy Nursyam | Bayu Mitra A. Kusuma | Luthfia Hanun Yuli Arini | Devi Veda Amanda
Source: International Journal of Biochemistry Research & Review (article, 2024-12-17)
Field: Chemistry — Organic Chemistry

Natural Deep Eutectic Solvents (NADES) Research Framework: Bibliometric Analysis Aims: The Natural Deep Eutectic Solvents (NADES) method is an extraction method currently being developed using affordable green solvents with low cytotoxicity. This research aims to (a) identify developments in NADES research, and (b) develop research gaps for NADES for future research. Study Design: This research was conducted using bibliometric analysis. Place and Duration of Study: Library sources refer to research articles available online that can be accessed via websites or search engines by entering specific keywords related to the topic. Methodology: A research approach using quantitative methods was carried out using bibliometric analysis. Data analysis using bibliometric analysis of “Natural Deep Eutectic Solvents” sourced by article tittle, abstract, and keywords that produced 1,045 Scopus documents. Results: The results show a growing research trend in NADES, but there are still several gaps that need to be addressed as mentioned in the proposed research framework. Conclusion: Future research should focus on more comprehensive comparisons, more detailed method explanations, deeper understanding of molecular mechanisms, and long-term stability evaluations to improve the applicability of NADES in various fields. This paper contributes to the development of NADES. Natural (archaeology) ; Eutectic system ; Bibliometrics ; Biochemical engineering ; Chemistry ; Computer science ; Engineering ; History ; Library science ; Organic chemistry ; Archaeology Inorganic and Organometallic Chemistry
Authors: Johann Brehmer | Sönke Behrends | Pim de Haan | Taco Cohen
Source: arXiv (Cornell University) (preprint, 2024-10-30)
Field: Materials Science — Materials Chemistry

Does equivariance matter at scale? Given large datasets and sufficient compute, is it beneficial to design neural architectures for the structure and symmetries of each problem? Or is it more efficient to learn them from data? We study empirically how equivariant and non-equivariant networks scale with compute and training samples. Focusing on a benchmark problem of rigid-body interactions and on general-purpose transformer architectures, we perform a series of experiments, varying the model size, training steps, and dataset size. We find evidence for three conclusions. First, equivariance improves data efficiency, but training non-equivariant models with data augmentation can close this gap given sufficient epochs. Second, scaling with compute follows a power law, with equivariant models outperforming non-equivariant ones at each tested compute budget. Finally, the optimal allocation of a compute budget onto model size and training duration differs between equivariant and non-equivariant models. Scale (ratio) ; Mathematics ; Geography ; Cartography Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks