jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew Mitchell | Teresa Del Aguila Ferrandis | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2024-12-11)
Field: Materials Science — Materials Chemistry

1D Kinetic Energy Density Functionals learned with Symbolic Regression Orbital-free density functional theory promises to deliver linear-scaling electronic structure calculations. This requires the knowledge of the non-interacting kinetic-energy density functional (KEDF), which should be accurate and must admit accurate functional derivatives, so that a minimization procedure can be designed. In this work, symbolic regression is explored as an alternative means to machine-learn the KEDF, which results into analytical expressions, whose functional derivatives are easy to compute. The so-determined semi-local functional forms are investigated as a function of the electron number, and we are able to track the transition from the von Weizsäcker functional, exact for the one-electron case, to the Thomas-Fermi functional, exact in the homogeneous electron gas limit. A number of separate searches are performed, ranging from totally unconstrained to constrained in the form of an enhancement factor. This work highlights the complexity in constructing semi-local approximations of the KEDF and the potential of symbolic regression to advance the search. Kinetic energy ; Symbolic regression ; Regression ; Energy (signal processing) ; The Symbolic ; Regression analysis ; Mathematics ; Computer science ; Statistical physics ; Applied mathematics ; Theoretical physics ; Physics ; Statistics ; Psychology ; Artificial intelligence ; Classical mechanics Machine Learning in Materials Science
Authors: Ronald Aznavourian | Guillaume Demésy | Sébastien Guenneau | Julien Marot
Source: EPJ Applied Metamaterials (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic cloak design with mono-objective and bi-objective optimizers: seeking the best tradeoff between protection and invisibility We revisit the design of cloaks, without resorting to any geometric transform. Cancellation techniques and anomalous resonances have been applied for this purpose. Instead of a deductive reasoning, we propose a novel mono-objective optimization algorithm, namely a ternary grey wolf algorithm, and we adapt a bi-objective optimization algorithm. Firstly, the proposed chaotic ternary grey wolf algorithm searches three-valued spaces for all permittivity values in the cloak while minimizing the summation of a protection criterion and an invisibility criterion. Secondly, a bi-objective genetic algorithm is adapted to find pairs of optimal values of invisibility and protection. Invisibility ; Cloak ; Ternary operation ; Genetic algorithm ; Computer science ; Metamaterial ; Permittivity ; Mathematical optimization ; Cloaking ; Optimization algorithm ; Chaotic ; Algorithm ; Mathematics ; Theoretical computer science ; Artificial intelligence ; Optics ; Physics ; Dielectric ; Programming language Metamaterials and Metasurfaces Applications ; Antenna Design and Optimization ; Advanced Antenna and Metasurface Technologies
Authors: Shusuke Ito | Koki Muraoka | Akira Nakayama
Source: ChemRxiv (preprint, 2024-03-22)
Field: Materials Science — Materials Chemistry

De Novo Design of Organic Structure-Directing Agents for Zeolites Using a General-Purpose Large Language Model Designing organic molecules lies at the heart of solving numerous chemistry-related challenges, necessitating effective collaboration between human intuition and computational power. This study demonstrates how general-purpose Large Language Models (LLMs) such as GPT-4 can facilitate the design of potent molecules, leveraging feedback from experiments and empirical knowledge through natural language. We used this approach to design organic structure-directing agents (OSDAs) that guide the crystallization of zeolites. A computational workflow was developed, wherein the LLM proposed novel OSDAs to stabilize targeted zeolites. The suggested candidates underwent evaluation through empirical screening criteria and atomistic simulation. Feedback was then provided to the LLM in natural language to refine subsequent proposals, thus progressively enhancing the proposed OSDAs and promoting the exploration of chemical space. The predicted candidates encompassed experimentally validated OSDAs, structurally analogous ones, and novel ones with superior affinity scores, underscoring the robust capability of the LLM. The collaborations between humans and machines, utilizing natural language as the communication interface, hold potential for application in other molecular design tasks, including drug design. Intuition ; Workflow ; Computer science ; Chemical space ; Natural language ; Space (punctuation) ; Artificial intelligence ; Human–computer interaction ; Drug discovery ; Cognitive science ; Psychology ; Bioinformatics ; Biology Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Asymmetric Hydrogenation and Catalysis
Authors: Shusuke Ito | Koki Muraoka | Akira Nakayama
Source: ChemRxiv (preprint, 2024-04-08)
Field: Materials Science — Materials Chemistry

Knowledge-informed generation of organic structure-directing agents for zeolites using ChatGPT towards human-machine collaborative molecular design Designing organic molecules lies at the heart of solving numerous chemistry-related challenges, necessitating effective collaboration between human intuition and computational power. This study demonstrates how general-purpose Large Language Models (LLMs) such as GPT-4 can facilitate the design of potent molecules, leveraging feedback from experiments and empirical knowledge through natural language. We used this approach to design organic structure-directing agents (OSDAs) that guide the crystallization of zeolites. A computational workflow was developed, wherein the LLM proposed novel OSDAs to stabilize targeted zeolites. The suggested candidates underwent evaluation through empirical screening criteria and atomistic simulation. Feedback was then provided to the LLM in natural language to refine subsequent proposals, thus progressively enhancing the proposed OSDAs and promoting the exploration of chemical space. The predicted candidates encompassed experimentally validated OSDAs, structurally analogous ones, and novel ones with superior affinity scores, underscoring the robust capability of the LLM. The collaborations between humans and machines, utilizing natural language as the communication interface, hold potential for application in other molecular design tasks, including drug design. Workflow ; Computer science ; Chemical space ; Intuition ; Natural language ; Empirical research ; Human–computer interaction ; Artificial intelligence ; Knowledge management ; Nanotechnology ; Drug discovery ; Chemistry ; Cognitive science ; Materials science ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Zeolite Catalysis and Synthesis
Authors: Sherry Yang | Simon Batzner | Ruiqi Gao | Muratahan Aykol | Alexander L. Gaunt | Brendan McMorrow | Danilo Jimenez Rezende | Dale Schuurmans | Igor Mordatch | Ekin D. Cubuk
Source: arXiv (Cornell University) (preprint, 2024-09-10)
Field: Materials Science — Materials Chemistry

Generative Hierarchical Materials Search Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from the domain expert in the form of high-level instructions can be essential for an automated system to output candidate crystals that are viable for downstream research. In this work, we formulate end-to-end language-to-structure generation as a multi-objective optimization problem, and propose Generative Hierarchical Materials Search (GenMS) for controllable generation of crystal structures. GenMS consists of (1) a language model that takes high-level natural language as input and generates intermediate textual information about a crystal (e.g., chemical formulae), and (2) a diffusion model that takes intermediate information as input and generates low-level continuous value crystal structures. GenMS additionally uses a graph neural network to predict properties (e.g., formation energy) from the generated crystal structures. During inference, GenMS leverages all three components to conduct a forward tree search over the space of possible structures. Experiments show that GenMS outperforms other alternatives of directly using language models to generate structures both in satisfying user request and in generating low-energy structures. We confirm that GenMS is able to generate common crystal structures such as double perovskites, or spinels, solely from natural language input, and hence can form the foundation for more complex structure generation in near future. Generative grammar ; Computer science ; Generative model ; Artificial intelligence ; Natural language processing Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Viacheslav Shkirskiy | Frédéric Kanoufi
Source: ChemRxiv (preprint, 2024-02-01)
Field: Chemistry — Electrochemistry

Key Requirements for Advancing Machine Learning Approaches in Single Entity Electrochemistry Despite the noteworthy progress in Single Entity Electrochemistry (SEE) in the last decade, the field still must undergo further advancements to attain the requisite maturity for facilitating and propelling machine learning (ML)-based discoveries. This mini-review presents an analysis of the required developments in the domain, using the success of AlphaFold in biology as a benchmark for future progress. The first essential requirement is the creation and support of high-quality, centralized, and open-access databases on the electrochemical properties of single entities. This should be facilitated through the automation and standardization of experiments, promoting high-throughput output and facilitating comparison between datasets. Finally, the creation of a new type of interdisciplinary specialist, trained to pinpoint critical issues in SEE and implement solutions from applied informatics, is vital for ML approaches to flourish in the SEE field. Standardization ; Computer science ; Field (mathematics) ; Automation ; Domain (mathematical analysis) ; Key (lock) ; Informatics ; Data science ; Benchmark (surveying) ; Quality (philosophy) ; Software engineering ; Artificial intelligence ; Engineering management ; Process management ; Engineering ; Computer security Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques ; Electrocatalysts for Energy Conversion
Authors: Yu-Qi Liang | Yan Zhang
Source: Journal of Computing and Electronic Information Management (article, 2024-09-28)
Field: Materials Science — Materials Chemistry

Data-Driven Approaches for Predicting Catalyst Performance in CO2 Hydrogenation This paper explores the critical role of data collection and preparation in leveraging machine learning techniques for predicting catalyst performance in CO2 hydrogenation processes. As the global community seeks sustainable solutions to mitigate carbon emissions, understanding the efficiency of catalysts in converting CO2 into valuable chemicals becomes increasingly important. The study discusses various types of data utilized in this context, including both experimental and simulation data, while highlighting their significance in comprehensively understanding key factors such as reaction rates, selectivity, and catalyst stability. For instance, the ability of a catalyst to selectively produce desired products over others can significantly impact the overall economic viability of CO2 hydrogenation processes. Furthermore, stability data sheds light on the longevity and durability of catalysts, revealing insights into deactivation mechanisms that can occur due to factors like sintering, poisoning, or leaching of active sites. On the other hand, simulation data generated from advanced computational methods such as density functional theory (DFT) and molecular dynamics (MD) provides a deeper understanding of the electronic and structural properties of catalysts. These computational techniques allow researchers to predict reaction pathways, activation energies, and the behavior of intermediate species, thereby complementing experimental findings and guiding the design of more effective catalysts. The paper also emphasizes the importance of utilizing publicly available databases and collaborative research datasets, which serve to enhance data accessibility and foster scientific collaboration among researchers in the field. By pooling resources and sharing findings, the scientific community can accelerate the discovery and optimization of novel catalysts. Additionally, the study outlines essential steps in the data preparation process, including rigorous data cleaning, preprocessing, and feature selection, all of which are crucial for ensuring the quality and reliability of the data used in machine learning models. The paper discusses the implementation of cross-validation techniques and performance metrics that help evaluate and validate model predictions, ensuring that the developed models generalize well to unseen data. Catalysis ; Environmental science ; Materials science ; Process engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysts for Methane Reforming ; Catalytic Processes in Materials Science
Authors: Zhiyuan Liu | Yaorui Shi | An Zhang | Sihang Li | Enzhi Zhang | Xiang Wang | Kenji Kawaguchi | Tat‐Seng Chua
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

ReactXT: Understanding Molecular “Reaction-ship” via Reaction-Contextualized Molecule-Text Pretraining Molecule-text modeling, which aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge, is an emerging research direction.Beyond single molecules, studying reaction-text modeling holds promise for helping the synthesis of new materials and drugs.However, previous works mostly neglect reaction-text modeling: they primarily focus on modeling individual molecule-text pairs or learning chemical reactions without texts in context.Additionally, one key task of reaction-text modeling -experimental procedure prediction -is less explored due to the absence of an open-source dataset.The task is to predict step-by-step actions of conducting chemical experiments and is crucial to automating chemical synthesis.To resolve the challenges above, we propose a new pretraining method, ReactXT, for reaction-text modeling, and a new dataset, OpenExp, for experimental procedure prediction.Specifically, ReactXT features three types of input contexts to incrementally pretrain LMs.Each of the three input contexts corresponds to a pretraining task to improve the text-based understanding of either reactions or single molecules.ReactXT demonstrates consistent improvements in experimental procedure prediction and molecule captioning and offers competitive results in retrosynthesis.Our code is available at https: //github.com/syr-cn/ReactXT. Computer science ; Molecule ; Chemistry ; Astrobiology ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Zhenyao Fang | Qimin Yan
Source: arXiv (Cornell University) (preprint, 2024-01-29)
Field: Materials Science — Materials Chemistry

Towards Accurate Prediction of Configurational Disorder Properties in Materials using Graph Neural Networks The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient and accurate evaluations of configurational energies. Previous cluster expansion methods are not applicable to configurationally-complex material systems, including those with atomic distortions and long-range orders. In this work, we propose to leverage the versatile expressive capabilities of graph neural networks (GNNs) for efficient evaluations of configurational energies and present a workflow combining attention-based GNNs and Monte Carlo simulations to calculate the disorder properties. Using the dataset of face-centered tetragonal gold copper without and with local atomic distortions as an example, we demonstrate that the proposed data-driven framework enables the prediction of phase transition temperatures close to experimental values. We also elucidate that the variance of the energy deviations among configurations controls the prediction accuracy of disorder properties and can be used as the target loss function when training and selecting the GNN models. The work serves as a fundamental step toward a new data-driven paradigm for the accelerated design of configurationally-complex functional material systems. Artificial neural network ; Graph ; Computer science ; Artificial intelligence ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Kohei Noda | Araki Wakiuchi | Yoshihiro Hayashi | Ryo Yoshida
Source: arXiv (Cornell University) (preprint, 2024-03-25)
Field: Materials Science — Materials Chemistry

Advancing Extrapolative Predictions of Material Properties through Learning to Learn Recent advancements in machine learning have showcased its potential to significantly accelerate the discovery of new materials. Central to this progress is the development of rapidly computable property predictors, enabling the identification of novel materials with desired properties from vast material spaces. However, the limited availability of data resources poses a significant challenge in data-driven materials research, particularly hindering the exploration of innovative materials beyond the boundaries of existing data. While machine learning predictors are inherently interpolative, establishing a general methodology to create an extrapolative predictor remains a fundamental challenge, limiting the search for innovative materials beyond existing data boundaries. In this study, we leverage an attention-based architecture of neural networks and meta-learning algorithms to acquire extrapolative generalization capability. The meta-learners, experienced repeatedly with arbitrarily generated extrapolative tasks, can acquire outstanding generalization capability in unexplored material spaces. Through the tasks of predicting the physical properties of polymeric materials and hybrid organic--inorganic perovskites, we highlight the potential of such extrapolatively trained models, particularly with their ability to rapidly adapt to unseen material domains in transfer learning scenarios. Leverage (statistics) ; Artificial intelligence ; Computer science ; Machine learning ; Generalization ; Limiting ; Property (philosophy) ; Transfer of learning ; Identification (biology) ; Data science ; Engineering ; Mathematics Machine Learning in Materials Science
Authors: Rohit S. K. Gadde | Sreelaya Devaguptam | Fangning Ren | Rajat Mittal | Lechen Dong | Yao Wang | Fang Liu
Source: ChemRxiv (preprint, 2024-03-05)
Field: Materials Science — Materials Chemistry

Chatbot-Assisted Quantum Chemistry for Explicitly Solvated Molecules Advanced computational chemistry software packages have transformed chemical research by leveraging quantum chemistry and molecular simulations. Despite their capabilities, the intricate design and requirement for specialized computing hardware hinder their applications in the broad chemistry community. Here, we introduce AutoSolvateWeb, a chatbot-assisted computational platform that addresses both challenges simultaneously. This platform employs a user-friendly chatbot interface to guide non-experts through a multistep procedure involving various computational packages, enabling them to configure and execute complex quantum mechanical/molecular mechanical (QM/MM) simulations of explicitly solvated molecules. Moreover, this platform operates on cloud infrastructure, allowing researchers to run simulations without prerequisite computing hardware. As a proof of concept, AutoSolvateWeb demonstrates that combining virtual agents with cloud computing can democratize access to sophisticated computational research tools. Chemistry ; Quantum ; Molecule ; Quantum chemistry ; Chemical physics ; Computational chemistry ; Quantum mechanics ; Physics ; Organic chemistry ; Supramolecular chemistry Machine Learning in Materials Science
Authors: Tim Mueller | Joseph H. Montoya | Weike Ye | X. L. Lei | Linda Hung | Jens S. Hummelshøj | Michael Puzon | Daniel Martinez | Chris Fajardo | Rachel Abela
Source: Proceedings of the National Academy of Sciences (article, 2024-09-09)
Field: Materials Science — Materials Chemistry

An electrochemical series for materials The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. To address this problem, we have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this series is generally more consistent with oxidation states in solid-state materials than the series based on aqueous ions. The electrochemical series was constructed by developing and parameterizing a physical, human-interpretable model of oxidation states in materials. We show that this model enables the prediction of oxidation states from composition in a way that is more accurate than a state-of-the-art transformer-based neural network model. We present applications of our approach to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available website and API. Series (stratigraphy) ; Electrochemistry ; Computer science ; Solid-state ; Aqueous solution ; Transformer ; Materials science ; Electrode ; Chemistry ; Physical chemistry ; Voltage ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography