jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: X. H. Xie | Zhengxin Yang | Dongxiao Chen | Yunfei Shi | Pei‐Lin Kang | Sicong Ma | Yefei Li | Cheng Shang | Zhi‐Pan Liu
Source: Precision Chemistry (review, 2024-09-14)
Field: Materials Science — Materials Chemistry

LASP to the Future of Atomic Simulation: Intelligence and Automation Atomic simulations aim to understand and predict complex physical phenomena, the success of which relies largely on the accuracy of the potential energy surface description and the efficiency to capture important rare events. LASP software (large-scale atomic simulation with a Neural Network Potential), released in 2018, incorporates the key ingredients to fulfill the ultimate goal of atomic simulations by combining advanced neural network potentials with efficient global optimization methods. This review introduces the recent development of the software along two main streams, namely, higher intelligence and more automation, to solve complex material and reaction problems. The latest version of LASP (LASP 3.7) features the global many-body function corrected neural network (G-MBNN) to improve the PES accuracy with low cost, which achieves a linear scaling efficiency for large-scale atomic simulations. The key functionalities of LASP are updated to incorporate (i) the ASOP and ML-interface methods for finding complex surface and interface structures under grand canonic conditions; (ii) the ML-TS and MMLPS methods to identify the lowest energy reaction pathway. With these powerful functionalities, LASP now serves as an intelligent data generator to create computational databases for end users. We exemplify the recent LASP database construction in zeolite and the metal-ligand properties for a new catalyst design. Automation ; Computer science ; Systems engineering ; Data science ; Cognitive science ; Engineering ; Engineering ethics ; Psychology ; Mechanical engineering Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Xuefeng Bai | Ya-Bo Xie | Xin Zhang | Honggui Han | Jian‐Rong Li
Source: Journal of Chemical Information and Modeling (article, 2024-03-26)
Field: Materials Science — Materials Chemistry

Evaluation of Open-Source Large Language Models for Metal–Organic Frameworks Research Along with the development of machine learning, deep learning, and large language models (LLMs) such as GPT-4 (GPT: Generative Pre-Trained Transformer), artificial intelligence (AI) tools have been playing an increasingly important role in chemical and material research to facilitate the material screening and design. Despite the exciting progress of GPT-4 based AI research assistance, open-source LLMs have not gained much attention from the scientific community. This work primarily focused on metal-organic frameworks (MOFs) as a subdomain of chemistry and evaluated six top-rated open-source LLMs with a comprehensive set of tasks including MOFs knowledge, basic chemistry knowledge, in-depth chemistry knowledge, knowledge extraction, database reading, predicting material property, experiment design, computational scripts generation, guiding experiment, data analysis, and paper polishing, which covers the basic units of MOFs research. In general, these LLMs were capable of most of the tasks. Especially, Llama2-7B and ChatGLM2-6B were found to perform particularly well with moderate computational resources. Additionally, the performance of different parameter versions of the same model was compared, which revealed the superior performance of higher parameter versions. Computer science ; Scripting language ; Artificial intelligence ; Data science ; Computational model ; Set (abstract data type) ; Natural language processing ; Programming language Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Asanka Wijerathne | Allison M. Sawyer | Rohil Daya | Christopher Paolucci
Source: JACS Au (article, 2024-01-04)
Field: Chemistry — Inorganic Chemistry

Competition between Mononuclear and Binuclear Copper Sites across Different Zeolite Topologies A key challenge for metal-exchanged zeolites is the determination of metal cation speciation and nuclearity under synthesis and reaction conditions. Copper-exchanged zeolites, which are widely used in automotive emissions control and potential catalysts for partial methane oxidation, have in particular evidenced a wide variety of Cu structures that are observed to change with exposure conditions, zeolite composition, and topology. Here, we develop predictive models for Cu cation speciation and nuclearity in CHA, MOR, BEA, AFX, and FER zeolite topologies using interatomic potentials, quantum chemical calculations, and Monte Carlo simulations to interrogate this vast configurational and compositional space. Model predictions are used to rationalize experimentally observed differences between Cu-zeolites in a wide-body of literature, including nuclearity populations, structural variations, and methanol per Cu yields. Our results show that both topological features and commonly observed Al-siting biases in MOR zeolites increase the population of binuclear Cu sites, explaining the small population of mononuclear Cu sites observed in these materials relative to other zeolites such as CHA and BEA. Finally, we used a machine learning classification model to determine the preference to form mononuclear or binuclear Cu sites at different Al configurations in 200 zeolites in the international zeolite database. Model results reveal several zeolite topologies at extreme ends of the mononuclear vs binuclear spectrum, highlighting synthetic options for realization of zeolites with strong Cu nuclearity preferences. Zeolite ; Population ; Copper ; Topology (electrical circuits) ; Chemistry ; Metal ; Network topology ; Catalysis ; Computational chemistry ; Inorganic chemistry ; Materials science ; Chemical physics ; Organic chemistry ; Computer science ; Mathematics Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Shun‐Li Shang | Shuang Lin | Michael C. Gao | Darrell G. Schlom | Zi‐Kui Liu
Source: APL Materials (article, 2024-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ellingham diagrams of binary oxides Controlling the oxidation state of constituents by tuning the oxidizing environment and materials chemistry is vital to the successful synthesis of targeted binary or multicomponent oxides. We have conducted a comprehensive thermodynamic analysis of 137 binary oxides to calculate their Ellingham diagrams. It is found that the “reactive” elements that oxidize easily are the f-block elements (lanthanides and actinides), elements in groups II, III, and IV (alkaline earth, Sc, Y, Ti, Zr, and Hf), and Al and Li. In contrast, the “noble” elements are easily reduced. These are coinage metals (Cu, Ag, and especially Au), Pt-group elements, and Hg and Se. Machine learning-based sequential feature selection indicates that the ease of oxidation can be represented by the electronic structures of pure elements, for example, their d- and s-valence electrons, Mendeleev numbers, and groups, making the Periodic Table a useful tool for qualitatively assessing the ease of oxidation. The other elemental features that weakly correlate with the ease of oxidation are thermochemical properties such as melting points and the standard entropy at 298 K of pure elements. Applying Ellingham diagrams enables the oxidation of multicomponent materials to be predicted, such as the Fe–20Cr–20Ni alloy (in wt. %) and the equimolar high entropy alloy of AlCoCrFeNi. These Ellingham diagram-based predictions are in accordance with thermodynamic calculations using the CALPHAD approach and experimental observations in the literature. Materials science ; Binary number Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics
Authors: Liangrong Li | Wendan Jiang | Xiaozhe Yang | Yundong Meng | Peng Hu | Cheng Huang | Feng Liu
Source: Polymers (review, 2024-08-16)
Field: Materials Science — Polymers and Plastics

From Molecular Design to Practical Applications: Strategies for Enhancing the Optical and Thermal Performance of Polyimide Films Polyimide (PI) films are well recognized for their outstanding chemical resistance, radiation resistance, thermal properties, and mechanical strength, rendering them highly valuable in advanced fields such as aerospace, sophisticated electronic components, and semiconductors. However, improving their optical transparency while maintaining excellent thermal properties remains a significant challenge. This review systematically checks over recent advancements in enhancing the optical and thermal performance of PI films, focusing on various strategies through molecular design. These strategies include optimizing the main chain, side chain, non-coplanar structures, and endcap groups. Rigid and flexible structural characteristics in the proper combination can contribute to the balance thermal stability and optical transparency. Introducing fluorinated substituents and bulky side groups significantly reduces the formation of charge transfer complexes, enhancing both transparency and thermal properties. Non-coplanar structures, such as spiro and cardo configurations, further improve the optical properties while maintaining thermal stability. Future research trends include nanoparticle doping, intrinsic microporous PI polymers, photosensitive polyimides, machine learning-assisted molecular design, and metal coating techniques, which are expected to further enhance the comprehensive optical and thermal performance of PI films and expand their applications in flexible displays, solar cells, and high-performance electronic devices. Overall, systematic molecular design and optimization have significantly improved the optical and thermal performance of PI films, showing broad application prospects. This review aims to provide researchers with valuable references, stimulate more innovative research and applications, and promote the deep integration of PI films into modern technology and industry. Polyimide ; Materials science ; Microporous material ; Transparency (behavior) ; Thermal stability ; Nanotechnology ; Doping ; Coating ; Thermal ; Optoelectronics ; Optical transparency ; Thermal resistance ; Computer science ; Composite material ; Layer (electronics) ; Chemistry ; Organic chemistry Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Tribology and Wear Analysis
Authors: Krzysztof Maziarz | Austin Tripp | Guoqing Liu | Megan Stanley | Shufang Xie | Piotr Gaiński | Philipp Seidl | Marwin Segler
Source: Faraday Discussions (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Re-evaluating retrosynthesis algorithms with Syntheseus Automated synthesis planning has recently re-emerged as a research area at the intersection of chemistry and machine learning. Despite the appearance of steady progress, we argue that imperfect benchmarks and inconsistent comparisons mask systematic shortcomings of existing techniques, and unnecessarily hamper progress. To remedy this, we present a synthesis planning library with an extensive benchmarking framework, called SYNTHESEUS, which promotes best practice by default, enabling consistent meaningful evaluation of single-step and multi-step synthesis planning algorithms. We demonstrate the capabilities of SYNTHESEUS by re-evaluating several previous retrosynthesis algorithms, and find that the ranking of state-of-the-art models changes in controlled evaluation experiments. We end with guidance for future works in this area, and call on the community to engage in the discussion on how to improve benchmarks for synthesis planning. Retrosynthetic analysis ; Computer science ; Algorithm ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Machine Learning and Data Classification ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Guanjie Wang | Erpeng Wang | Zefeng Li | Jian Zhou | Zhimei Sun
Source: Interdisciplinary materials (article, 2024-05-29)
Field: Materials Science — Materials Chemistry

Exploring the mathematic equations behind the materials science data using interpretable symbolic regression Abstract Symbolic regression (SR), exploring mathematical expressions from a given data set to construct an interpretable model, emerges as a powerful computational technique with the potential to transform the “black box” machining learning methods into physical and chemistry interpretable expressions in material science research. In this review, the current advancements in SR are investigated, focusing on the underlying theories, fundamental flowcharts, various techniques, implemented codes, and application fields. More predominantly, the challenging issues and future opportunities in SR that should be overcome to unlock the full potential of SR in material design and research, including graphics processing unit acceleration and transfer learning algorithms, the trade‐off between expression accuracy and complexity, physical or chemistry interpretable SR with generative large language models, and multimodal SR methods, are discussed. Symbolic regression ; Computer science ; Construct (python library) ; Set (abstract data type) ; Graphics ; Artificial intelligence ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Caiyuan Ye | Hongming Weng | Quansheng Wu
Source: Computational Materials Today (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Con-CDVAE: A method for the conditional generation of crystal structures In recent years, progress has been made in generating new crystalline materials using generative machine learning models, though gaps remain in efficiently generating crystals based on target properties. This paper proposes the Con-CDVAE model, an extension of the Crystal Diffusion Variational Autoencoder (CDVAE), for conditional crystal generation. We introduce innovative components, design a two-step training method, and develop three unique generation strategies to enhance model performance. The effectiveness of Con-CDVAE is demonstrated through extensive testing under various conditions, including both single and combined property targets. Ablation studies further underscore the critical role of the new components in achieving our model’s performance. Additionally, we validate the physical credibility of the generated crystals through Density Functional Theory (DFT) calculations, confirming Con-CDVAE’s potential in material science research. Computer science ; Mathematics ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Platon P. Chebotaev | Andrey A. Buglak | Aimee Sheehan | Mikhail A. Filatov
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Predicting fluorescence to singlet oxygen generation quantum yield ratio for BODIPY dyes using QSPR and machine learning Functional dyes that are capable of both bright fluorescence and efficient singlet oxygen generation are crucial for theranostic techniques, which integrate fluorescence imaging and photodynamic therapy (PDT). The development of new functional dyes for theranostics is often costly and time-consuming due to laborious synthesis and post-synthetic screening of large libraries of compounds. In this work, we describe machine learning methods suitable for simultaneous prediction of fluorescence and photosensitizing ability of heavy-atom-free boron dipyrromethene (BODIPY) compounds. We analysed the ratio between fluorescence quantum yield (<i>Φ</i><sub>Fl</sub>) and singlet oxygen quantum yield (<i>Φ</i><sub>Δ</sub>) for over 70 BODIPY structures in polar (acetonitrile) and non-polar (toluene) solvents, which mimic hydrophilic and hydrophobic cell environments, respectively. QSPR models were developed based on more than 5000 calculated molecular descriptors, including quantum chemical and topological descriptors. We applied multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR) methods for model building and optimization. The resulting models demonstrated robust statistical parameters (<i>R</i><sup>2</sup> = 0.73-0.91) for both polar and non-polar media. The relative contributions of the descriptors to the models were assessed, identifying Eig03_EA(dm), F01[C-N], and TDB06p as the most influential. These results demonstrate that QSPR machine learning methods are effective in predicting key photochemical parameters of BODIPY photosensitizers, thereby potentially streamlining the development of theranostic agents. Singlet oxygen ; Quantum yield ; BODIPY ; Yield (engineering) ; Quantitative structure–activity relationship ; Fluorescence ; Photochemistry ; Chemistry ; Oxygen ; Singlet state ; Computational chemistry ; Materials science ; Excited state ; Organic chemistry ; Physics ; Stereochemistry ; Atomic physics ; Optics Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Advanced Fluorescence Microscopy Techniques
Authors: Anushka Bhaskar | Rama Chandra Muduli | Paresh Kale
Source: International Journal of Hydrogen Energy (article, 2024-12-14)
Field: Materials Science — Materials Chemistry

Prediction of hydrogen storage in metal hydrides and complex hydrides: A supervised machine learning approach Hydrogen storage ; Hydrogen ; Computer science ; Metal ; Machine learning ; Materials science ; Artificial intelligence ; Chemistry ; Metallurgy Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Nuclear Materials and Properties
Authors: Kaihua Zhang | Xiangrui Gong | Ying Jiang
Source: Advanced Functional Materials (article, 2024-01-31)
Field: Materials Science — Materials Chemistry

Machine Learning in Soft Matter: From Simulations to Experiments Abstract Soft matter with diverse functionalities that are easily designable has fascinated tremendous research interests in the past several decades. Nevertheless, the inherent confluence of time and length scale ubiquitous in soft matter immensely complicates the elucidation of the structure–property relationship and thereby severely impedes the function exploration of soft materials. Recently, the emergent machine learning (ML) techniques open new paradigms in property prediction and molecular design of functional materials, due to their extraordinarily distinguished performance in the aspect of trend identity and pattern extraction from data, and objective optimization by accelerating the guided search in high‐dimensional spaces. This review exclusively focuses on the current state‐of‐the‐art progress in the development of ML techniques applied in the realms of soft matter, ranging from coarse‐grained simulations to theoretical prediction on the structural formation and macroscopic properties, as well as the optimization and algorithm‐aided design in experiments. Finally, an outlook on the challenges and opportunities for this rapidly evolving field is discussed. Materials science ; Soft matter ; Soft robotics ; Nanotechnology ; Engineering physics ; Artificial intelligence ; Computer science ; Chemical engineering ; Physics ; Engineering ; Robot Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Thermodynamics and Statistical Mechanics
Authors: Zachary A. H. Goodwin | Malia B. Wenny | Julia H. Yang | Andrea Cepellotti | Jingxuan Ding | Kyle Bystrom | Blake Duschatko | Anders Johansson | Lixin Sun | Simon Batzner | Albert Musaelian | Jarad A. Mason | Boris Kozinsky | Nicola Molinari
Source: The Journal of Physical Chemistry Letters (article, 2024-07-18)
Field: Materials Science — Materials Chemistry

Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as "designer solvents" as they can be mixed to precisely tailor the physiochemical properties. As using machine learning interatomic potentials (MLIPs) to simulate ILs is still relatively unexplored, several questions need to be answered to see if MLIPs can be transformative for ILs. Since ILs are often not pure, but are either mixed together or contain additives, we first demonstrate that a MLIP can be trained to be compositionally transferable; i.e., the MLIP can be applied to mixtures of ions not directly trained on, while only being trained on a few mixtures of the same ions. We also investigated the accuracy of MLIPs for a novel IL, which we experimentally synthesize and characterize. Our MLIP trained on ∼200 DFT frames is in reasonable agreement with our experiments and DFT. Ionic liquid ; Transferability ; Ion ; Computer science ; Electrolyte ; Class (philosophy) ; Ionic bonding ; Materials science ; Chemical physics ; Chemistry ; Artificial intelligence ; Machine learning ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Ionic liquids properties and applications ; Electrochemical Analysis and Applications
Authors: Yongqiang Li | Liangfeng Chen | Siwei Yang | Genwang Wei | Xue Ping Ren | Anli Xu | Hang Wang | Peng He | Hui Dong | Gang Wang | Caichao Ye | Guqiao Ding
Source: Advanced Materials (article, 2024-02-14)
Field: Materials Science — Materials Chemistry

Symmetry‐Triggered Tunable Phosphorescence Lifetime of Graphene Quantum Dots in a Solid State Studying the phosphorescent mechanisms of carbon nanostructures synthesized by the "bottom-up" approach is key to understanding the structure modulation and the interfacial properties of carbon nanostructures. In this work, the relationships among symmetry of precursors in the "bottom-up" synthesis, structures of products, and phosphorescence lifetimes of graphene quantum dots (GQDs) are studied. The symmetry matching of precursors in the formation of a D<sub>6h</sub> graphene-like framework is considered the key factor in controlling the separability of sp<sup>2</sup> domains in GQDs. As the separability of sp<sup>2</sup> domains in GQDs increases, the phosphorescence lifetimes (14.8-125.5 ms) of GQDs in the solid state can be tuned. Machine learning is used to define the degree of disorder (S) of the GQD structure, which quantitatively describes the different space groups of precursors. The negative correlation between S and the oscillator strength of GQDs is uncovered. Therefore, S can be recognized as reflective of oscillator strength in the GQD structure. Finally, based on the correlations found between the structures and phosphorescence lifetimes of GQDs, GQDs with an ultralong phosphorescence lifetime (28.5 s) are obtained. Moreover, GQDs with visible phosphorescence emission (435-618 nm) are synthesized. Phosphorescence ; Materials science ; Graphene ; Quantum dot ; Oscillator strength ; Nanostructure ; Chemical physics ; Nanotechnology ; Photochemistry ; Physics ; Quantum mechanics ; Chemistry ; Fluorescence Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Nanocluster Synthesis and Applications