jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhiling Zheng | Federico Florit | Brooke Jin | Haoyang Wu | Shih‐Cheng Li | Kakasaheb Y. Nandiwale | Chase A. Salazar | Jason Mustakis | William H. Green | Klavs F. Jensen
Source: Angewandte Chemie International Edition (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions Electrochemical C-H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet identifying suitable substrates and optimizing synthesis remain challenging. Here, we report an integrated approach combining machine learning and large language models to streamline the exploration of electrochemical C-H oxidation reactions. Utilizing a batch rapid screening electrochemical platform, we evaluated a wide range of reactions, initially classifying substrates by their reactivity, while LLMs text-mined literature data to augment the training set. The resulting ML models for reactivity prediction achieved high accuracy (>90 %) and enabled virtual screening of a large set of commercially available molecules. To optimize reaction conditions for selected substrates, LLMs were prompted to generate code that iteratively improved yields. This human-AI collaboration proved effective, efficiently identifying high-yield conditions for 8 drug-like substances or intermediates. Notably, we benchmarked the accuracy and reliability of 12 different LLMs-including LLaMA series, Claude series, OpenAI o1, and GPT-4-on code generation and function calling related to ML based on natural language prompts given by chemists to showcase potentials for accelerating research across four diverse tasks. In addition, we collected an experimental benchmark dataset comprising 1071 reaction conditions and yields for electrochemical C-H oxidation reactions. Benchmark (surveying) ; Computer science ; Set (abstract data type) ; Artificial intelligence ; Code (set theory) ; Reactivity (psychology) ; Machine learning ; Electrochemistry ; Chemistry ; Programming language Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Asymmetric Hydrogenation and Catalysis
Authors: Michael H. Prince | Henry Chan | Aikaterini Vriza | Tao Zhou | Varuni K. Sastry | Yanqi Luo | Matthew T. Dearing | Ross Harder | Rama K. Vasudevan | Mathew J. Cherukara
Source: npj Computational Materials (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

Opportunities for retrieval and tool augmented large language models in scientific facilities Abstract Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output. Scientific instrument ; Computer science ; Documentation ; Leverage (statistics) ; Data science ; Context (archaeology) ; Artificial intelligence Machine Learning in Materials Science ; Scientific Computing and Data Management ; Electronic and Structural Properties of Oxides
Authors: H. M. Johnson | Filipp Gusev | Jordan T. Dull | Yejoon Seo | Rodney D. Priestley | Olexandr Isayev | Barry P. Rand
Source: Journal of the American Chemical Society (article, 2024-07-25)
Field: Materials Science — Materials Chemistry

Discovery of Crystallizable Organic Semiconductors with Machine Learning Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts. Certain organic molecular thin films can be transitioned from initially prepared amorphous layers to large-scale crystalline films via abrupt thermal annealing. Ideally, these films crystallize as platelets with long-range-ordered domains on the scale of tens to hundreds of microns. However, other organic molecular thin films may instead crystallize as spherulites or resist crystallization entirely. Organic molecules that have the capability of transforming into a platelet morphology feature both high melting point (<i>T</i><sub><i>m</i></sub>) and crystallization driving force (<i>ΔG</i><sub><i>c</i></sub>). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were experimentally evaluated; three crystallized as platelets, one crystallized as a spherulite, and two resisted thin film crystallization. These results demonstrate a successful application of ML in the scope of predicting thermal properties of organic molecules and reinforce the principles of <i>T</i><sub><i>m</i></sub> and <i>ΔG</i><sub><i>c</i></sub> as metrics that aid in predicting the crystallization behavior of organic thin films. Organic semiconductor ; Amorphous solid ; Chemistry ; Annealing (glass) ; Crystallization ; Thin film ; Melting point ; Semiconductor ; Exciton ; Polymer ; Chemical physics ; Nanotechnology ; Chemical engineering ; Crystallography ; Optoelectronics ; Materials science ; Condensed matter physics ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Liujia Qian | Rui Sun | Ruedi Aebersold | Peter Bühlmann | Chris Sander | Tiannan Guo
Source: Cell Genomics (article, 2024-11-01)
Field: Chemistry — Spectroscopy

AI-empowered perturbation proteomics for complex biological systems The insufficient availability of comprehensive protein-level perturbation data is impeding the widespread adoption of systems biology. In this perspective, we introduce the rationale, essentiality, and practicality of perturbation proteomics. Biological systems are perturbed with diverse biological, chemical, and/or physical factors, followed by proteomic measurements at various levels, including changes in protein expression and turnover, post-translational modifications, protein interactions, transport, and localization, along with phenotypic data. Computational models, employing traditional machine learning or deep learning, identify or predict perturbation responses, mechanisms of action, and protein functions, aiding in therapy selection, compound design, and efficient experiment design. We propose to outline a generic PMMP (perturbation, measurement, modeling to prediction) pipeline and build foundation models or other suitable mathematical models based on large-scale perturbation proteomic data. Finally, we contrast modeling between artificially and naturally perturbed systems and highlight the importance of perturbation proteomics for advancing our understanding and predictive modeling of biological systems. Proteomics ; Computational biology ; Computer science ; Perturbation (astronomy) ; Data science ; Biology ; Physics ; Genetics Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques ; Gene Regulatory Network Analysis
Authors: Manajit Das | Ankit Ghosh | Raghavan B. Sunoj
Source: Journal of Computational Chemistry (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Advances in machine learning with chemical language models in molecular property and reaction outcome predictions Molecular properties and reactions form the foundation of chemical space. Over the years, innumerable molecules have been synthesized, a smaller fraction of them found immediate applications, while a larger proportion served as a testimony to creative and empirical nature of the domain of chemical science. With increasing emphasis on sustainable practices, it is desirable that a target set of molecules are synthesized preferably through a fewer empirical attempts instead of a larger library, to realize an active candidate. In this front, predictive endeavors using machine learning (ML) models built on available data acquire high timely significance. Prediction of molecular property and reaction outcome remain one of the burgeoning applications of ML in chemical science. Among several methods of encoding molecular samples for ML models, the ones that employ language like representations are gaining steady popularity. Such representations would additionally help adopt well-developed natural language processing (NLP) models for chemical applications. Given this advantageous background, herein we describe several successful chemical applications of NLP focusing on molecular property and reaction outcome predictions. From relatively simpler recurrent neural networks (RNNs) to complex models like transformers, different network architecture have been leveraged for tasks such as de novo drug design, catalyst generation, forward and retro-synthesis predictions. The chemical language model (CLM) provides promising avenues toward a broad range of applications in a time and cost-effective manner. While we showcase an optimistic outlook of CLMs, attention is also placed on the persisting challenges in reaction domain, which would optimistically be addressed by advanced algorithms tailored to chemical language and with increased availability of high-quality datasets. Chemical space ; Computer science ; Artificial intelligence ; Outcome (game theory) ; Machine learning ; Property (philosophy) ; Chemistry ; Drug discovery ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Andreas Karydis‐Messinis | Christina Kyriakaki | Eleni Triantafyllou | Kyriaki Tsirka | Christina Gioti | Dimitris Gkikas | Konstantinos Nesseris | Dimitrios A. Exarchos | Spyridoula Farmaki | Aris E. Giannakas | Constantinos E. Salmas | Theodore E. Matikas | Dimitrios Moschovas | Apostolos Avgeropoulos
Source: Gels (article, 2024-04-09)
Field: Materials Science — Biomaterials

Development and Physicochemical Characterization of Edible Chitosan–Casein Hydrogel Membranes for Potential Use in Food Packaging The increasing global concern over plastic waste and its environmental impact has led to a growing interest in the development of sustainable packaging alternatives. This study focuses on the innovative use of expired dairy products as a potential resource for producing edible packaging materials. Expired milk and yogurt were selected as the primary raw materials due to their protein and carbohydrate content. The extracted casein was combined with various concentrations of chitosan, glycerol, and squid ink, leading to the studied samples. Chitosan was chosen due to its appealing characteristics, including biodegradability, and film-forming properties, and casein was utilized for its superior barrier and film-forming properties, as well as its biodegradability and non-toxic nature. Glycerol was used to further improve the flexibility of the materials. The prepared hydrogels were characterized using various instrumental methods, and the findings reveal that the expired dairy-based edible packaging materials exhibited promising mechanical properties comparable to conventional plastic packaging and improved barrier properties with zero-oxygen permeability of the hydrogel membranes, indicating that these materials have the potential to effectively protect food products from external factors that could compromise quality and shelf life. Food packaging ; Biodegradation ; Chitosan ; Oxygen permeability ; Food science ; Materials science ; Shelf life ; Raw material ; Self-healing hydrogels ; Casein ; Gelatin ; Pulp and paper industry ; Chemistry ; Chemical engineering ; Organic chemistry ; Oxygen ; Polymer chemistry Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution ; Food Waste Reduction and Sustainability
Authors: Priyanka Sinha | M. V. Jyothirmai | B. Moses Abraham | Jayant K. Singh
Source: Materials Chemistry and Physics (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Machine learning driven advancements in catalysis for predicting hydrogen evolution reaction activity Catalysis ; Nanotechnology ; Materials science ; Chemistry ; Computer science ; Chemical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Chenghan Sun | Rajat Goel | Ambarish Kulkarni
Source: Langmuir (article, 2024-02-05)
Field: Materials Science — Materials Chemistry

Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NH<sub><i>x</i></sub> (<i>x</i> = 1, 2, and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including: (1) using a sequential optimization protocol, (2) developing a new geometry-based descriptor, and (3) repurposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cost-effective DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community. Workflow ; Density functional theory ; Computer science ; Entropy (arrow of time) ; Adsorption ; Alloy ; Chemistry ; Machine learning ; Artificial intelligence ; Computational chemistry ; Thermodynamics ; Physics ; Database ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Pengcheng Xue | Rui Qiu | Chuchuan Peng | Zehang Peng | Kui Ding | Rui Long | Liang Ma | Qifeng Zheng
Source: Advanced Science (review, 2024-11-18)
Field: Materials Science — Materials Chemistry

Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. However, the accuracy of ML predictions is strongly dependent on the underlying data, while the data of lithium battery materials faces many challenges, such as the multi-sources, heterogeneity, high-dimensionality, and small-sample size. Through the systematic review of the existing literatures, several effective strategies are proposed for data processing as follows: classification and extraction, screening and exploration, dimensionality reduction and generation, modeling and evaluation, and incorporation of domain knowledge, with the aim to enhance the data quality, model reliability, and interpretability. Furthermore, other possible strategies for addressing data quality such as database management techniques and data analysis methodologies are also emphasized. At last, an outlook of ML development for data processing methods is presented. These methodologies are not only applicable to the data of lithium battery materials, but also endow important reference significance to electrocatalysis, electrochemical corrosion, high-entropy alloys, and other fields with similar data challenges. Interpretability ; Computer science ; Dimensionality reduction ; Curse of dimensionality ; Lithium battery ; Reliability (semiconductor) ; Battery (electricity) ; Lithium (medication) ; Data mining ; Machine learning ; Artificial intelligence ; Power (physics) Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: S. Macé | Yingjian Xu | Bao N. Nguyen
Source: ChemCatChem (article, 2024-01-05)
Field: Materials Science — Materials Chemistry

Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning Abstract Significant progress has been made in recent years in the use of AI and Machine Learning (ML) for catalyst discovery and optimisation. The effectiveness of ML and data science techniques was demonstrated in predicting and optimising enantioselectivity and regioselectivity in catalytic reactions through optimisation of the ligands, counterions and reaction conditions. Direct discovery of new catalysts/reactions is more difficult and requires efficient exploration of transition metal chemical space. A range of computational techniques for descriptor generation, ranging from molecular mechanics to DFT methods, have been successfully demonstrated, often in conjunction with ML to reduce computational cost associated with TS calculations. Complex aspects of catalytic reactions, such as solvent, temperature, etc., have also been successfully incorporated into the ML optimisation and discovery workflow. Catalysis ; Transition metal ; Computer science ; Chemistry ; Nanotechnology ; Artificial intelligence ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Stephen Xie | Shreyas Honrao | John W. Lawson
Source: Chemistry of Materials (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of Li Solid-State Electrolytes with Bond Valence Methods and Machine Learning Li-based solid-state electrolyte materials enable safer, all-solid-state batteries, but the computational search for candidates with favorable stability and high Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a high-throughput screening approach for identifying promising materials using a combination of bond-valence methods and graph neural networks. An ablation study involving geometric and bond-valence quantities reveals their relative importance in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li SSE. We identify 329 candidates with good stability and ionic conductivity, including 28 stable against Li metal. Furthermore, we combine the ML-accelerated screening procedure with an isovalent substitution scheme to generate and screen additional candidates beyond existing databases, identifying an additional 239 candidate materials for battery applications. Solid-state ; Valence (chemistry) ; Throughput ; High-throughput screening ; Fast ion conductor ; Materials science ; Valence bond theory ; Electrolyte ; Chemistry ; Computer science ; Nanotechnology ; Physical chemistry ; Organic chemistry ; Electrode ; Molecule ; Telecommunications Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Sangjoon Lee | Clio Chen | Griheydi Garcia | Anton O. Oliynyk
Source: Data in Brief (article, 2024-02-09)
Field: Materials Science — Materials Chemistry

Machine learning descriptors in materials chemistry used in multiple experimentally validated studies: Oliynyk elemental property dataset Materials informatics employs data-driven approaches for analysis and discovery of materials. Features also referred to as descriptors are essential in generating reliable and accurate machine-learning models. While general data can be obtained through public and commercial sources, features must be tailored to specific applications. Common featurizers suitable for generic chemical problems may not be effective in features-property mapping in solid-state materials with ML models. Here, we have assembled the Oliynyk property list for compositional feature generation, which performs well on limited datasets (50 to 1000 training data points) in the solid-state materials domain. The dataset contains 98 elemental features for atomic numbers from 1 to 92, including thermodynamic properties, electronic structure data, size, electronegativity, and bulk properties such as melting point, density, and conductivity. The dataset has been utilized peer-reviewed publications in predicting material hardness, classification, discovery of novel Heusler compounds, band gap prediction, and determining the site preference of atoms using machine learning models including support vector machines, random forests for classification, and support vector regression for regression problems. We have compiled the dataset by parsing data from publicly available databases and literature and further supplementing it by interpolating values with Gaussian process regression. Support vector machine ; Random forest ; Computer science ; Materials informatics ; Property (philosophy) ; Machine learning ; Artificial intelligence ; Kriging ; Electronegativity ; Parsing ; Data mining ; Ensemble learning ; Chemistry ; Health informatics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Qian Chen | Han Wang | Haodi Ji | Xiaobing Ma | Yikun Cai
Source: Process Safety and Environmental Protection (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

Data-driven atmospheric corrosion prediction model for alloys based on a two-stage machine learning approach Stage (stratigraphy) ; Corrosion ; Artificial intelligence ; Materials science ; Computer science ; Metallurgy ; Machine learning ; Geology Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques