Seyed Mohamad Moosavi
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MOF-ChemUnity: Literature-Informed Large Language Models for Metal–Organic Framework Research Open
Artificial intelligence (AI) is transforming research in metal-organic frameworks (MOFs), where models trained on structured computational data routinely predict new materials and optimize their properties. This raises a central question: …
32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery Open
Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent develo…
View article: The Rise of Generative AI for Metal-Organic Framework Design and Synthesis
The Rise of Generative AI for Metal-Organic Framework Design and Synthesis Open
Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This Perspective introduces the shift from laborious enumeration of MOF candidates to generative approaches th…
MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling Open
Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accuracy close to quantum calculations. However, their reliabil…
Connecting metal-organic framework synthesis to applications using multimodal machine learning Open
Every year, researchers create hundreds of thousands of new materials, each with unique structures and properties. For example, over 5000 new metal-organic frameworks (MOFs) were reported in the past year alone. While these materials are o…
CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction Open
Developing improved predictive models for multi-molecular systems is crucial, as nearly every chemical product used results from a mixture of chemicals. While being a vital part of the industry pipeline, the chemical mixture space remains …
Thermodynamics-informed machine learning for predicting temperature-dependent chemical properties Open
Emerging energy and electronic systems rely on the thermodynamic properties of chemical and cooling fluids. These properties are a function of both chemical structure and temperature. For instance, the dynamic viscosity of a fluid can vary…
MOF-ChemUnity: Unifying metal-organic framework data using large language models Open
Artificial intelligence (AI) is transforming materials research in metal-organic frameworks (MOFs), where models trained on structured computational data routinely predict new materials and optimize their properties. This raises a central …
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery Open
Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent develo…
Periodic GFN1-xTB Tight Binding: A Generalized Ewald Partitioning Scheme for the Klopman–Ohno Function Open
A novel formulation is presented for the treatment of electrostatics in the periodic GFN1-xTB tight-binding model. Periodic GFN1-xTB is hindered by the functional form of the second-order electrostatics, which only recovers Coulombic behav…
Adaptive representation of molecules and materials in Bayesian optimization Open
Feature Adaptive Bayesian Optimization (FABO) enhances molecular and materials discovery by dynamically selecting optimal feature representations during Bayesian optimization, outperforming fixed representations.
Assessment of fine-tuned large language models for real-world chemistry and material science applications Open
We studied the performance of fine-tuning open-source LLMs for a range of different chemical questions. We benchmark their performances against “traditional” machine learning models and find that, in most cases, the fine-tuning approach is…
View article: Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Open
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submis…
Adaptive Representation of Molecules and Materials in Bayesian Optimization Open
Bayesian optimization (BO) is increasingly used in molecular optimization and in guiding self-driving laboratories for automated materials discovery. A crucial aspect of BO is how molecules and materials are represented as feature vectors,…
Connecting metal-organic framework synthesis to applications with a self-supervised multimodal model Open
Every year, researchers create hundreds of thousands of new materials, each with unique structures and properties. For example, over 5,000 new metal-organic frameworks (MOFs) were reported in the past year alone. While these materials are …
Assessment of Fine-Tuned Large Language Models for Real-World Chemistry and Material Science Applications Open
The current generation of large language models (LLMs), like ChatGPT, have limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. In this work, we explore the …
A holistic platform for accelerating sorbent-based carbon capture Open
Reducing carbon dioxide (CO 2 ) emissions urgently requires the large-scale deployment of carbon-capture technologies. These technologies must separate CO 2 from various sources and deliver it to different sinks 1,2 . The quest for optimal…
Inverse design of porous materials: a diffusion model approach Open
A diffusion model was employed to generate porous materials, marking one of the earliest endeavors in this domain. The model demonstrates high efficacy in designing structures with user-desired properties.
Agent-based learning of materials datasets from the scientific literature Open
An AI Agent for autonomous development of materials dataset from scientific literature.
Agent-based Learning of Materials Datasets from Scientific Literature Open
Advancements in machine learning and artificial intelligence are transforming materials discovery. Yet, the availability of structured experimental data remains a bottleneck. The vast corpus of scientific literature presents a valuable and…
View article: Accelerated chemical science with AI
Accelerated chemical science with AI Open
The ASLLA Symposium focused on accelerating chemical science with AI. Discussions on data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, and academic bodies were provided.
Inverse Design of Porous Materials: A Diffusion Model Approach Open
The success of diffusion models in the field of image processing has propelled the creation of software such as Dall-E, Midjourney and Stable Diffusion, which are tools used for text-to-image generations. Mapping this workflow onto materia…
A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture Open
To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials, we also increasingly rely on isotherms pre…
Shedding Light on the Stakeholders' Perspectives for Carbon Capture Open
Reducing CO2 emissions requires urgently deploying large-scale carbon capture technologies, amongst other strategies. The quest for optimum technologies is a multi-objective problem involving various stakeholders. Today's research of these…
Shedding Light on the Stakeholders' Perspectives for Carbon Capture Open
The data repository contains three folders: 1. "CIFS", and it includes all structure files 2. "Properties", and it includes all geometric and adsorption properties for simulated and experimental structures 3. ""KPIs", and it includes KPIs …