jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sameen Raut | Joseph Dahlen | Thomas L. Eberhardt | Laurence R. Schimleck | Daniel M. Johnson | Kier D. Klepzig | Sudhagar Mani
Source: Journal of Near Infrared Spectroscopy (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Light gradient-boosting machine model provides biologically realistic predictions of southern pine wood extractives content using near infrared spectroscopy hyperspectral imaging compared to artificial neural network and partial least squares regression models Near infrared (NIR) spectroscopy combined with partial least squares (PLS) regression has been widely used to predict the physical, mechanical, and chemical properties of wood. Recently, advanced modeling techniques such as deep learning and machine learning models using spectral data have increasingly been used to predict variability in wood physical and mechanical properties, but studies on chemical properties are scarce. Herein, southern pine wood samples were acquired from multiple height levels from 119 trees to yield 1007 pith-to-bark radial strips. The strips were scanned on an NIR hyperspectral imaging system and the spectral data aligned to ring-level measurements. Acetone soluble extractives content was measured on 50-mm radial length samples with 262 data points assigned to training a PLS regression model, an artificial neural network (ANN) model, and a light gradient-boosting machine learning model (LGBM). The models were then evaluated on the test dataset (140 samples). The fit statistics were good for all models, although the LGBM model (RMSE = 2.1%) performed better than the PLS regression (3.3%) or ANN (2.6%) models. The models were subsequently used to predict extractives content for 31,314 rings. The LGBM model provided biologically realistic values for all rings, whereas the PLS regression model predicted 9648 negative values, and the ANN model extrapolated well beyond the measured values (max predicted = 69.4% vs max measured = 53.2%). This study highlights real benefits of using deep learning and machine learning models compared to PLS regression models for NIR hyperspectral imaging datasets. Hyperspectral imaging ; Partial least squares regression ; Boosting (machine learning) ; Artificial neural network ; Near-infrared spectroscopy ; Spectroscopy ; Biological system ; Chemometrics ; Remote sensing ; Content (measure theory) ; Environmental science ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics ; Optics ; Geology ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Wenjie Zhao | Yaling An | Christina Soyoung Song | Yang Huang | Lijie Zhang | Kezhuang Liu | Zhenwei Li | Xiao‐kang Liu | Dongping Zhang | De‐an Guo
Source: Phytochemical Analysis (article, 2025-05-29)
Field: Chemistry — Analytical Chemistry

ATR‐FTIR Coupled With Machine Learning Provides a Fast Method for Identifying and Distinguishing 55 Varieties of Fruit‐Derived Medicinal Materials The application of ATR-FTIR technology combined with the robust classification capabilities of ML models enabled rapid and accurate differentiation and identification of 55 FDMM, which contributed to ensuring their quality. Support vector machine ; Artificial intelligence ; Machine learning ; Naive Bayes classifier ; Kernel (algebra) ; Linear discriminant analysis ; Identification (biology) ; Pattern recognition (psychology) ; Artificial neural network ; Stability (learning theory) ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Bingkun Chen | Like Cao | Qijun Wang | Chunmin Cheng | Zijun Qi | Zhanpeng Sun | Lijie Li | Gai Wu | Wei Shen
Source: Journal of Physics D Applied Physics (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Predicting thermal conductivity of InP via molecular dynamics simulations with machine learning potential Abstract Indium Phosphide (InP) is a direct-gap semiconductor that has garnered significant attention in the field of optoelectronics, particularly in areas such as fiber optic communications and high-frequency devices. However, research on the thermal conductivity of bulk InP remains limited, with relatively few literature reports. In this work, a neuroevolution potential (NEP) is trained using a dataset constructed from ab initio molecular dynamics (AIMD), with the demonstrated high accuracy in simulating thermal transport properties. Subsequently, the thermal conductivity of zincblende-structured InP (ZB-InP) is obtained from 300 K to 900 K via equilibrium molecular dynamics (EMD) simulations, with the thermal conductivity curves exhibiting good agreement with the experimental data. The NEP offers higher accuracy than empirical potentials and consumes less computational resources than the density functional theory. Specifically, the thermal conductivity values for bulk InP at 300 K are κ [100] = 46.9 W·m −1 ·K −1 , κ [010] = 48.2 W·m −1 ·K −1 , and κ [001] = 46.1 W·m −1 ·K −1 , with an average thermal conductivity κ [ave] = 47.1 W·m −1 ·K −1 , and it is suggested that the thermal anisotropy is insignificant. Finally, it is indicated that an interatomic potential is developed to predict the thermal conductivity of bulk ZB-InP quickly and accurately in this work, which can further provide methods and data support for the subsequent study of the thermal properties of InP. Thermal conductivity ; Molecular dynamics ; Thermal ; Indium phosphide ; Interatomic potential ; Anisotropy ; Semiconductor Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: N. P. Bhatia | Patrick Rinke | Ondřej Krejčí
Source: npj Computational Materials (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Infrared (IR) spectroscopy is a pivotal analytical tool as it provides real-time molecular insight into material structures and enables the observation of reaction intermediates in situ. However, interpreting IR spectra often requires high-fidelity simulations, such as density functional theory based ab-initio molecular dynamics, which are computationally expensive and therefore limited in the tractable system size and complexity. In this work, we present a novel active learning-based framework, implemented in the open-source software package PALIRS, for efficiently predicting the IR spectra of small catalytically relevant organic molecules. PALIRS leverages active learning to train a machine-learned interatomic potential, which is then used for machine learning-assisted molecular dynamics simulations to calculate IR spectra. PALIRS reproduces IR spectra computed with ab-initio molecular dynamics accurately at a fraction of the computational cost. PALIRS further agrees well with available experimental data not only for IR peak positions but also for their amplitudes. This advancement with PALIRS enables high-throughput prediction of IR spectra, facilitating the exploration of larger and more intricate catalytic systems and aiding the identification of novel reaction pathways. Molecular dynamics ; Infrared spectroscopy ; Spectral line ; Infrared ; Interatomic potential ; Spectroscopy ; Software Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Noura Jariri | Mohammed Chaman | Rania Majdoubi | Abdelkader Hadjoudja
Source:  (article, 2025-02-19)
Field: Materials Science — Materials Chemistry

Evaluating HLS4ML For Converting a CNN Model Into High Level Synthesis Deep learning models, although high-performing, often require hardware acceleration to be effectively deployed on Field Programmable Gate Arrays (FPGAs). This paper investigates the use of the hls4ml framework to convert a Convolutional Neural Network (CNN) model into High Level Synthesis (HLS). hls4ml is a Python tool that translates neural networks into FPGA-compatible firmware for high-performance inference with reduced latency and power usage. It employs High-Level Synthesis (HLS) techniques to translate high-level programming languages like C++ or Python into hardware description languages, such as VHDL or Verilog. We evaluate the model’s performance in terms of accuracy and demonstrate the effectiveness of converting a deep learning model, specifically a CNN, for hardware implementation. The results highlight the potential of hls4ml facilitating embedded artificial intelligence applications, offering an efficient and scalable solution for deploying deep learning models on resource-constrained platforms. Computer science ; High-level synthesis ; Artificial intelligence ; Embedded system ; Field-programmable gate array Machine Learning in Materials Science
Authors: G.K. Liu | J. Y. Huang | Rui Hou | Ding Pan
Source: Chemical Physics Letters (article, 2025-03-14)
Field: Chemistry — Spectroscopy

First-principles machine-learning study of infrared spectra of methane under extreme pressure and temperature conditions Methane ; Infrared ; Infrared spectroscopy ; Spectral line ; Materials science ; Chemistry ; Environmental science ; Thermodynamics ; Physics ; Optics ; Organic chemistry ; Quantum mechanics Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Chika Oliver Ujah | Peter Apata Olubambi
Source: Advances in Materials Science and Engineering (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Exploring New Potential Applications of Carbon Nanomaterials Through Simulation This study systematically explores the computational investigation of carbon nanomaterials (CNMs), including carbon nanotubes, graphene, fullerenes, carbon nanofibers, quantum dots, and nanodiamonds for innovative utilizations. Dwelling on computational approaches such as molecular dynamics (MD), density functional theory (DFT), and finite element modeling (FEM), the review highlights the ability of these simulation methods to enhance innovative applications of CNMs in the medical sector for drug delivery, electronic, and biosensors; energy sector for supercapacitors, batteries, and fuel cells; and green environment for remediation adsorbents and catalysts. The combination of multiple simulation approaches and machine learning (ML) is crucial in optimizing the system’s ability to forecast innovations and speed up smart development of CNMs for improved sustainability. By outlining the correlation between atomic‐level modeling and practical world usage of CNMs, this research fills the gap between theoretical and practical nanotechnology. It reiterates the role of computational modeling in bringing down costs of laboratory experiments, enhancing material functionality, and providing robust decisions during the design of CNMs for specific applications. Model limitations, data combination, data scarcity, and lack of experimental validation were some of the challenges of computational modeling of CNMs discussed in the study. This comprehensive review presents a robust platform for scholars who intend to harvest the wholesome prospects of CNMs via modeling and simulation techniques. Computational model ; Nanomaterials ; Modeling and simulation ; Carbon fibers ; Molecular dynamics ; Efficient energy use Graphene research and applications ; Carbon Nanotubes in Composites ; Fullerene Chemistry and Applications
Authors: Sadegh Kaviani
Source: Energy storage materials (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Covalent organic framework-based solid polymer electrolytes for metal-ion batteries: pioneering the future of DFT, MD, and ML techniques Leverage (statistics) ; Polymer electrolytes ; Electrolyte ; Rational design ; Ionic liquid ; Battery (electricity) ; Energy storage ; Polymer ; Electrochemical energy storage Covalent Organic Framework Applications ; Advanced Battery Materials and Technologies ; Advanced battery technologies research
Authors: Hana Brožková | Julie Weisová | Antonín Hlaváček
Source: Langmuir (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

The Application of Anisotropically Collapsing Gels, Deep Learning, and Optical Microscopy for Chemical Characterization of Nanoparticles and Nanoplastics The surface chemistry of nanomaterials, particularly the density of functional groups, governs their behavior in applications such as bioanalysis, bioimaging, and environmental impact studies. Here, we report a precise method to quantify carboxyl groups per nanoparticle by combining anisotropically collapsing agarose gels for nanoparticle immobilization with fluorescence microscopy and acid-base titration. We applied this approach to photon-upconversion nanoparticles (UCNPs) coated with poly(acrylic acid) (PAA) and fluorescence-labeled polystyrene nanoparticles (PNs), which serve as models for bioimaging and environmental pollutants, respectively. UCNPs exhibited 152 ± 14 thousand carboxyl groups per particle (∼11 groups/nm<sup>2</sup>), while PNs were characterized with 38 ± 3.6 thousand groups (∼1.7 groups/nm<sup>2</sup>). The limit of detection was 6.4 and 1.9 thousand carboxyl groups per nanoparticle, and the limit of quantification was determined at 21 and 6.2 thousand carboxyl groups per nanoparticle for UCNP-PAAs and PNs, respectively. High intrinsic luminescence enabled direct imaging of UCNPs, while PNs required fluorescence staining with Nile Red to overcome low signal-to-noise ratios. The study also discussed the critical influence of nanoparticle concentration and titration conditions on the assay performance. This method advances the precise characterization of surface chemistry, offering insights into nanoparticle structure that extend beyond the resolution of electron microscopy. Our findings establish a robust platform for investigating the interplay of surface chemistry with nanoparticle function and fate in technological and environmental contexts, with broad applicability across nanomaterials. Nanoparticle ; Nanomaterials ; Fluorescence ; Chemistry ; Nanotechnology ; Bioanalysis ; Fluorescence microscope ; Titration ; Chemical engineering ; Materials science ; Organic chemistry Nanoparticles: synthesis and applications ; Nanoplatforms for cancer theranostics ; Optical Imaging and Spectroscopy Techniques
Authors: Lai Wei | Rongzhi Dong | Nihang Fu | Sadman Sadeed Omee | Jianjun Hu
Source: Computational Materials Science (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

TCSP 2.0: Template based crystal structure prediction with improved oxidation state prediction and chemistry heuristics Heuristics ; Template ; Embedding ; Similarity (geometry) ; Key (lock) ; Base (topology) ; Crystal structure prediction ; Selection (genetic algorithm) ; Voting Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Yuanyuan Xu | Hanchen Wang | Wenjie Zhang | Lexing Xie | Yin Chen | Flora D. Salim | Ying Zhang | J. Justin Gooding | Toby Walsh
Source: ArXiv.org (preprint, 2025-02-19)
Field: Materials Science — Materials Chemistry

AI-Empowered Catalyst Discovery: A Survey from Classical Machine Learning Approaches to Large Language Models Catalysts are essential for accelerating chemical reactions and enhancing selectivity, which is crucial for the sustainable production of energy, materials, and bioactive compounds. Catalyst discovery is fundamental yet challenging in computational chemistry and has garnered significant attention due to the promising performance of advanced Artificial Intelligence (AI) techniques. The development of Large Language Models (LLMs) notably accelerates progress in the discovery of both homogeneous and heterogeneous catalysts, where their chemical reactions differ significantly in material phases, temperature, dynamics, etc. However, there is currently no comprehensive survey that discusses the progress and latest developments in both areas, particularly with the application of LLM techniques. To address this gap, this paper presents a thorough and systematic survey of AI-empowered catalyst discovery, employing a unified and general categorization for homogeneous and heterogeneous catalysts. We examine the progress of AI-empowered catalyst discovery, highlighting their individual advantages and disadvantages, and discuss the challenges faced in this field. Furthermore, we suggest potential directions for future research from the perspective of computer science. Our goal is to assist researchers in computational chemistry, computer science, and related fields in easily tracking the latest advancements, providing a clear overview and roadmap of this area. We also organize and make accessible relevant resources, including article lists and datasets, in an open repository at https://github.com/LuckyGirl-XU/Awesome-Artificial-Intelligence-Empowered-Catalyst-Discovery. Computer science ; Artificial intelligence ; Data science Machine Learning in Materials Science ; Topic Modeling
Authors: J. A. Pérez | Marı́a M. Zanardi | Ariel M. Sarotti
Source: Journal of Chemical Information and Modeling (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Low-Cost, High-Accuracy Reactivity Modeling: Integrating Genetic Algorithms and Machine Learning with Multilevel DFT Calculations Accurate prediction of Gibbs activation energies (Δ<i>G</i><sup>‡</sup>) for Diels-Alder (DA) reactions remains a critical challenge in computational chemistry, as conventional density functional theory (DFT) methods often fail to consistently achieve chemical accuracy (<1 kcal mol<sup>-1</sup>). In this work, we demonstrate that no single method reliably meets this threshold through a systematic evaluation of 720 DFT approaches across 24 DA reactions. Here, we introduce a proof-of-concept framework that integrates a genetic algorithm and machine learning (GA-ML) to intelligently select cost-effective, multilevel DFT combinations. Our optimized GA1 model identified four low-cost DFT combinations that yield Δ<i>G</i><sup>‡</sup> predictions with a mean absolute error (MAE) of 0.4 kcal mol<sup>-1</sup> in both training and external validation sets, matching the accuracy of high-level CCSD(T) calculations at a fraction of the computational cost. To further enhance adaptability, we introduce Dynamic Generalization-Driven Transfer Learning (DGDTL), a novel method that adaptively optimizes linear coefficients, ensuring robust predictions for both known and unseen reactions. The integration of GA-ML and DGDTL establishes a scalable, high-fidelity paradigm for reactivity prediction, with potential applications in catalysis, drug design, and materials science. Matching (statistics) ; Density functional theory ; Genetic algorithm ; Fraction (chemistry) ; Training set ; Reactivity (psychology) ; Set (abstract data type) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Shokirbek Shermukhamedov | Dilorom Mamurjonova | Michael Probst
Source: APL Machine Learning (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Accurate classification of materials with elEmBERT: Element embeddings for chemical benchmarks We introduce the elEmBERT model for chemical classification tasks. It is based on deep learning techniques, such as a multilayer encoder architecture. We demonstrate the opportunities offered by our approach on sets of organic, inorganic, and crystalline compounds. In particular, we developed and tested the model using the Materials Project and MoleculeNet benchmarks, which include crystal properties and drug design-related benchmarks. We also conduct an analysis of vector representations of chemical compounds, shedding light on the underlying patterns in structural data. Our model exhibits exceptional predictive capabilities and proves universally applicable to molecular and material datasets. For instance, on the Tox21 dataset, we achieved an average precision of 96%, surpassing the previously best result by 10%. Element (criminal law) ; Computer science ; Political science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Shubin Liu
Source: Journal of Chemical Theory and Computation (review, 2025-10-09)
Field: Chemistry — Physical and Theoretical Chemistry

The Evolving Quest for Chemical Understanding in the Quantum Age As quantum mechanics enters its second century, theoretical and computational chemistry stands at a pivotal transition. Traditional orbital-based approaches such as valence bond theory and molecular orbital theory and density-based frameworks like density functional theory have long provided the computational and conceptual foundations for the field. However, the advent of machine learning and quantum computers introduces completely new paradigms for representation, inference, and understanding. In this perspective, we examine how chemical understanding has been evolving in the past century through the lenses of ontology, epistemology, and emergence. We argue that chemical concepts, such as aromaticity, electronegativity, reactivity, and stereoselectivity, are not merely reducible to basic laws of physics but emerge as essential scaffolds linking chemical theories to chemical understanding. We propose a general scheme to obtain chemical understanding from the basic variables of chemical theories. Extending this scheme to deep learning and quantum computing, we suggest roadmaps to harvest chemical understanding from them and then advocate for hierarchical modeling as a new platform that moves beyond the constraints of multiscale modeling. Hierarchical modeling integrates abstraction across scales, captures emergent behaviors, and enables conceptual innovation for hierarchical systems. We conclude that the future of chemical understanding depends less on solving harder physical equations alone and more on epistemological shift characterized by conceptual pluralism, epistemic adaptability, and deeper appreciation of the multilayered ontological structure inherent to molecular systems. Abstraction ; Quantum chemical ; Scheme (mathematics) ; Conceptual framework ; Quantum ; Chemical bond ; Density functional theory ; Valence (chemistry) ; Physical law Various Chemistry Research Topics ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Sepideh Akhbarifar | Mona Zebarjadi
Source: MRS Bulletin (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Innovation in thermoelectric materials: From fundamental physics to practical applications Thermoelectric effect ; Thermoelectric materials ; Engineering physics ; Nanotechnology ; Materials science ; Engineering ethics ; Physics ; Engineering ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Chalcogenide Semiconductor Thin Films