jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenyu Li | Zhitao Mao | Zhengyang Xiao | Xiao‐Ping Liao | Mattheos Koffas | Yixin Chen | Hongwu Ma | Yinjie Tang
Source: Trends in biotechnology (review, 2025-03-05)
Field: Materials Science — Materials Chemistry

Large language model for knowledge synthesis and AI-enhanced biomanufacturing Large language models (LLMs) are transforming synthetic biology (SynBio) education and research. In this review we cover the advancements and potential impacts of LLMs in biomanufacturing. First, we summarize recent developments and compare the capabilities of US and Chinese language models in addressing fundamental SynBio questions. Second, we discuss the application of LLMs in extracting SynBio information from unstructured data, constructing knowledge graphs, and enabling retrieval-augmented generation. Third, we anticipate that LLMs will not only revolutionize the design-build-test-learn (DBTL) cycle in metabolic modeling and engineering but also enable self-driving laboratories in future biomanufacturing. Finally, we emphasize the need for establishing benchmarks for LLMs, fostering trustworthy knowledge synthesis, developing biosecurity frameworks to prevent misuse, and encouraging collaboration among artificial intelligence (AI) scientists, SynBio researchers, and bioprocess engineers. Biomanufacturing ; Synthetic biology ; Computer science ; Computational biology ; Data science ; Biotechnology ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Evan Xie | Xijun Wang | J. Ilja Siepmann | Haoyuan Chen | Randall Q. Snurr
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Generative AI for design of nanoporous materials: review and future prospects Generative AI is reshaping the design of nanoporous materials by enabling rapid generation and screening of structures with desired properties. This review highlights capabilities, limitations, and practical use of AI for targeted material design. Nanoporous ; Generative grammar ; Generative Design ; Nanotechnology ; Computer science ; Systems engineering ; Engineering ; Materials science ; Artificial intelligence ; Chemical engineering Machine Learning in Materials Science ; Nanoporous metals and alloys ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Mylange Dona Kasundi Mekhala Gunasena | G. D. C. P. Galpaya | C. J. Abeygunawardena | Ashan Induranga | H. V. V. Priyadarshana | S. S. Millavithanachchi | Pramod Bandara | Kaveenga Rasika Koswattage
Source: Nanomaterials (review, 2025-03-31)
Field: Materials Science — Materials Chemistry

Advancements in Bio-Nanotechnology: Green Synthesis and Emerging Applications of Bio-Nanoparticles The field of bio-nanotechnology has seen significant advancements in recent years, particularly in the synthesis and application of bio-nanoparticles (BNPs). This review focuses on the green synthesis of BNPs using biological entities such as plants, bacteria, fungi, and algae. The utilization of these organisms for nanoparticle synthesis offers an eco-friendly and sustainable alternative to conventional chemical and physical methods, which often involve toxic reagents and high energy consumption. Phytochemicals present in plant extracts, unique metabolic pathways, and biomolecules in bacteria and fungi, and the rich biochemical composition of algae facilitate the production of nanoparticles with diverse shapes and sizes. This review further explores the wide-ranging applications of BNPs in various fields like therapeutics, fuel cells, energy generation, and wastewater treatment. In therapeutics, BNPs have shown efficacy in antimicrobial, anti-inflammatory, antioxidant, and anticancer activities. In the energy sector, BNPs are being integrated into fuel cells and other energy generation systems like bio-diesel to improve efficiency and sustainability. Their catalytic properties and large surface area enhance the performance of these devices. Wastewater treatment is another critical area where BNPs are employed for the removal of heavy metals, organic pollutants, and microbial contaminants, offering a cost-effective and environmentally friendly solution to water purification. This comprehensive review highlights the potential of bio-nanoparticles synthesized through green methods. It highlights the need for further research to optimize synthesis processes, understand mechanisms of action, and expand the scope of their applications. BNPs can be utilized to address advantages and some of the pressing challenges in medicine, energy, and environmental sustainability, paving the way for innovative and sustainable technological advancements in future prospects. Nanotechnology ; Environmentally friendly ; Chemistry ; Biochemical engineering ; Materials science ; Engineering ; Biology Nanoparticles: synthesis and applications
Authors: Chenguang Fu | Mouyang Cheng | Nguyen Tuan Hung | Eunbi Rha | Zhantao Chen | Ryotaro Okabe | Denisse Córdova Carrizales | Manasi Mandal | Yongqiang Cheng | Mingda Li
Source: Advanced Materials (review, 2025-06-23)
Field: Materials Science — Materials Chemistry

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the "curse of dimensionality". This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials. Thermoelectric materials ; Materials science ; Thermoelectric effect ; Curse of dimensionality ; Seebeck coefficient ; Multiscale modeling ; Artificial intelligence ; Material Design ; Nanotechnology ; Computer science ; Thermal conductivity ; Mechanical engineering ; Machine learning ; Engineering physics ; Engineering Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Zheng Tang | Zhongliang Dong | Ling Yuan | Bowen Li | Yinlong Zhu
Source: EES Catalysis (article, 2025-01-01)
Field: Chemistry — Electrochemistry

Unlocking the potential: key roles of interfacial water in electrocatalysis This review presents a comprehensive overview of interfacial water in electrocatalysis. Electrocatalyst ; Key (lock) ; Chemistry ; Computer science ; Computer security ; Electrochemistry ; Electrode Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: Inhwan Lee | Luyao Ma
Source: Food Innovation and Advances (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Integrating machine learning, optical sensors, and robotics for advanced food quality assessment and food processing Machine learning, in combination with optical sensing, extracts key features from high-dimensional data for non-destructive food quality assessment. This approach overcomes the limitations of traditional destructive and labor-intensive methods, facilitating real-time decision-making for food quality profiling and robotic handling. This mini-review highlights various optical techniques integrated with machine learning for assessing food quality, including chemical profiling methods such as near-infrared, Raman, and hyperspectral imaging spectroscopy, as well as visual analysis such as RGB imaging. In addition, the review presents the application of robotics and computer vision techniques to assess food quality and then drives the automation of food harvesting, grading, and processing. Lastly, the review discusses current challenges and opportunities for future research. Robotics ; Artificial intelligence ; Food quality ; Quality (philosophy) ; Quality assessment ; Computer science ; Food processing ; Optical sensing ; Machine learning ; Engineering ; Food science ; Engineering management ; Materials science ; Robot ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Geng Yin | Haiyan Zhu | Shanlin Chen | Tingting Li | Chou Wu | Shaobo Jia | Jingzhi Shang | Zhifeng Ren | Tianhao Ding | Yawei Li
Source: Molecules (review, 2025-02-07)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction Hydrogen as an environmentally friendly energy carrier, has many significant advantages, such as cleanliness, recyclability, and high calorific value of combustion, which makes it one of the major potential sources of energy supply in the future. Hydrogen evolution reaction (HER) is an important strategy to cope with the global energy shortage and environmental degradation, and given the large cost involved in HER, it is crucial to screen and develop stable and efficient catalysts. Compared with the traditional catalyst development model, the rapid development of data science and technology, especially machine learning technology, has shown great potential in the field of catalyst development in recent years. Among them, the research method of combining high-throughput computing and machine learning has received extensive attention in the field of materials science. Therefore, this paper provides a review of the recent research on combining high-throughput computing with machine learning to guide the development of HER electrocatalysts, covering the application of machine learning in constructing prediction models and extracting key features of catalytic activity. The future challenges and development directions of this field are also prospected, aiming to provide useful references and lessons for related research. Throughput ; Economic shortage ; Computer science ; Environmentally friendly ; Biochemical engineering ; Field (mathematics) ; Artificial intelligence ; Nanotechnology ; Machine learning ; Process engineering ; Risk analysis (engineering) ; Materials science ; Engineering ; Telecommunications ; Business ; Wireless Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Jonathan A. Semelak | Ignacio Pickering | Kate Huddleston | Justo Olmos | Juan Santiago Grassano | Camila M. Clemente | Salvador I. Drusin | Marcelo A. Martí | Mariano C. González Lebrero | Adrián E. Roitberg | Darío A. Estrı́n
Source: Journal of Chemical Theory and Computation (article, 2025-03-04)
Field: Materials Science — Materials Chemistry

Advancing Multiscale Molecular Modeling with Machine Learning-Derived Electrostatics We introduce an innovative machine learning (ML)-based framework for multiscale molecular modeling in which the ML subsystem is treated as an electrostatic entity interacting with its molecular mechanics (MM) environment through classical electrostatics. The integration of ML accuracy with multiscale modeling is accomplished by leveraging the capabilities of the ANI neural networks to predict geometry-dependent atomic partial charges at the minimal basis iterative stockholder (MBIS) level, going beyond static mechanical embedding. This ML/MM approach can closely approximate state-of-the-art multiscale quantum-classical (QM/MM) methods while significantly lowering computational requirements, thereby facilitating more efficient and precise simulations in computational chemistry. The method requires no additional training beyond the initial model setup and is integrated into Amber, one of the most widely used software suites for molecular modeling, ensuring accessibility to the broader community. We validate its performance across a variety of challenging applications, including the solvation structure, vibrational spectra, torsion free energy profiles, and protein-ligand interactions, achieving excellent agreement with QM/MM benchmarks. This framework not only advances the frontiers of multiscale modeling but also showcases the potential of machine learning to achieve quantum-level accuracy with exceptional efficiency for complex chemical systems. Computer science ; Electrostatics ; Embedding ; Multiscale modeling ; Software ; QM/MM ; Quantum ; Solvation ; Molecular dynamics ; Artificial intelligence ; Computational science ; Machine learning ; Physics ; Chemistry ; Computational chemistry ; Ion ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Martin Nováček | Jan Řezáč
Source: Journal of Chemical Theory and Computation (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a Δ-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a Δ-ML method that synergizes the semiempirical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML potential applied as a universal correction. The method demonstrates superior performance over standalone SQM and ML approaches and covers a broader chemical space than its predecessors. It is scalable to systems with thousands of atoms, which makes it applicable to large biomolecular systems. Extensive benchmarking confirms PM6-ML's accuracy and robustness. Its practical application is facilitated by a direct interface to MOPAC. The code and parameters are available at https://github.com/Honza-R/mopac-ml. Transferability ; Robustness (evolution) ; Benchmarking ; Computer science ; Scalability ; Quantum chemistry ; Quantum chemical ; Quantum ; Computational science ; Machine learning ; Artificial intelligence ; Algorithm ; Chemistry ; Molecule ; Quantum mechanics ; Physics ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yingli Liu | Yuting Cui | Haihe Zhou | Tao Shen | Lei Sheng | Haibin Yuan | Jiancheng Yin
Source: Computers, materials & continua/Computers, materials & continua (Print) (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Methods for Materials Inverse Design: A Review Finding materials with specific properties is a hot topic in materials science. Traditional materials design relies on empirical and trial-and-error methods, requiring extensive experiments and time, resulting in high costs. ... | Find, read and cite all the research you need on Tech Science Press Computer science ; Inverse ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Additive Manufacturing and 3D Printing Technologies
Authors: P. Soumya | Prachi Prachi | Chhaya Ravi Kant | Ranjana Jha
Source: Journal of Alloys and Compounds (article, 2025-05-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metal-air batteries: From fundamental mechanisms to practical applications Materials science ; Nanotechnology Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Louise A. M. Rosset | D. A. Drabold | Volker L. Deringer
Source: Nature Communications (article, 2025-03-10)
Field: Materials Science — Materials Chemistry

Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics The structure of amorphous silicon has been studied for decades. The two main theories are based on a continuous random network and on a 'paracrystalline' model, respectively-the latter defined as showing localized structural order resembling the crystalline state whilst retaining an overall amorphous network. However, the extent of this local order has been unclear, and experimental data have led to conflicting interpretations. Here we show that signatures of paracrystallinity in an otherwise disordered network are indeed compatible with experimental observations for amorphous silicon. We use quantum-mechanically accurate, machine-learning-driven simulations to systematically sample the configurational space of quenched silicon, thereby allowing us to elucidate the boundary between amorphization and crystallization. We analyze our dataset using structural and local-energy descriptors to show that paracrystalline models are consistent with experiments in both regards. Our work provides a unified explanation for seemingly conflicting theories in one of the most widely studied amorphous networks. Paracrystalline ; Amorphous solid ; Amorphous silicon ; Silicon ; Crystallization ; Materials science ; Chemical physics ; Statistical physics ; Molecular dynamics ; Computer science ; Nanotechnology ; Physics ; Crystalline silicon ; Chemistry ; Crystallography ; Thermodynamics ; Optoelectronics Machine Learning in Materials Science ; Thin-Film Transistor Technologies ; Advanced Electron Microscopy Techniques and Applications
Authors: Jinzhe Zeng | Timothy J. Giese | Duo Zhang | Handong Wang | Darrin M. York
Source: Journal of Chemical Information and Modeling (article, 2025-03-28)
Field: Materials Science — Materials Chemistry

DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials Machine learning potentials (MLPs) have revolutionized molecular simulation by providing efficient and accurate models for predicting atomic interactions. MLPs continue to advance and have had profound impact in applications that include drug discovery, enzyme catalysis, and materials design. The current landscape of MLP software presents challenges due to the limited interoperability between packages, which can lead to inconsistent benchmarking practices and necessitates separate interfaces with molecular dynamics (MD) software. To address these issues, we present DeePMD-GNN, a plugin for the DeePMD-kit framework that extends its capabilities to support external graph neural network (GNN) potentials.DeePMD-GNN enables the seamless integration of popular GNN-based models, such as NequIP and MACE, within the DeePMD-kit ecosystem. Furthermore, the new software infrastructure allows GNN models to be used within combined quantum mechanical/molecular mechanical (QM/MM) applications using the range corrected ΔMLP formalism.We demonstrate the application of DeePMD-GNN by performing benchmark calculations of NequIP, MACE, and DPA-2 models developed under consistent training conditions to ensure fair comparison. Plug-in ; Computer science ; Graph ; Artificial neural network ; Artificial intelligence ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xiaojie Wu | Qiming Sun | Zhichen Pu | Tianze Zheng | Wenzhi Ma | Wen Yan | Yu Xia | Zhengxiao Wu | Mian Huo | Xiang Li | Weiluo Ren | Sheng Gong | Yumin Zhang | Weihao Gao
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Enhancing <scp>GPU</scp>‐Acceleration in the Python‐Based Simulations of Chemistry Frameworks ABSTRACT We describe our contribution as industrial stakeholders to the existing open‐source GPU4PySCF project ( https://github.com/pyscf/gpu4pyscf ), a GPU‐accelerated Python quantum chemistry package. We have integrated GPU acceleration into other PySCF functionalities including Density Functional Theory (DFT), geometry optimization, frequency analysis, solvent models, and the density fitting technique. Through these contributions, GPU4PySCF v1.0 can now be regarded as a fully functional and industrially relevant platform, which we demonstrate in this work through a range of tests. When performing DFT calculations with the density fitting scheme on modern GPU platforms, GPU4PySCF delivers a 30 times speedup over a 32‐core CPU node, resulting in approximately 90% cost savings for most DFT tasks. The performance advantages and productivity improvements have been found in multiple industrial applications, such as generating potential energy surfaces, analyzing molecular properties, calculating solvation free energy, identifying chemical reactions in lithium‐ion batteries, and accelerating neural‐network methods. With the improved design that makes it easy to integrate with the Python and PySCF ecosystem, GPU4PySCF is a natural choice that we can now recommend for many industrial quantum chemistry applications. Python (programming language) ; Acceleration ; Computer science ; Computational science ; Chemistry ; Programming language ; Physics Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Ana Rebeka Kamšek | Francisco Ruiz‐Zepeda | Marjan Bele | Anja Logar | Goran Dražić | Nejc Hodnik
Source: ACS Nano (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Structure–Stability Relationships in Pt-Alloy Nanoparticles Using Identical-Location Four-Dimensional Scanning Transmission Electron Microscopy and Unsupervised Machine Learning Nanoparticulate electrocatalysts for the oxygen reduction reaction are structurally diverse materials. Scanning transmission electron microscopy (STEM) has long been the go-to tool to obtain high-quality information about their nanoscale structure. More recently, its four-dimensional modality has emerged as a tool for a comprehensive crystal structure analysis using large data sets of diffraction patterns. In this study, we track the alternations of the crystal structure of individual carbon-supported PtCu<sub>3</sub> nanoparticles before and after fuel cell-relevant activation treatment, consisting of a mild acid-washing protocol and potential cycling, essential for forming an active catalyst. To take full advantage of the rich, identical location 4D-STEM capabilities, unsupervised algorithms were used for the complex data analysis, starting with <i>k</i>-means clustering followed by non-negative matrix factorization, to find commonly occurring signals within specific nanoparticle data. The study revealed domains with (partially) ordered alloy structures, twin boundaries, and local amorphization. After activation, specific nanoparticle surface sites exhibited a loss of crystallinity which can be correlated to the simultaneous local scarcity of the ordered alloy phase, confirming the enhanced stability of the ordered alloy during potential cycling activation conditions. With the capabilities of our in-house developed identical-location 4D-STEM approach to track changes in individual nanoparticles, combined with advanced data analysis, we determine how activation treatment affects the electrocatalysts' local crystal structure. Such an approach provides considerably richer insights and is much more sensitive to minor changes than traditional STEM imaging. This workflow requires little manual input, has a reasonable computational complexity, and is transferrable to other functional nanomaterials. Scanning transmission electron microscopy ; Materials science ; Nanoparticle ; Crystallinity ; Alloy ; Nanotechnology ; Transmission electron microscopy ; Composite material Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications