jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eliana Marcela Mayorga Chaves
Source: Repositorio Digital Universidad ECCI (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Modeling Critical Properties of Substances using Neural Networks and Molecular Structure Este estudio se centra en la aplicación de redes neuronales en ingeniería química para la estimación precisa de propiedades críticas, como la presión y la temperatura crítica, fundamentales en el diseño de plantas químicas. En Colombia, la industria química enfrenta un desafío importante debido a la falta de avances tecnológicos en el diseño de equipos para sectores clave como agricultura, alimentos, farmacéutica y petroquímica. Actualmente, el diseño de equipos se basa en modelos empíricos o determinísticos que, carecen de la información necesaria para realizar estimaciones precisas de las variables pertinentes. Ante esta problemática, se propone el desarrollo de una red neuronal que se enfoca en la temperatura crítica como parámetro principal. Esta red utiliza matrices de distancia y conectividad para representar la información de la estructura molecular de las sustancias. Adicionalmente, usa descriptores como peso molecular y electronegatividad. Los resultados conllevan a estimaciones de propiedades críticas, especialmente para sustancias orgánicas, partiendo de matrices de conectividad. Este enfoque promete contribuir al avance tecnológico en la industria química colombiana al proporcionar herramientas más precisas y eficientes para el diseño de procesos y equipos. Además, abre nuevas oportunidades para investigaciones futuras en el campo de la química computacional y la inteligencia artificial aplicada a la ingeniería química con el fin de satisfacer las necesidades industriales y sociales. Network structure ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Ali Ramlaoui | Théo Saulus | Basile Terver | Victor Schmidt | David Rolnick | Fragkiskos D. Malliaros | Alexandre Duval
Source: arXiv (Cornell University) (preprint, 2024-07-11)
Field: Materials Science — Materials Chemistry

Improving Molecular Modeling with Geometric GNNs: an Empirical Study Rapid advancements in machine learning (ML) are transforming materials science by significantly speeding up material property calculations. However, the proliferation of ML approaches has made it challenging for scientists to keep up with the most promising techniques. This paper presents an empirical study on Geometric Graph Neural Networks for 3D atomic systems, focusing on the impact of different (1) canonicalization methods, (2) graph creation strategies, and (3) auxiliary tasks, on performance, scalability and symmetry enforcement. Our findings and insights aim to guide researchers in selecting optimal modeling components for molecular modeling tasks. Computer science ; Empirical research ; Econometrics ; Mathematics ; Statistics Machine Learning in Materials Science
Authors: Ashley Lenau
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Advantages and Limitations of using Physics Informed Deep Learning to Model Crystal Plasticity Crystal plasticity ; Plasticity ; Computer science ; Psychology ; Artificial intelligence ; Statistical physics ; Cognitive science ; Theoretical physics ; Data science ; Mathematics education ; Physics ; Thermodynamics Machine Learning in Materials Science
Authors: Tomoki Kobayashi | Shuma Tanaka | Kazuyoshi Ogasawara
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Prediction of <sup>1</sup>E<sub>g </sub>energies in Ni<sup>2+</sup> in Crystals Based on Racah Parameters B and C By First-Principles Calculations and Machine Learning Recently, demand of infrared phosphors is increasing because they can be used not only for lighting applications such as night vision camera and plant growth but also in various fields such as biomarkers that emit light by binding to abnormal sites in living organisms. Since the Ni 2+ -doped phosphors are drawing attentions as potential infrared phosphors, analysis of multiplet energies and emission mechanism of Ni 2+ in crystals is very important for the development of novel infrared phosphors. For Mn 4+ , Cr 3+ and Ni 2+ in crystals, Brik et al. analyzed a large amount of experimental data in literature and indicated the importance of the Racah parameter C in addition to B and proposed a new Nephelauxetic parameter. Considering their results, we have recently created prediction models of 2 E g levels of Mn 4+ and Cr 3+ in oxides and fluorides based on the Racah parameters B and C using machine learning. For theoretical design of infrared phosphors, a similar prediction model for 1 E g level of Ni 2+ would be useful. According to the Tanabe-Sugano diagram for the d 8 configuration, the energy of 1 E g is almost independent of Δ/B which is a measure of the strength of the ligand field normalized by the Racah parameter B. Therefore, the energy of 1 E g seems to be determined by the Racah parameter B. However, as pointed out by Brik et al., in actual experimental values in literature, there are Ni 2+ -doped crystals which have different 1 E g energies despite having almost the same B values, indicating the necessity of consideration of the Racah parameter C as well. In this work, we created systematic model clusters for Ni 2+ ions in crystals and performed first-principles calculations of the multiplet energies using the DV-Xα and DVME methods. Using these systematic data as the training data, predictive models of 1 E g based on the Racah parameters B and C were created by machine learning and the influence of these parameters on the multiplet energies was investigated in detail. Atomic physics ; Physics ; Nuclear physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Sasan Amariamir | Janine George | Philipp Benner
Source: arXiv (Cornell University) (preprint, 2024-11-18)
Field: Materials Science — Materials Chemistry

SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in realizing novel materials, remains a complex challenge due to the limitations of traditional heuristics and thermodynamic proxies. While stability metrics such as formation energy offer partial insights, they fail to account for kinetic factors and technological constraints that influence synthesis outcomes. These challenges are further compounded by the scarcity of negative data, as failed synthesis attempts are often unpublished or context-specific. We present SynCoTrain, a semi-supervised machine learning model designed to predict the synthesizability of materials. SynCoTrain employs a co-training framework leveraging two complementary graph convolutional neural networks: SchNet and ALIGNN. By iteratively exchanging predictions between classifiers, SynCoTrain mitigates model bias and enhances generalizability. Our approach uses Positive and Unlabeled (PU) Learning to address the absence of explicit negative data, iteratively refining predictions through collaborative learning. The model demonstrates robust performance, achieving high recall on internal and leave-out test sets. By focusing on oxide crystals, a well-characterized material family with extensive experimental data, we establish SynCoTrain as a reliable tool for predicting synthesizability while balancing dataset variability and computational efficiency. This work highlights the potential of co-training to advance high-throughput materials discovery and generative research, offering a scalable solution to the challenge of synthesizability prediction. Classifier (UML) ; Artificial intelligence ; Computer science ; Machine learning ; Dual (grammatical number) ; Philosophy Machine Learning in Materials Science ; Chemical Synthesis and Analysis
Authors: Muhammad Saqib | Sana Farooq | Mohamed Mohamed Soliman | Shahida Bashir | Mudassir Hussain Tahir | Hamdy Khamees Thabet | Mohamed Kallel
Source: Journal of Solid State Chemistry (article, 2024-10-18)
Field: Materials Science — Polymers and Plastics

Data-driven designing of organic electrode materials for batteries and property prediction Property (philosophy) ; Electrode ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Physical chemistry Conducting polymers and applications ; Advanced Battery Technologies Research ; Machine Learning in Materials Science
Authors: Emmanuel Arinze | Alum Benedict Nnachi
Source: IDOSR JOURNAL OF SCIENCE AND TECHNOLOGY (article, 2024-11-07)
Field: Chemistry — Physical and Theoretical Chemistry

Challenges in Mathematics Arising from Theoretical and Computational Chemistry The combination of theoretical chemistry and mathematics represents a significant advancement in science since it has yielded insights into the enigmas surrounding atoms and molecules. The advancement of technology has greatly benefited drug development, leading to significant advancements in both medicine and materials research. This summary discusses innovative methodologies and computational challenges in the expansive domain of theoretical and computational chemistry. Despite the computational challenges associated with the Schrodinger equation and Density Functional Theory (DFT), quantum mechanics offers a basic understanding of the behaviour of molecules and atoms. While MD simulations may not handle long-range interactions and complex configuration spaces, they nonetheless excel at capturing various time scales of molecular behaviour. Nevertheless, the process of transforming abstract notions into computer models for the purpose of solving MD simulations continues to be difficult; statistical mechanics offers a theoretical framework for understanding MD simulations. Data-driven methodologies and machine learning have significantly transformed the perspective of computational chemistry by enabling the observation and comprehension of very complex chemical interactions. We must also address concerns about interpretability, reliability in limited data streams, and system transferability. These challenges expedite the advancement of novel materials, product designs, and pharmaceuticals by leveraging machine learning and integrating it with specialised domain expertise. We used available data from many reputable databases, spanning the time period from 2010 to 2024. It is imperative to prioritise the advancement of multiscale modelling techniques and hybrid quantum-classical tools in order to achieve sufficient mastery over chemical phenomena. Another potential benefit is the use of novel mathematical models to uncover previously unknown patterns and connections in genetic data. The most effective way to tackle mathematical problems is by employing a straightforward approach and engaging in collaboration with experts from other fields. By adopting this approach, we can effectively address significant societal issues and propel advancements in chemistry. Keywords: mathematics, computational, chemistry, theoretical, challenges, quantum mechanics Mathematics education ; Computer science ; Chemistry ; Mathematics History and advancements in chemistry
Authors: Shi‐Zhang Qiao | Haobo Li | Xinyu Li | Pengtang Wang | Zhen Zhang | Kenneth Davey | Qinfeng Shi
Source: Research Square (preprint, 2024-04-03)
Field: Materials Science — Materials Chemistry

Establishing C-C electro-coupling mechanism via big data analyses of reaction networks Bimetallic strip ; Coupling (piping) ; Catalysis ; Selectivity ; Big data ; Coupling reaction ; Mechanism (biology) ; Chemistry ; Computer science ; Reaction mechanism ; Computational chemistry ; Materials science ; Physics ; Data mining ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Yujian Shen | Zhongjie Zhu | Qingjie Xiao | Kanglei Ye | Qisheng Wang | Yue Wang | Bo Sun
Source: Crystals (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Predicting X-ray Diffraction Quality of Protein Crystals Using a Deep-Learning Method Over the past few decades, significant advancements in protein crystallography have led to a steady increase in the number of determined protein structures. The X-ray diffraction experiment remains one of the primary methods for investigating protein crystal structures. To obtain information about crystal structures, a sufficient number of high-quality crystals are typically required. At present, X-ray diffraction experiments on protein crystals primarily rely on manual selection by experimenters. However, each experiment is not only costly but also time-consuming. To address the urgent need for automatic selection of the proper protein crystal candidates for X-ray diffraction experiments, a protein-crystal-quality classification network, leveraging the ConvNeXt network architecture, is proposed. Subsequently, a new database is created, which includes protein crystal images and their corresponding X-ray diffraction images. Additionally, a novel method for categorizing protein quality based on the number of diffraction spots and the resolution is introduced. To further enhance the network’s focus on essential features of protein crystal images, a CBAM (Convolutional Block Attention Module) attention mechanism is incorporated between convolution layers. The experimental results demonstrate that the network achieves significant improvement in performing the prediction task, thereby effectively enhancing the probability of high-quality crystals being selected by experimenters. Protein crystallization ; Diffraction ; Crystal (programming language) ; Computer science ; Quality (philosophy) ; X-ray crystallography ; Convolution (computer science) ; Selection (genetic algorithm) ; Crystallography ; Artificial intelligence ; Materials science ; Optics ; Physics ; Chemistry ; Crystallization ; Artificial neural network Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Yifeng Tian | Soumendu Bagchi | Liam Myhill | Giacomo Po | Enrique Martínez | Yen Ting Lin | Nithin Mathew | Danny Pérez
Source: Research Square (preprint, 2024-04-12)
Field: Materials Science — Materials Chemistry

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning Dislocation ; Computer science ; Physics ; Artificial intelligence ; Machine learning ; Statistical physics ; Condensed matter physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Maryam Astero | Juho Rousu
Source: Research Square (preprint, 2024-12-20)
Field: Materials Science — Materials Chemistry

Enhancing Atom Mapping with Multitask Learning and Symmetry-Aware Deep Graph Matching Matching (statistics) ; Graph ; Computer science ; Symmetry (geometry) ; Deep learning ; Multi-task learning ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Task (project management) ; Engineering ; Statistics ; Geometry Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Radioactive element chemistry and processing
Authors: Han-Chung Chang | Ming‐Hsuan Tsai | Yi‐Pei Li
Source: ChemRxiv (preprint, 2024-12-12)
Field: Materials Science — Materials Chemistry

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches—transfer learning, delta learning, and feature engineering—to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data. Using the Chemprop model, we systematically evaluated how these methods leverage data from semiempirical quantum mechanical (SQM) calculations to improve predictions. Delta learning, which adjusts low-level SQM activation energies to align with high-level CCSD(T)-F12a targets, emerged as the most effective method, achieving high accuracy with substantially reduced high-level data requirements. Notably, delta learning trained with just 20%–30% of high-level data matched or exceeded the performance of other methods trained with full datasets, making it advantageous in data-scarce scenarios. However, its reliance on transition state searches imposes significant computational demands during model application. Transfer learning, which pretrains models on large datasets of low-level data, provided mixed results, particularly when there was a mismatch in the reaction distributions between the training and target datasets. Feature engineering, which involves adding computed molecular properties as input features, showed modest gains, particularly when incorporating thermodynamic properties. Our study highlights the trade-offs between accuracy and computational demand in selecting the best approach for enhancing activation energy predictions. These insights provide valuable guidelines for researchers aiming to apply machine learning in chemical reaction engineering, helping to balance accuracy with resource constraints. Leverage (statistics) ; Computer science ; Artificial neural network ; Feature engineering ; Artificial intelligence ; Machine learning ; Transfer of learning ; Graph ; Scarcity ; Deep learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Ali Khoshsima | Amir Hossein Sheikhshoaei | Davood Zabihzadeh
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Predicting the Heat Capacity of Strontium-Praseodymium Oxysilicate Srpr4(Sio4)3o Using Machine Learning, Deep Learning, and Hybrid Models Praseodymium ; Strontium ; Deep learning ; Artificial intelligence ; Computer science ; Materials science ; Chemistry ; Metallurgy Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure ; Machine Learning in Materials Science
Authors: Raghvender Raghvender | Mortier Fabien | Shiab Firas | Cadars Sylvian | Thomas Philippe | Masson Olivier | Bernard Samuel | Assil Bouzid
Source: SPIRE - Sciences Po Institutional REpository (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

First-principles and machine learning modelling in materials science: An overview and practical examples International audience Key (lock) ; Feature (linguistics) ; Process (computing) ; Work (physics) ; Active learning (machine learning) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Ali Mesbah | Ketong Shao | Aditya Lele | Zhiyu Shi | Victor A. Miller | Yiguang Ju
Source: Research Square (preprint, 2024-06-25)
Field: Materials Science — Materials Chemistry

Interpretable Attention-based Transfer Learning in Plasma Catalysis: A Study on the Role of Surface Charge <title>Abstract</title> Low-temperature plasma catalysis holds promise for electrification of energy-intensive chemical processes such as methane reforming and ammonia synthesis. However, fundamental understanding of plasma-catalyst interactions, essential for catalyst design and screening for plasma catalysts, remains largely limited. Recent work has demonstrated the importance of first-principles studies, including density functional theory (DFT), for elucidating the role of electro- and photo-effects such as electric field and charge in plasma catalysis. The availability of increasing amounts of DFT data in thermal catalysis presents a unique opportunity for plasma catalysis research to efficiently leverage this existing first-principles knowledge of thermal catalysis towards investigating plasma-catalyst interactions. To this end, this paper investigates interpretable transfer learning from thermal to plasma catalysis, with a focus on the role of surface charge. Pre-trained attention-based graph neural networks (GNNs) from the Open Catalysis Project, trained using millions of thermal catalysis DFT data points, are structurally adapted to account for surface charge effects and fine-tuned using plasma catalysis DFT data of single metal atoms on Al$_2$O$_3$ support and adsorbates involved in plasma-catalytic ammonia synthesis. Not only the fine-tuned attention-based GNN model provides a high test accuracy for predicting adsorption energies and atomic forces in plasma catalysis, but also shows adequate extrapolation for unseen single metal atoms in the plasma catalysis data used for the model fine-tuning. To distinguish the effects of surface charge from other dissimilarities in DFT data of thermal and plasma catalysis, a dual-model framework is presented that relies on two pre-trained GNNs, one of which is specifically tasked to capture surface charge effects using an attention mechanism that provides interpretable insights into their role. Lastly, it is demonstrated how the attention-based GNNs developed for single metal atoms can be efficiently adapted for predicting adsorption energies and atomic forces for metal clusters in plasma catalysis. This work highlights the vast potential of interpretable transfer learning from thermal catalysis to plasma catalysis to mitigate excessive computational requirements of first-principles studies in plasma catalysis, towards accelerating fundamental research in this domain. Charge (physics) ; Transfer of learning ; Plasma ; Surface (topology) ; Transfer (computing) ; Chemistry ; Psychology ; Chemical physics ; Nanotechnology ; Materials science ; Computer science ; Physics ; Artificial intelligence ; Mathematics ; Nuclear physics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Machine Learning and ELM