jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ashwini Verma | Kavita Joshi
Source: iScience (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

MH-PCTpro: A machine learning model for rapid prediction of pressure-composition-temperature (PCT) isotherms We present a machine-learning powered <b>M</b>etal <b>H</b>ydride's <b>P</b>ressure-<b>C</b>omposition-<b>T</b>emperature isotherm <b>Pr</b>edict<b>o</b>r <b>(MH-PCTpro)</b> for metal compositions. To train the MH-PCTpro, an experimental database of PCT isotherms is built from published literature. The database comprises over 14,000 data points extracted from 237 PCT isotherms representing 138 distinct compositions. The dataset encompasses more than 25 elements and spans a broad spectrum of absorption temperatures (263-653 K) and hydrogen pressures (0.001-40 MPa). The model is validated on a wide range of alloy families and its predictions are consistent with experimental results. The model also captures temperature-dependent variations in plateau pressure, enabling determination of enthalpy and entropy of hydride formation through Van't Hoff plots. Hence, MH-PCTpro can be used as an ML tool for guiding PCT experiments, offering PCT isotherm predictions and valuable thermodynamic insights into materials suitable for solid-state hydrogen storage. Composition (language) ; Thermodynamics ; Predictive modelling ; Chemistry ; Machine learning ; Computer science ; Philosophy ; Physics Thermal and Kinetic Analysis ; Machine Learning in Materials Science
Authors: Mojtaba Kordrostami | Ali Akbar Ghasemi-Soloklui
Source: Biochar (article, 2025-05-16)
Field: Chemistry — Inorganic Chemistry

Innovative applications of biochar in nuclear remediation and catalysis Abstract Emphasizing its potential for environmental sustainability, this review investigated how biochar—a carbon-rich material obtained from biomass pyrolysis—might be used into nuclear science and technology. High surface area, porosity, and functional groups give biochar special adsorption capacity, which qualifies it as a potential instrument for radiation cleanup and improving energy economy in nuclear uses. From the historical development of nuclear physics to the creative application of biochar in nuclear waste management and radiation shielding as well as its contribution to sustainable nuclear energy, the study covers several spheres. Biochar presents amazing efficiency in adsorbing and immobilizing radionuclides in the field of nuclear waste management, therefore establishing itself as a viable substitute for more traditional approaches. Its uses cover handling of high-level radioactive materials as well as treating low-level radioactive effluents. The paper also looks at using biochar as radiation shielding since its carbonaceous character produces strong, light-weight protective barriers. Using controlled pyrolysis and later changes, the paper addresses advanced manufacturing processes for customizing nuclear-grade biochar for particular uses. Within the nuclear industry, economic studies emphasize the affordability and possible financial gains of biochar, as well as its market potential and commercialization techniques. Lifetime analysis helps to evaluate environmental effects and sustainability by stressing the part of biochar in carbon sequestration and lowering of ecological footprints. The paper discusses safety and regulatory issues, how artificial intelligence and machine learning might be used for material optimization, and the limits and difficulties in using biochar. Practical case studies highlight its success in nuclear environments. The study ends by placing biochar as a major component in creating sustainable nuclear technology, which calls for continuous research, cooperation, and creativity. Graphical Abstract Biochar ; Environmental remediation ; Environmental science ; Waste management ; Chemistry ; Environmental chemistry ; Contamination ; Engineering ; Pyrolysis ; Biology Radioactive element chemistry and processing ; Graphite, nuclear technology, radiation studies ; Chemical Synthesis and Characterization
Authors: Sean D. Griesemer | Bianca Baldassarri | Ruijie Zhu | Jiahong Shen | Koushik Pal | Cheol Woo Park | Chris Wolverton
Source: Science Advances (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

Wide-ranging predictions of new stable compounds powered by recommendation engines The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed. We conduct a systematic comparison of the performance of previously developed recommendation engines, specifically ones based on elemental substitution, data mining, and neural network prediction of formation enthalpy. After identifying ways to improve the recommendation engines, we find the neural network to be superior at recommending stable Heusler compounds. Armed with improved recommendation engines, we identify tens of thousands of compounds that are stable at zero temperature and pressure, now available in the Open Quantum Materials Database. We summarize this diverse pool of compounds, including the elusive mixed anion compounds, and two of their many applications: thermoelectricity and solar thermochemical fuel production. Computer science ; Throughput ; Artificial neural network ; Search engine ; Density functional theory ; Space (punctuation) ; Ranging ; Biochemical engineering ; Chemistry ; Machine learning ; Information retrieval ; Computational chemistry ; Wireless ; Engineering Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Inorganic Chemistry and Materials
Authors: Lingjun Zeng | Yu Zhang | Xiaoping Mai | Ai Peng | Lan Xie | Bai Xue | Qiang Zheng
Source: Journal of Colloid and Interface Science (article, 2025-05-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Excellent absorption-dominant electromagnetic interference shielding performances of asymmetric gradient layered composite films exploited with assistance of machine learning Electromagnetic shielding ; EMI ; Electromagnetic interference ; Materials science ; Absorption (acoustics) ; Composite material ; Reflection loss ; Reflection coefficient ; Composite number ; Fabrication ; Computer science ; Optics ; Telecommunications ; Physics Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Compatibility and Measurements
Authors: Zhenhong Qi | Meiqi Dai | Yu‐Juan Ma | Dongpeng Yan
Source: Angewandte Chemie International Edition (article, 2025-11-02)
Field: Materials Science — Materials Chemistry

Artificial Decision Tree Guided Screening of Ultralong Room‐Temperature Phosphorescent Cocrystals with Locally Excited States High-throughput screening of ultralong room-temperature phosphorescence (RTP) systems is essential for advancing photofunctional materials. Among molecular solids, charge-transfer (CT) cocrystals offer great potential due to the wide range of electron donors and acceptors with tunable energy levels. However, realizing RTP in CT systems remains difficult, as intricate orbital interactions in donor-acceptor pairs often lead to spin-forbidden singlet-to-triplet transitions. To overcome this challenge, we developed an artificial-decision-tree method guided by theoretical calculations and introduced an energy-based descriptor (E) to streamline the design of RTP-active cocrystals. This descriptor enables CT-dominated singlet emission alongside locally excited triplet emission, effectively promoting intersystem crossing. Applying this strategy, we predicted and experimentally validated new cocrystals (TP&1,2-TFP and TP&1,4-TFP), which achieved RTP lifetimes up to 1.223 s, the longest reported among current CT cocrystals. We further demonstrated the versatility of this strategy by identifying additional RTP-active systems (TP&1,2-TCP and TP&1,4-TCP). Owing to their tunable emission and time-resolved RTP, these materials were successfully employed in digital and visual information encryption and time-gated optical logic gate applications. Therefore, this work presents a robust framework that combines machine learning-driven prediction with experimental validation, offering a systematic and efficient alternative to traditional trial-and-error methods in the discovery of luminescent materials. Phosphorescence ; Intersystem crossing ; Excited state ; Encryption ; Singlet fission ; Luminescence ; Range (aeronautics) Luminescence and Fluorescent Materials ; Luminescence Properties of Advanced Materials ; Crystallography and molecular interactions
Authors: Mahsa Sadat Razavi | Vali Rasouli Sharabiani | Mohammad Tahmasebi | Mariusz Szymanek
Source: Journal of Food Composition and Analysis (article, 2025-03-18)
Field: Chemistry — Analytical Chemistry

Unveiling the fingerprint of apple browning: A Vis/NIR-metaheuristic approach for rapid polyphenol oxidase and peroxidases activities detection in red delicious apples Browning ; Polyphenol oxidase ; Peroxidase ; Polyphenol ; Food science ; Fingerprint (computing) ; Chemistry ; Catechol oxidase ; Botany ; Biology ; Biochemistry ; Enzyme ; Antioxidant ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Meat and Animal Product Quality
Authors: Yunrui Li | Hao Xu | Ambrish Kumar | Duo‐Sheng Wang | Christian Heiß | Parastoo Azadi | Pengyu Hong
Source: Communications Chemistry (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

TransPeakNet for solvent-aware 2D NMR prediction via multi-task pre-training and unsupervised learning Nuclear Magnetic Resonance (NMR) spectroscopy is essential for revealing molecular structure, electronic environment, and dynamics. Accurate NMR shift prediction allows researchers to validate structures by comparing predicted and observed shifts. While Machine Learning (ML) has improved one-dimensional (1D) NMR shift prediction, predicting 2D NMR remains challenging due to limited annotated data. To address this, we introduce an unsupervised training framework for predicting cross-peaks in 2D NMR, specifically Heteronuclear Single Quantum Coherence (HSQC). Our approach pretrains an ML model on an annotated 1D dataset of <sup>1</sup>H and <sup>13</sup>C shifts, then finetunes it in an unsupervised manner using unlabeled HSQC data, which simultaneously generates cross-peak annotations. Our model also adjusts for solvent effects. Evaluation on 479 expert-annotated HSQC spectra demonstrates our model's superiority over traditional methods (ChemDraw and Mestrenova), achieving Mean Absolute Errors (MAEs) of 2.05 ppm and 0.165 ppm for <sup>13</sup>C shifts and <sup>1</sup>H shifts respectively. Our algorithmic annotations show a 95.21% concordance with experts' assignments, underscoring the approach's potential for structural elucidation in fields like organic chemistry, pharmaceuticals, and natural products. Task (project management) ; Computer science ; Training (meteorology) ; Artificial intelligence ; Machine learning ; Engineering ; Physics ; Systems engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Geping He | X. R. Li | Wenbo Zhou | Zhong Hua Huang | Huijun HuangFu | Lang Jv | Yuxia Qiao | Zhilei Li | Donghai Ding | Zongmo Shi | Quan Yang
Source: Journal of Energy Storage (article, 2025-05-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancements on the synthesis and modification of metal-organic framework derivatives for supercapacitors Supercapacitor ; Biochemical engineering ; Nanotechnology ; Engineering ; Chemistry ; Materials science ; Electrochemistry Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications ; Conducting polymers and applications
Authors: Nengzhuo Chou | Sen Lin | Fang Xu | Zuojuan Du | Jiawei Zhong | Guangyao Li | Ding Wen Bao | Guoping Wang | Yi Min Xie
Source: Acta Biomaterialia (article, 2025-02-26)
Field: Materials Science — Polymers and Plastics

Impact-absorbing helmet design inspired by walnut texture reaction-diffusion mechanisms Materials science ; Texture (cosmology) ; Diffusion ; Composite material ; Chemical engineering ; Thermodynamics ; Computer science ; Artificial intelligence ; Engineering Textile materials and evaluations ; Cellular and Composite Structures ; Advanced Technology in Applications
Authors: Junmin Chen | Qian Gao | Miaofei Huang | Kuang Yu
Source: Physical Chemistry Chemical Physics (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Application of modern artificial intelligence techniques in the development of organic molecular force fields The molecular force field (FF) determines the accuracy of molecular dynamics (MD) and is one of the major bottlenecks that limits the application of MD in molecular design. Recently, artificial intelligence (AI) techniques, such as machine-learning potentials (MLPs), have been rapidly reshaping the landscape of MD. Meanwhile, organic molecular systems feature unique characteristics, and require more careful treatment in both model construction, optimization, and validation. While an accurate and generic organic molecular force field is still missing, significant progress has been made with the facilitation of AI, warranting a promising future. In this review, we provide an overview of the various types of AI techniques used in molecular FF development and discuss both the advantages and weaknesses of these methodologies. We show how AI methods provide unprecedented capabilities in many tasks such as potential fitting, atom typification, and automatic optimization. Meanwhile, it is also worth noting that more efforts are needed to improve the transferability of the model, develop a more comprehensive database, and establish more standardized validation procedures. With these discussions, we hope to inspire more efforts to solve the existing problems, eventually leading to the birth of next-generation generic organic FFs. Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Materials science ; Computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Sourav Ghosh | Ashwath Sibi | G. Sudha Priyanga | Zaineb Chelly Dagdia | Tiju Thomas
Source: Journal of Energy Storage (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Temperature-dependent performance prediction for cerium oxynitride solid-state symmetric supercapacitor using machine learning Supercapacitor ; Cerium ; Materials science ; Solid-state ; Computer science ; Chemical engineering ; Chemistry ; Engineering ; Electrochemistry ; Physical chemistry ; Metallurgy ; Electrode MXene and MAX Phase Materials ; Advancements in Battery Materials ; Fuel Cells and Related Materials
Authors: Jiajun Li | Bin Zou | Amirbek Bekeshev | Marzhan Akhmetova | Raigul Orynbassar | Xin Wang | Yuan Hu
Source: Polymer Degradation and Stability (article, 2025-01-19)
Field: Materials Science — Polymers and Plastics

Practical machine learning model selection and interpretation for organophosphorus flame retardancy in Epoxy resin Epoxy ; Selection (genetic algorithm) ; Interpretation (philosophy) ; Materials science ; Composite material ; Polymer science ; Computer science ; Biochemical engineering ; Artificial intelligence ; Engineering Flame retardant materials and properties ; Epoxy Resin Curing Processes ; Photopolymerization techniques and applications
Authors: Mahmoud Thabet | Eman M. Abd El-Monaem | Mohamed A. Abdel‐Lateef | Tarek A. Seaf Elnasr | Ahmed Hamad Alanazi | Hazim M. Ali | Hassanien Gomaa
Source: Inorganic Chemistry Communications (article, 2025-02-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A crossbred mesoporous composite of Ni0.15-doped Zn0.85S @ alginate-derived graphitic carbon beads for methylene blue removal: DFT and machine learning investigations Methylene blue ; Composite number ; Mesoporous material ; Materials science ; Doping ; Chemical engineering ; Carbon fibers ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry ; Photocatalysis ; Optoelectronics ; Engineering ; Catalysis Supercapacitor Materials and Fabrication ; Gas Sensing Nanomaterials and Sensors ; Nanomaterials for catalytic reactions
Authors: Weiqing Zhou | Daye Zheng | Qianrui Liu | Denghui Lu | Yu Liu | Peize Lin | Yike Huang | Xing‐Liang Peng | Jie Bao | Chun Cai | Zuxin Jin | Jing Wu | Haochong Zhang | J. Gan | Yuyang Ji | Zheng Shen | Xiaohui Liu | Liang Sun | Yu Cao | Ming Sun | Jianchuan Liu | Tao Chen | Renxi Liu | Yuanbo Li | Haozhi Han | Xinyuan Liang | Taoni Bao | Z. Y. Deng | Tao Liu | Nuo Chen | Hongxu Ren | Xiaoyang Zhang | Zhao‐Qing Liu | Yiwei Fu | Maochang Liu | Zhuoyuan Li | Tongqi Wen | Zechen Tang | Yong Xu | Wenhui Duan | Xiaoyang Wang | Qiangqiang Gu | Fu‐Zhi Dai | Qijing Zheng | Yang Zhong | Hongjun Xiang | Xingao Gong | Jin Zhao | Yuzhi Zhang | Qi Ou | Hong Jiang | Shi Liu | Ben Xu | Shenzhen Xu | Xinguo Ren | Lixin He | Linfeng Zhang | Mohan Chen
Source: The Journal of Chemical Physics (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

ABACUS: An electronic structure analysis package for the AI era ABACUS (Atomic-orbital Based Ab initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and molecular dynamics functions and is compatible with both plane wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as deep potential with attention models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several artificial intelligence-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, HamGNN, etc. Abacus (architecture) ; Basis (linear algebra) ; Electronic structure ; Computation ; Software ; Ab initio Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Minaam Hussaini | Muhammad S. Vohra | Sagheer A. Onaizi
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2025-08-02)
Field: Chemistry — Inorganic Chemistry

Investigating the removal of Congo red dye using ZIF-8 and GQD composite: Characterization, kinetics, isotherm, thermodynamics, optimization, and machine learning studies Kinetics ; Characterization (materials science) ; Composite number ; Thermodynamics ; Congo red ; Materials science ; Chemistry ; Physical chemistry ; Nanotechnology ; Adsorption ; Physics ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Song He | Wenli Du | Xin Peng
Source: Chemical Engineering Journal (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Uncertainty-aware optimization of zeolite particle size: Utilizing quantile regression and SHAP analysis Quantile regression ; Quantile ; Zeolite ; Particle size ; Statistics ; Econometrics ; Regression analysis ; Regression ; Environmental science ; Computer science ; Mathematics ; Engineering ; Chemistry ; Chemical engineering Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Zeolite Catalysis and Synthesis
Authors: Peng Zheng | Steve Semancik | Ishan Barman
Source: Nano Letters (article, 2025-03-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning-Assisted SERS for Therapeutic Drug Monitoring of Clozapine in Serum on Plasmonic Metasurfaces Clozapine is widely regarded as one of the most effective therapeutics for treatment-resistant schizophrenia. Despite its proven efficacy, the therapeutic use of clozapine is complicated by its narrow therapeutic index, which necessitates rapid and precise therapeutic drug monitoring (TDM) to optimize patient outcomes and minimize adverse effects. However, conventional techniques, such as high-performance liquid chromatography, are limited by their high costs, complex instrumentation, and long turnaround times. Herein, we propose a novel approach that integrates artificial neural networks (ANNs) with surface-enhanced Raman spectroscopy (SERS) on a plasmonic metasurface for rapid TDM of clozapine and its two primary metabolites, norclozapine and clozapine-N-oxide, in human serum. The ANN-SERS strategy enables accurate classification and robust concentration prediction of the three analytes. We envision that the integrated ANN-SERS framework could deliver a scalable biomedical diagnostic and therapeutic tool for studying a wide variety of chemical and biological molecules in clinical settings. Plasmon ; Nanotechnology ; Drug ; Therapeutic drug monitoring ; Clozapine ; Materials science ; Plasmonic nanoparticles ; Chemistry ; Medicine ; Optoelectronics ; Pharmacology ; Schizophrenia (object-oriented programming) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced biosensing and bioanalysis techniques
Authors: Bichuan Cao | Jiawei Dong | Zedong Wang | Linjun Wang
Source: The Journal of Physical Chemistry Letters (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Large-Scale Non-Adiabatic Dynamics Simulation Based on Machine Learning Hamiltonian and Force Field: The Case of Charge Transport in Monolayer MoS<sub>2</sub> We present an efficient and reliable large-scale non-adiabatic dynamics simulation method based on machine learning Hamiltonian and force field. The quasi-diabatic Hamiltonian network (DHNet) is trained in the Wannier basis based on well-designed translation and rotation invariant structural descriptors, which can effectively capture both local and nonlocal environmental information. Using the representative two-dimensional transition metal dichalcogenide MoS<sub>2</sub> as an illustration, we show that density functional theory (DFT) calculations of only ten structures are sufficient to generate the training set for DHNet due to the high efficiency of Wannier analysis and orbital classification in sampling the interorbital couplings. DHNet demonstrates good transferability, thus enabling direct construction of the electronic Hamiltonian matrices for large systems. Compared with direct DFT calculations, DHNet significantly reduces the computational cost by about 5 orders of magnitude. By combining DHNet with the DeePMD machine learning force field, we successfully simulate electron transport in monolayer MoS<sub>2</sub> with up to 3675 atoms and 13475 electronic levels by using a state-of-the-art surface hopping method. The electron mobility is calculated to be 110 cm<sup>2</sup>/(V s), which is in good agreement with the extensive experimental results in the range of 3-200 cm<sup>2</sup>/(V s) during 2013-2023. Due to the high performance, the proposed DHNet and large-scale non-adiabatic dynamics methods have great potential to be applied to study charge carrier dynamics in a wide range of material systems. Adiabatic process ; Hamiltonian (control theory) ; Force field (fiction) ; Monolayer ; Scale (ratio) ; Statistical physics ; Molecular dynamics ; Charge (physics) ; Physics ; Computer science ; Materials science ; Nanotechnology ; Quantum mechanics ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Fuel Cells and Related Materials ; 2D Materials and Applications