jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dave Pinxteren | Willem Jespers
Source: Journal of Chemical Information and Modeling (review, 2025-09-17)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning into Free Energy Perturbation Workflows Free energy perturbation (FEP) methods are among the most accurate tools in structure-based drug design for predicting protein-ligand binding affinities. However, their adoption remains limited due to high computational demands and complex setup procedures. This review explores how integrating machine learning (ML), especially active learning (AL) and deep learning (DL), can enhance the efficiency, accessibility, accuracy, and precision of FEP workflows. It examines three key areas where ML has been successfully applied: sampling strategies, protocol optimization, and force field development. AL algorithms can significantly reduce the number of FEP calculations needed during virtual screening by guiding the molecule selection. DL-based protein-ligand cofolding methods such as AlphaFold, NeuralPLexer, and DragonFold enable the automated generation of accurate complex structures for FEP, bypassing traditional docking and preparation steps. Additionally, ML-derived neural network potentials (NNPs), trained on quantum mechanical data, offer improved force field accuracy, although at the cost of higher computational expenses. This review emphasizes a hybrid approach combining human expertise with ML tools as the most promising strategy for accelerating and democratizing FEP-based drug discovery. Future developments in this interdisciplinary space are expected to expand the scope and impact of computer-aided drug design across pharmaceutical and materials science applications. Workflow ; Free energy perturbation ; Virtual screening ; Artificial neural network ; Force field (fiction) ; Scope (computer science) ; Field (mathematics) ; Deep learning Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Chandra Bhusan Chowdhury | Matteo Lovato | Giovanni Di Liberto | Francesc Viñes | Francesc Illas | Gianfranco Pacchioni | Livia Giordano
Source: Journal of Materials Chemistry A (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Predicting the HER activity of SACs on MXenes with simple features and interpretable machine learning models An interpretable machine learning model was developed using DFT data to predict hydrogen adsorption energies, enabling accurate and transparent screening of HER catalysts with performance comparable to the intrinsic accuracy of DFT methods. MXenes ; Simple (philosophy) ; Pattern recognition (psychology) ; Experimental data ; Training set MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Xiangfei Ye | Zhentao Zhang | Haoxiang Lan | Chongyin Zhang | Liquan Wang | Jiaping Lin | Xinyao Xu | Yisheng Xu | Lei Du | Xiaohui Tian
Source: Macromolecules (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Design of Thermosetting Polymers with High Thermal Stability and Enhanced Processability via ML-assisted Material Genome Approach Phthalonitrile (PN), due to its good mechanical and thermal properties, stands out as an ideal high-performance resin. However, the conflict between heat resistance and processing property has always hindered the development and application of PN resins. We proposed a machine-learning-assisted materials genome approach to enable the practical design of PNs with excellent heat resistance and expansive processing windows. Combining domain knowledge, we constructed three property prediction models through machine learning. Subsequently, an extensive collection of candidate structures was created to screen optimal structures based on the machine learning predictions. Three PN structures were selected for proof-of-concept experiments. The measure properties of PNs agree well with the predictions, confirming the effectiveness of our strategy. The optimal PN surpasses existing performance constraints and extends processing boundaries without compromising heat resistance. In addition, the integrated gradients method was extended to analyze chemical “genes”, which deepens the understanding of the structure–property relationship of the PNs, and takes a step forward in implementing interpretable machine learning methods. The insights derived from gene analysis can assist in the design of heat-resistant polymers with excellent processing properties. Thermosetting polymer ; Thermal stability ; Polymer ; Materials science ; Polymer chemistry ; Polymer science ; Thermal ; Chemical engineering ; Composite material ; Engineering ; Thermodynamics Machine Learning in Materials Science ; Injection Molding Process and Properties ; Epoxy Resin Curing Processes
Authors: Yongjian Li | Chongteng Wu | Yihong Wang | Ning Li | Tiefeng Liu | Jun Lü
Source: Advanced Functional Materials (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Accelerating the Battery Revolution: AI‐Driven Multiscale Innovation From Material Discovery to Smart Manufacturing Abstract Just as artificial intelligence (AI) demonstrates remarkable potential in accelerating material discovery, its transformative impact is now extending to address critical challenges in lithium‐ion batteries (LIBs) development, particularly in overcoming persistent hurdles like protracted innovation cycles and prohibitive costs. This review systematically examines how AI and machine learning (ML) provide innovative solutions across the LIBs value chain‐from accelerating material innovation and optimizing synthesis processes to enhancing manufacturing precision. Beginning with fundamental concepts of AI/ML in energy storage, the analysis progresses to comprehensive applications in LIBs technology. Meanwhile, AI‐driven approaches enhance discovery efficiency for electrode materials, while improving property prediction accuracy and cost‐effectiveness. For materials synthesis, AI enables parameter optimization across scales and facilitates transition from lab‐scale breakthroughs to industrial production. Within electrode manufacturing, AI applications evolve from localized process optimization toward integrated full‐chain modeling and closed‐loop control systems. In cell manufacturing, AI demonstrates particular promise in three key areas, while showing limitations in whole‐process reliability forecasting. The review ultimately identifies critical barriers to AI adoption in battery manufacturing, including data fragmentation across production stages, insufficient high‐quality datasets, lack of standardized data protocols, and fundamental constraints in model interpretability and cross‐scenario adaptability. Interpretability ; Battery (electricity) ; Transformative learning ; Curse of dimensionality ; Process (computing) ; Data-driven ; Reliability (semiconductor) ; Key (lock) ; Big data ; Advanced manufacturing Machine Learning in Materials Science ; Extraction and Separation Processes ; Advancements in Battery Materials
Authors: Zihan Tang | Weikang Xian | Jianxin He | Rong Long | Ying Li
Source: International Journal of Smart and Nano Materials (article, 2025-10-02)
Field: Materials Science — Polymers and Plastics

From bonds to breaks: multiscale perspectives on polymer network fracture Fracture in polymer networks arises from complex, multiscale phenomena spanning molecular bond rupture to macroscopic crack propagation. This review summarizes recent theoretical and computational advances that deepen our understanding of polymer network fracture. We trace the evolution from classical theories, such as Griffith’s energy balance and the Lake-Thomas theory of chain scission model to recent frameworks incorporating nonlocal energy dissipation, topological defects, and loop-opening mechanisms. Emphasis is placed on how network structure, entanglement, and architectural heterogeneities influence fracture toughness. To complement these theories, we examine a wide range of simulation strategies, including all-atomic molecular dynamics, coarse-grained models, mesoscale network representations, and continuum mechanics. These simulations reveal the molecular origins of void nucleation, craze formation, and crack advancement in materials ranging from glassy polymers to double networks and nanocomposites. Additionally, we discuss the quasistatic and dynamic mesoscale models employed to study the influence of network connectivity and spatial heterogeneity as critical links between molecular mechanisms and macroscopic fracture. At the continuum scale, cohesive zone modeling, eXtended Finite Element Method, and damage-based are discussed to simulate macroscopic crack growth and complex fracture patterns. Together, these multiscale approaches provide mechanistic insights and design principles critical for tougher, more resilient polymeric materials. Finally, we discuss emerging opportunities in dynamic scale-bridging, machine learning integration, and the design of defect-tolerant architectures. Multiscale modeling ; Fracture mechanics ; Fracture (geology) ; Molecular dynamics ; Void (composites) ; Network model ; Polymer ; Mesoscale meteorology Polymer crystallization and properties ; Polymer Nanocomposites and Properties ; Material Dynamics and Properties
Authors: Kai Wagoner-Oshima | Romakanta Bhattarai | Humberto Terrones | Trevor David Rhone
Source: ACS Applied Materials & Interfaces (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Data-Driven Studies of Two-Dimensional Materials and Their Nonlinear Optical Properties We present a data-driven investigation leveraging high-throughput density functional theory calculations and machine learning to expedite the discovery of van der Waals (vdW) materials with nonlinear optical properties. Using the Computational 2D Materials Database, we analyze data from 345 noncentrosymmetric, nonmagnetic semiconductor monolayers, focusing on their second-order susceptibility tensors across multiple energy ranges suitable for various laser applications. By applying data mining techniques to extract key features from second harmonic generation spectra and employing machine learning models, we predict the second-order optical susceptibility for these materials. Our framework for this work facilitates the rapid identification of vdW materials for advanced photonics, optoelectronics, and data storage applications. Materials science ; Nonlinear optical ; Photonics ; 3D optical data storage ; van der Waals force ; Nonlinear system ; Semiconductor ; Nonlinear optics ; Second-harmonic generation ; Work (physics) ; Key (lock) ; Density functional theory ; Nanotechnology ; Laser ; Optoelectronics ; Computer science ; Optics ; Physics ; Quantum mechanics ; Molecule 2D Materials and Applications ; Nanowire Synthesis and Applications ; Boron and Carbon Nanomaterials Research
Authors: Dong Hyeon Mok | Jaehyoung Lim | Hyunju Chang | Jungho Shin | Jiwoo Jang | Uk Sim | Geun Ho Gu | Seoin Back
Source: Chemical Engineering Journal (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Machine learning high-throughput screening of double perovskites for enhanced acidic oxygen evolution Throughput ; Oxygen ; Oxygen evolution ; High-throughput screening ; Chemistry ; Materials science ; Chemical engineering ; Nanotechnology ; Computer science ; Engineering ; Electrochemistry ; Electrode ; Telecommunications ; Biochemistry ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Bao-Juan Cheng | Hongdan Wang | Shouqin Xiang | Shun Lu | Bingzhi Ren
Source: Nanomaterials (review, 2025-10-27)
Field: Chemistry — Electrochemistry

MoS2-Based Composites for Electrochemical Detection of Heavy Metal Ions: A Review Heavy metal ions (HMIs) threaten ecosystems and human health due to their carcinogenicity, bioaccumulativity, and persistence, demanding highly sensitive, low-cost real-time detection. Electrochemical sensing technology has gained significant attention owing to its rapid response, high sensitivity, and low cost. Molybdenum disulfide (MoS<sub>2</sub>), with its layered structure, tunable bandgap, and abundant edge active sites, demonstrates significant potential in the electrochemical detection of heavy metals. This review systematically summarizes the crystal structure characteristics of MoS<sub>2</sub>, various preparation strategies, and their mechanisms for regulating electrochemical sensing performance. It particularly explores the cooperative effects of MoS<sub>2</sub> composites with other materials, which effectively enhance the sensitivity, selectivity, and detection limits of electrochemical sensors. Although MoS<sub>2</sub>-based materials have made significant progress in theoretical and applied research, practical challenges remain, including fabrication process optimization, interference from complex-matrix ions, slow trace-metal enrichment kinetics, and stability issues in flexible devices. Future work should focus on developing efficient, low-cost synthesis methods, enhancing interference resistance through microfluidic and biomimetic recognition technologies, optimizing composite designs, resolving interfacial reaction dynamics via in situ characterization, and establishing structure-property relationship models using machine learning, ultimately promoting practical applications in environmental monitoring, food safety, and biomedical fields. Molybdenum disulfide ; Electrochemistry ; Microfluidics ; Fabrication ; Composite number ; Molybdenum ; Process (computing) ; Metal Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; 2D Materials and Applications
Authors: Rui Zhou | Luyao Bao | Weifeng Bu | Feng Zhou
Source: npj Computational Materials (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AI Data-driven material innovation has the potential to revolutionize the traditional Edisonian process and significantly shorten development cycles. However, the scarcity of data in materials science and the poor interpretability of machine learning pose serious obstacles to the adoption of this new paradigm. Here, we propose a pipeline that integrates data production, virtual screening, and theoretical innovation using high-throughput all-atom molecular dynamics (MD) as a data flywheel. Using this pipeline, we explored high-performance viscosity index improver polymers and constructed a dataset of 1166 entries for viscosity index improvers (VII) started from only five types of polymers. Under multi-objective constraints, 366 potential high-viscosity-temperature performance polymers were identified, and six representative polymers were validated through direct MD simulations. Starting from high-dimensional physical features, we conducted an unbiased systematic analysis of the quantitative structure-property relationships for polymers VII, providing an explicit mathematical model with promising application in VII industry. This work demonstrates the advanced capabilities and reliability of the pipeline proposed here in initiating material innovation cycles in data-scarce fields, and the establishment of the VII dataset and models will serve as a critical starting point for the data-driven design of high viscosity-temperature performance polymers. Throughput ; Viscosity ; Index (typography) ; Polymer ; Dynamics (music) ; Computer science ; Nanotechnology ; Chemistry ; Chemical engineering ; Materials science ; Engineering ; Organic chemistry ; Physics ; Composite material ; Telecommunications ; World Wide Web Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Raphaël Kou | Cong Wang | Jinxia Liu | R. Wan | Zhe Jin | Le Zhao | Ye Liu | Junwei Guo | Li Feng | Hongbo Wang | Song Yang | Cong Nie
Source: Frontiers in Plant Science (article, 2025-07-25)
Field: Chemistry — Analytical Chemistry

Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning Tobacco leaf position is closely associated with its quality whose material basis is the chemical components of tobacco leaf. In recent years, near-infrared (NIR) spectroscopy combined with algorithmic models has emerged as a popular method for identifying the tobacco leaf position. However, when applied to leaf position discrimination, these models often rely on principal components derived from dimensionality-reduced spectral signals, resulting in limited interpretability and difficulty in identifying key chemical components. Chemical composition data combined with algorithmic models can also be used to discriminate tobacco leaf positions. However, the acquisition of chemical components relies on traditional instrumental analytical methods. As a result, the acquisition of chemical composition data is time-consuming and labor-intensive, involving only a limited number of compounds. The study proposes a novel approach that integrates machine learning with advanced interpretability techniques for both tobacco leaf position discrimination and analysis. Based on the 70 tobacco leaf chemical components obtained using near-infrared rapid analysis technology, tobacco leaf position discrimination models were built using Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Random Forest (RF). Particle swarm optimization (PSO) was used to optimize parameters of each model. Chemical components were analyzed for statistical significance across leaf positions, and their influence on model predictions was interpreted using SHapley Additive exPlanations (SHAP). The experimental results showed that among all models, the SVM- hybrid kernel demonstrated the most robust and accurate performance, achieving discrimination accuracies of 98.17% and 96.33% on the training and test sets, respectively. SHAP analysis provided a clear ranking of feature importance and revealed the positive and negative contributions of individual chemical components. The proposed method can be useful for position traceability and chemical feature analysis of various crops. Interpretability ; Tobacco leaf ; Artificial intelligence ; Computer science ; Principal component analysis ; Position (finance) ; Support vector machine ; Curse of dimensionality ; Discriminative model ; Pattern recognition (psychology) ; Machine learning ; Biological system ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Chen Dong | Youcai Zhu | Chunlin Qu | Yuxin Chen | Yi Ma | Yang Yu | Caiting Li
Source: Coordination Chemistry Reviews (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

Coordination chemistry-driven oxygen vacancy strategy for rational design of high-performance catalysts in BTX oxidation Chemistry ; Catalysis ; Rational design ; Oxygen ; Molecular oxygen ; Chemical engineering ; Organic chemistry ; Nanotechnology Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Semih Oktay | İremnur Duru | Halit Bakır | Timuçin Emre Tabaru
Source: Optical and Quantum Electronics (article, 2025-01-11)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning and machine learning based highly accurate reflection prediction model for multi layers anti-reflection coatings Abstract This study focuses on creating an accurate reflection prediction model that will guide the design of filters with multilayer Anti-Reflection Coating (ARC) to optimize the thickness parameters using Machine Learning (ML) and Deep Learning (DL) techniques. This model aims to shed light on the design process of a multilayer optical filter, making it more cost-effective by providing faster and more precise production. In creating this model, a dataset containing data obtained from 3000 (1500 Ge–Al 2 O 3 , 1500 Ge–SiO 2 ) simulations previously performed on a computer based on the thicknesses of multilayer structural materials was used. The data are generated using Computational Electromagnetic simulation software based on the Finite-Difference Time-Domain method. To understand the mechanism of the proposed model, two different two-layer coating simulations were studied. While Ge was used as the substrate in both coatings, Al 2 O 3 and SiO 2 were used as the second layers. The data set consists of the 3–5 µm and 8–12 µm bands typical for the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands and includes reflectance values for wavelengths ranging between these spectra. In the specified 2-layer data set, the average reflectance was obtained with a minimum of 0.36 at 515 nm Ge and 910 nm SiO2 thicknesses. This value can be increased by adapting the proposed model to more than 2 layers. Six ML algorithms and a DL model, including artificial neural networks and convolutional neural networks, are evaluated to determine the most effective approach for predicting reflectance properties. Furthermore, in the proposed model, a hyperparameter tuning phase is used in the study to compare the efficiency of ML and DL methods to generate dual-band ARC and maximize the prediction accuracy of the DL algorithm. To our knowledge, this is the first time this has been implemented in this field. The results show that ML models, particularly decision tree (MSE: 0.00000069, RMSE: 0.00083), random forest (MSE: 0.00000525, RMSE: 0.00229), and bagging (MSE: 0.00000667, RMSE: 0.00258) methods, outperform the DL model (MSE: 0.0040107, RMSE: 0.06333) in predicting reflectance values and provide a valuable reference for designing and fabricating optical thin-film filters. Reflection (computer programming) ; Computer science ; Optics ; Artificial intelligence ; Machine learning ; Physics Optical Coatings and Gratings ; Surface Roughness and Optical Measurements ; Advanced Fiber Optic Sensors