jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Brianna
R. Farris (13230811) | Kevin C. Leonard (379075)
Source: Figshare (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Accelerating CatalysisUnderstanding via Large LanguageModel Data Extraction and Shallow Machine Learning Techniques Catalysis is inherently complex. The lack of precise knowledge available to experimental researchers about the microenvironment, catalytic sites, mechanisms, and changes that occur under reaction conditions has hindered the effectiveness of deep-learning artificial intelligence algorithms to predict catalyst behavior under reaction conditions. Given the type and quality of data available in the scientific literature, there are still open questions on how machine learning can be used by experimentalists working in the field of catalysis to accelerate catalyst design. Here, we present a framework that leverages large language models to extract textual data from known and trusted sources to automatically generate large, but relatively low-fidelity, experimental catalysis data sets across many research groups. We also show that instead of using deep-learning models, which require high-quality data, shallow learning models with posthoc interpretability can extract valuable information about experimental catalytic systems from these low-fidelity data sets. The innovation of this work lies not in the model development but in the prompt engineering, data encoding, and question architectures employed to extract meaningful information. We applied this framework to two different model reactions: the electrocatalytic reduction of carbon dioxide and the electrocatalytic oxygen reduction reaction. We showcase that this framework has the ability to uncover known and established facts within the catalysis community, such as the catalytic properties of Cu, as well as novel insights, including the critical role of voltages above a certain threshold in producing multicarbon products from CO<sub>2</sub>. We anticipate that this proposed framework will serve as an entryway for experimental catalytic researchers to utilize machine learning to rapidly process literature, generate novel hypotheses, and design experiments to accelerate catalyst development. Interpretability ; Catalysis ; Field (mathematics) ; Quality (philosophy) ; Experimental data ; Work (physics) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Mitsugu Araki | Shigeyuki Matsumoto | Biao Ma | Kei Terayama | Yasushi Okuno
Source: Biophysical Journal (article, 2025-02-01)
Field: Chemistry — Spectroscopy

BPS2025 – Deep-learning approaches for protein dynamics extraction from cryo-EM maps and structure-based de novo molecular generation Molecular dynamics ; Computational biology ; Deep learning ; Extraction (chemistry) ; Dynamics (music) ; Chemistry ; Artificial intelligence ; Computer science ; Biology ; Physics ; Computational chemistry ; Chromatography Advanced NMR Techniques and Applications
Authors: Yongxian Wu | Qiang Zhu | Ray Luo
Source: Journal of Chemical Theory and Computation (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

End-to-End Modeling of Reaction Field Energy Using Data-Driven Geometric Graph Neural Networks Electrostatic interactions are fundamental to the structure, dynamics, and function of biomolecules, with broad applications in protein-ligand binding, enzymatic catalysis, and nucleic acid regulation. The Poisson-Boltzmann (PB) equation provides a physically grounded framework for modeling these interactions. However, solving the PB equation for large and complex biomolecular systems remains computationally expensive as traditional numerical solvers scale poorly with system size. While the Generalized Born (GB) model offers a more computationally efficient approximation, it does so at the cost of reduced accuracy relative to full PB solutions. To overcome these limitations, we propose PBGNN, a novel end-to-end framework that uses data-driven geometric graph neural networks to directly approximate PB electrostatic energies without relying on the GB approximation. PBGNN incorporates sinusoidal embeddings of atomic charges and a message-passing architecture to efficiently capture long-range interactions in large biomolecules. To address training instability caused by high variance in atomic electrostatic potentials, we introduce a charge-weighted mean squared error (CMSE) optimization objective that improves convergence. We benchmark PBGNN on the AMBER PBSA suite and PBSMALL, a new dataset designed for rapid evaluation of small-molecule electrostatics in drug discovery contexts. The results demonstrate that PBGNN consistently achieves high accuracy in predicting the PB energy with linear computational complexity. Furthermore, it provides reliable and precise PB free energy predictions for both large biomolecular complexes and small-molecule datasets, showcasing its strong generalizability, scalability, and potential utility in drug discovery tasks requiring accurate electrostatic modeling of small molecules. Comprehensive ablation studies further reveal the impact of architectural components, such as geometric representation, objective design, and cutoff strategy, informing future research directions. Finally, we release PBGNN as an open-source, self-contained codebase, along with preprocessed datasets and a complete training and evaluation pipeline, to support scalable and accurate electrostatic analysis that facilitates future research in associated areas. Benchmark (surveying) ; Electrostatics ; Suite ; Artificial neural network ; Graph ; Field (mathematics) ; Energy minimization ; Function (biology) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Nauman Bashir Bhatti | Dylan Young | Wilfred Lam | Rachel W. Chan | Pejman Maralani | Arjun Sahgal | Hany Soliman | Greg J. Stanisz | Ali Sadeghi‐Naini
Source: International Journal of Radiation Oncology*Biology*Physics (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Attention-Guided Deep Learning of Chemical Exchange Saturation Transfer Magnetic Resonance Imaging to Differentiate Between Tumor Progression and Radiation Necrosis in Brain Metastasis Attention-guided deep-learning analysis of chemical exchange saturation transfer MRI shows strong potential for accurately distinguishing RN from TP, underscoring the significance of multimodal MRI inputs for post-SRS lesion evaluation. Magnetic resonance imaging ; Saturation (graph theory) ; Lesion ; Tumor progression ; Metastasis ; Brain tumor Lanthanide and Transition Metal Complexes ; Magnetism in coordination complexes ; MRI in cancer diagnosis
Authors: Yu Yan
Source: Scientific Reports (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Multi-feature deep learning framework for predicting CO adsorption mechanisms at metal oxide interfaces: a transformer-based approach This study presents a novel multi-feature deep learning framework that integrates Transformer architecture with readily computable molecular descriptors to predict CO adsorption mechanisms at single metal oxide interfaces. The framework employs specialized encoders for structural, electronic, and kinetic descriptors, utilizing cross-feature attention mechanisms to capture the multifaceted nature of catalytic processes. Unlike traditional approaches requiring expensive DFT calculations, our method uses empirical descriptors and pre-computed parameters that can be rapidly obtained for practical catalyst screening applications. Comprehensive evaluation across seven distinct metal oxide systems demonstrates superior performance over traditional machine learning methods, achieving mean absolute errors below 0.12 eV for adsorption energy prediction and correlation coefficients exceeding 0.92. Systematic ablation studies reveal the hierarchical importance of different data modalities, with structural information providing the most critical contribution. Case studies on CeO₂, TiO₂, and ZnO validate the model's capability to distinguish material-specific mechanisms and provide mechanistic insights consistent with experimental observations. The multi-feature approach successfully predicts coverage-dependent effects, surface termination influences, and defect-mediated processes, establishing a foundation for data-driven catalyst design and mechanism elucidation in sustainable catalysis applications. Oxide ; Adsorption ; Deep learning ; Catalysis ; Mechanism (biology) ; Artificial neural network ; Metal Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Osman Nuri Öcalan | Onur Saraçoğlu
Source: Applied Fruit Science (article, 2025-12-04)
Field: Chemistry — Analytical Chemistry

Modeling Anthocyanin Content in Black Mulberry (Morus nigra) Fruits Using Machine Learning Based on Color and Chemical Properties Anthocyanin ; Random forest ; Support vector machine ; Mean squared error ; Content (measure theory) ; Perceptron ; Multilayer perceptron Spectroscopy and Chemometric Analyses ; Plant Gene Expression Analysis ; Smart Agriculture and AI
Authors: Jiaqi Wang (369548) | Sijun Huang (827305) | Fanxiang Meng (11143782) | Qiuting Huang (17718396) | Xue Xia (124699) | Weiqing Xu (1279959) | Yi Guo (35817) | Guokun Liu (1995766) | Weigang Wang (774885) | Ping Li (31981) | Shuping Xu (461976)
Source: Figshare (article, 2025-06-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecule-ResponsiveSERS Sensors for Urine Diagnosisof Kidney Diseases Enhanced by Neural Networks Early diagnosis of kidney disease is crucial for treatment and prognosis. Compared with kidney biopsy, a noninvasive urine-based diagnosis method of kidney disease can be more convenient and less painful for patients. Urine is closely associated with kidney disease, including nephritis, kidney failure, and kidney cancer. Since it contains various biomolecules, including small-molecule metabolites, proteins, and so on, urine is an appropriate sample for diagnosing and monitoring kidney disease progression. Herein, we developed a liquid biopsy method for diagnosing various kidney diseases based on a specific SERS sensing mode combined with neural network models, which allows an integrated response to multiple types of targets with a single probe and facilitates the detection of complex samples from multiple target groups. Compared to label-free SERS, this method relies on changes in the probe molecule, which facilitates the sensitivity of the assay. Two kinds of silver nanoparticle-casting films assist this method with the surface decoration of molecule-responsive Raman reporter molecules: <i>p</i>-mercaptobenzoic acid (MBA) and <i>p</i>-aminothiophenol (PATP). MBA responds to amino components in urine by SERS spectral changes caused by molecular polarizability. PATP illustrates the level of small-molecule metabolites in urine according to SERS changes resulting from the rate of the hot hole-catalyzed reactions. These interactions were evidenced by density functional theory and molecular docking simulations. Through these two SERS sensors, we acquired the SERS data sets of urine samples and established a classifier by incorporating neural network models, enabling the effective discrimination of healthy and kidney disease samples. The method is helpful for clinical validation and shows promise for use in long-term kidney disease monitoring programs. Kidney ; Urine ; Kidney disease ; Classifier (UML) ; Clinical diagnosis ; Artificial neural network Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Shancai Guo | Hu Q | Xu Zhang | Jinjun Zhao | Jianyuan Lin | Zhijian Jia | Teng Wang | Haili Wang | Chunjie Bai | Jinghua Chen
Source: Microchemical Journal (article, 2025-07-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of in situ gold nanorod synthesis via a combined RSM-BP neural network approach: Application to SERS detection of tetracycline In situ ; Nanorod ; Tetracycline ; Nanotechnology ; Artificial neural network ; Materials science ; Chemistry ; Computer science ; Organic chemistry ; Artificial intelligence ; Biochemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Water Quality Monitoring and Analysis
Authors: Valerii Chuiko | Paul W. Ayers | Ayers, Paul W.
Source: ArXiv.org (preprint, 2025-04-04)
Field: Materials Science — Materials Chemistry

Paying attention to long-range electron correlation: a size-independent deep-learning approach to predicting molecules' electronic energies from one- and two-electron integrals We propose a descriptor for molecular electronic structure that is based solely on the one- and two-electron integrals but is translationally, rotationally, and unitarily invariant. Then, directly exploiting size consistency, we train and fine tune a neural network to predict the energies of strongly-correlated systems, specifically hydrogen clusters. We use an attention mechanism to formulate a size-independent approach that uses and preserves size-consistency. Therefore, training on few-electron systems can guide predictions for systems with more electrons. Our results are more accurate than alternative geometry-based machine-learning models. Artificial neural network ; Electron ; Electronic structure ; Electronic systems ; Energy (signal processing) ; Mechanism (biology) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Electron Microscopy Techniques and Applications
Authors: G.B.G. de Souza | Steven Bennett Hancock | D. P. Landau | Yohannes Abate | Rodrigo de Almeida Paiva | V. B. Nascimento
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Revealing the role of ripples in phonon modes for MoS <sub>2</sub> and MoSe <sub>2</sub> : insights from molecular dynamics and machine learning Interatomic potentials for single-layer MoS<sub>2</sub> and MoSe<sub>2</sub> were developed by training an artificial neural network with a reference data set generated using density functional theory. High accuracy was obtained for the phonon dispersion as well as a direct correspondence of the predicted Raman and infrared (IR) active modes (between 100 cm<sup>-1</sup> and 600 cm<sup>-1</sup>) with molecular dynamics results. The ability to perform simulations with thousands of atoms allowed our system to accommodate spontaneous fluctuations in height and ripples propagating in the material. As a result of these ripples, our simulations suggest the existence of IR activity in the E' and modes with inverted polarization, as well as the presence of IR activity at frequencies close to E'' and , which are generally considered IR-inactive. The rippling of the system is expected to be responsible for breaking the 3-fold rotational symmetry around the <i>z</i>-axis of the material, resulting in non-trivial IR modes. Phonon ; Molecular dynamics ; Artificial neural network ; Density functional theory ; Set (abstract data type) ; Materials science ; Chemical physics ; Layer (electronics) ; Dynamics (music) ; Statistical physics ; Condensed matter physics ; Molecular physics ; Physics ; Computer science ; Nanotechnology ; Biological system ; Artificial intelligence ; Computational chemistry ; Chemistry ; Acoustics Machine Learning in Materials Science ; 2D Materials and Applications ; Organic and Molecular Conductors Research
Authors: Samrat Chakraborty
Source: ChemRxiv (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Classical Machine Learning vs Quantum Machine Learning in Computational Chemistry: Hype, Hope, and Horizons Quantum machine learning ; Quantum computer ; Quantum ; Quantum algorithm ; Field (mathematics) ; Scaling ; Quantum state ; Computational learning theory Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Andrew Rosen
Source: Angewandte Chemie International Edition (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Capturing the Complexities of Catalyst–Support Interactions with the Help of Machine Learning The structure of metal nanoparticles is central to their catalytic activity, but metal-support interactions are difficult to model via quantum-mechanical calculations. Using a machine-learned potential to model supported silver nanoparticles, it has been shown that the idealized nanoparticle shapes commonly invoked in the literature do not reflect experiments for diameters below 8 nm, as reported by Maxson and Szilvási. Feature (linguistics) ; Nanoparticle ; Key (lock) ; Set (abstract data type) Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Complex Network Analysis Techniques
Authors: Yongjun Kim | Yeonjoon Kim | Hyeonsu Kim | Sungwoo Kang | Jaewook Kim | Kyung-Hoon Lee | Wook Jeong | Won Jong Lee | Ho Ryu | Kyungwoo Kim | Woo Youn Kim
Source: Chemistry - A European Journal (article, 2025-05-07)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Based Design of Metallocene Catalysts for Controlled Olefin Copolymerization Polyolefins are versatile materials for various purposes, but their functionality should be fine-tuned for target applications including the mitigation of adverse environmental impacts. Producing such polymers with desired properties requires catalysts that can control polymerization at an atomistic level. However, complex reaction mechanisms and very limited experimental data make it difficult to design new efficient catalysts using conventional computational and data-driven approaches. Here, we present a pragmatic strategy based on data-efficient predictive models combined with a genetic algorithm to design new catalysts for controlled ethylene/hexene copolymerization. By deriving the chemically intuitive descriptors from the mechanistic analysis of the polymerization, we achieved the promising predictive models with small data applicable to various core structures and different experimental conditions, respectively. We screened catalysts through a virtual screening scheme combining a genetic algorithm and predictive models using chemically intuitive descriptors and considered their synthesizability through the manual inspections of experts. As a result, we successfully designed nine catalysts with desired comonomer ratios and diverse core structures. Comonomer ; Copolymer ; Metallocene ; Computer science ; Materials science ; Catalysis ; Polymerization ; Model predictive control ; Polymer ; Artificial intelligence ; Chemistry ; Organic chemistry ; Control (management) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organometallic Complex Synthesis and Catalysis
Authors: István Kolossváry | Robert J. Coffey
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-17)
Field: Materials Science — Materials Chemistry

Reinforced molecular dynamics: Physics-infused generative machine learning model explores CRBN activation process We propose a simple and practical machine learning-based desktop solution for modeling biologically relevant protein motions. We termed our technology reinforced molecular dynamics (rMD) combining MD trajectory data and free-energy (FE) map data to train a dual-loss function autoencoder network that can explore conformational space more efficiently than the underlying MD simulation. The key insight of rMD is that it effectively replaces the latent space with an FE map, thus infusing the autoencoder network with a physical context. The FE map is computed from an MD simulation over a low-dimensional collective variable space that captures some biological function. One can directly use then the FE map for example, to generate more protein structures in poorly sampled regions, follow paths on the FE map to explore conformational transitions, etc. The rMD technology is entirely self-contained, does not rely on any pre-trained model, and can be run on a single GPU desktop computer. We present our rMD computations in a key area of molecular-glue degraders aimed at a deeper understanding of the structural transition from open to closed conformations of CRBN. Process (computing) ; Generative grammar ; Dynamics (music) ; Generative model ; Molecular dynamics ; Computer science ; Cognitive science ; Artificial intelligence ; Physics ; Psychology ; Programming language ; Quantum mechanics Machine Learning in Materials Science
Authors: Mingyi Xue | Bojun Liu | Xuhui Huang
Source: ChemRxiv (article, 2025-10-07)
Field: Chemistry — Physical and Theoretical Chemistry

Machine Learning in Chemistry: A Data Centred, Hands-on Introductory Machine Learning Course for Undergraduate Students Context (archaeology) ; Artificial neural network ; Perspective (graphical) ; Chemistry education ; Reinforcement learning ; Property (philosophy) Various Chemistry Research Topics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Betinol, Isaiah
Source: Open Collections (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven approaches to interrogate selectivity and generality in asymmetric catalysis The pursuit of highly selective and broadly applicable catalytic transformations has driven decades of innovation in synthetic chemistry. However, the design of new catalytic systems and the a priori prediction of their performance remain elusive, relying heavily on empirical screening and chemical intuition. In recent years, the fields of machine learning and data science have offered powerful new paradigms for navigating such complex, high-dimensional problems. These data- driven approaches hold the promise of uncovering subtle structure-activity relationships that elude human intuition, leading to predictive models that pose to greatly accelerate catalyst design and reaction optimization. This thesis details the development and application of tailored machine learning frameworks to interrogate the fundamental principles governing selectivity in asymmetric catalysis, with a focus on providing actionable insights for experimental chemists. The first section of this thesis deals with expanding the use of techniques based on Linear Free Energy Relationships (LFERs), specifically demonstrating how these models can be leveraged by experimentalists to aid in target synthesis campaigns and how simple classifiers can be used to accelerate mechanistic interpretation. I further show that the identification of LFERs themselves can be a powerful approach to generate new mechanistic hypotheses from chemically heterogenous datasets. Next, I establish principles for optimal data collection in data-limited scenarios, demonstrating that a target-aware protocol is more efficient for building predictive models than maximizing dataset diversity. Finally, I propose a workflow built on unsupervised machine learning to quantify generality, the ability of catalysts to selectively catalyze diverse reactions. I show that this data-driven approach can streamline the identification of general catalysts, a process typically requiring decades of experimental effort, in addition to accelerating reaction optimization. Overall, this thesis aims to bridge the gap between data-driven methodologies and experimental insights, providing researchers with predictive tools to make catalyst and reaction design a more rational and efficient process. Generality ; Workflow ; A priori and a posteriori ; Identification (biology) ; Relevance (law) ; Focus (optics) ; Simple (philosophy) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis