jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N. Kim | Hyuk Jun Yoo | Daeho Kim | Hee‐Seung Lee | Sang Soo Han
Source: ChemRxiv (preprint, 2025-08-06)
Field: Materials Science — Materials Chemistry

SPACESHIP: synthesizable parameter acquisition via closed-loop exploration and self-directed, hardware-aware intelligent protocols for autonomous lab Autonomous laboratories hold great promise for accelerating material discovery but are often restricted by static, predefined experimental constraints. We present SPACESHIP, an AI-driven framework for dynamic, constraint-free exploration of synthesizable regions in chemical parameter spaces. SPACESHIP integrates probabilistic models and uncertainty-aware acquisition to learn from both successful and failed experiments to adaptively refine feasible synthesis conditions. A central feature is the Autopilot strategy, which dynamically selects between models and achieves up to 4.3× faster exploration than does random sampling. When applied to gold (Au) nanoparticle (NP) and nanorod (NR) synthesis, SPACESHIP identified synthesizable regions with 90% accuracy in only 23 experiments and reached 97% accuracy within 127 experiments—substantially fewer than the 625 experiments required to establish the ground truth. This method also enables the ternary classification of Au NRs on the basis of their optical properties, revealing distinct growth regimes. Compared with literature-based maps, SPACESHIP expanded the validated synthesizable space by a factor of 8 for NPs and 4 for NRs, supporting scalable discovery and mechanistic insight. Computer science ; Embedded system ; Computer architecture ; Computer hardware Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Duo Wang | Ao Xie | Shengcun Ma | Wei Tong | Shiqiang Zou | Anubhav Jain
Source: ChemRxiv (preprint, 2025-08-05)
Field: Materials Science — Materials Chemistry

Computational Catalysis and Machine Learning Applications to Water Treatment Technologies Electrocatalysis can offer new routes to water treatment. For example, electrocatalytic reduction is an emerging technology for treating oxyanions of concern in water. However, identification of highly performant, cost-effective catalysts remains a major barrier to deployment at scale. This article discusses how computational modeling and machine learning can accelerate the search for new catalyst materials. It describes how traditional computational chemistry workflows, now deployed in their basic form for at least two decades, can be expanded in breadth and depth through newly developed machine-learned force fields that have been trained on millions of examples. It also discusses how the theory and machine learning pipeline can effectively integrate with experimental synthesis and characterization platforms to rapidly identify and validate new catalyst chemistries. Computer science ; Catalysis ; Chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Rogério Almeida Gouvêa | Pierre-Paul De Breuck | Tatiane Pretto | Gian‐Marco Rignanese | Marcos J. L. Santos
Source: Research Square (preprint, 2025-09-17)
Field: Materials Science — Materials Chemistry

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Interpretability ; Graph ; Expressive power ; Artificial neural network ; Representation (politics) ; Deep learning ; Suite ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Haojie Liu | Yonggang Yao
Source: The Innovation Materials (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

From automation to autonomy: Embracing multimodal intelligence in materials discovery The discovery of efficient and durable electrocatalysts underpins a broad spectrum of sustainable technologies, from renewable energy conversion and storage to carbon management and green chemical manufacturing.Yet progress remains constrained by the near-infinite complexity of chemical space: even small variations in composition, processing, or nanostructure can cause dramatic changes in catalytic performance. 1Conventional trial-anderror or intuition-driven searches are slow and biased, while most optimization strategies remain unimodal, adjusting limited parameters against a single metric and failing to capture the nonlinear, multi-parameter interactions that govern catalytic behavior (Figure 1A).Artificial intelligence (AI) and robotics have emerged as promising solutions.Several autonomous laboratories now couple machine learning with robotic synthesis and testing, accelerating the design-make-test-learn cycle and reducing human labor. 2,3Yet most remain efficient "tools" rather than true "scientists."This efficiency defines automation: the fast execution of pre-programmed, repetitive tasks, typically relying on a single, simplified data stream (e.g., recipes and outputs).However, such systems remain blind to the broader scientific context-literature knowledge, microstructural cues from microscopy, or experimental subtleties that influence outcomes.Bridging this gap requires autonomy: the capacity for sophisticated, multimodal knowledge integration, reasoning, and most critically, self-diagnosis and selfcorrection of experimental anomalies to ensure reproducibility. Automation ; Key (lock) ; Expert system ; Applications of artificial intelligence Machine Learning in Materials Science ; Material Selection and Properties ; Electronic and Structural Properties of Oxides
Authors: Andrea Giunto | Yuxing Fei | Pragnay Nevatia | Bernardus Rendy | Nathan J. Szymanski | Gerbrand Ceder
Source: Research Square (preprint, 2025-10-14)
Field: Materials Science — Materials Chemistry

Harnessing Automated SEM-EDS and Machine Learning to Unlock High-Throughput Compositional Characterization of Powder Materials Characterization (materials science) ; Bottleneck ; Throughput ; Software ; Single particle analysis X-ray Diffraction in Crystallography ; Mineral Processing and Grinding ; Advanced materials and composites
Authors: Rama Durga Pujari | Govinda Rao Kamala
Source: Journal of Pharma Insights and Research. (article, 2025-08-05)
Field: Chemistry — Organic Chemistry

A Review on Uses of Combinatorial Chemistry in Drug Discovery Combinatorial chemistry represents a paradigm shift in chemical synthesis methodology, enabling simultaneous generation of multiple structurally diverse compounds called libraries. The approach encompasses rapid synthesis of compound arrays through systematic combination of building blocks using solid-phase, solution-phase, parallel synthesis and split-pool techniques. Integration with high-throughput screening allows efficient identification of active compounds, significantly accelerating drug discovery timelines compared to traditional one-compound-at-a-time methods. The methodology originated from Merrifield's solid-phase peptide synthesis in the 1960s and gained widespread industrial adoption in the 1990s. Modern combinatorial approaches utilize various solid supports like polystyrene resins, sophisticated linker chemistry, and automated parallel synthesizers. The technology has revolutionized lead discovery and optimization in pharmaceutical research while finding applications in materials science, catalyst development, and chemical biology. Combinatorial libraries containing thousands of compounds can be rapidly synthesized and screened against biological targets. Key advantages include accelerated synthesis timeframes, broader structural diversity, rich structure-activity relationship data, and comprehensive patent coverage. Despite challenges in product characterization and chemistry limitations on solid phase, combinatorial chemistry continues advancing through innovations in synthetic methodology, automation, and analytical techniques. The significant impact spans multiple scientific domains including drug development, materials discovery, and chemical process optimization Drug discovery ; Chemistry ; Computer science ; Combinatorial chemistry ; Computational biology ; Biology ; Biochemistry Click Chemistry and Applications ; Computational Drug Discovery Methods
Authors: Anton A. Krutov | Evgeniy Bychkov | Elena I. Voevodina | Aleksandr Sal'nikov
Source: Theoretical economics (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

Key global trends in the digital inverse engineering market The article provides a comprehensive analysis of global technological, market, and institutional trends in the development of digital inverse engineering of polymer materials, with a particular focus on applications in additive manufacturing. It is demonstrated that traditional empirical approaches to polymer development increasingly fail to meet modern industrial requirements, which are characterized by growing product complexity, shortened innovation cycles, and the need for rapid delivery of materials with precisely defined properties. The concept of inverse engineering is examined as an alternative paradigm, where material development starts from target performance characteristics, and the selection of structure and composition is carried out using computational modeling and artificial intelligence methods. The study reviews key technological drivers of digital materials science, including machine learning techniques, generative models, graph neural networks, and natural language processing for the formalization of engineering requirements. Special attention is given to platform-based solutions that implement a closed digital loop “request — model — material” and their integration with CAD/CAE, PLM, ELN systems, and robotic laboratories. The economic rationale for adopting digital inverse engineering is analyzed, highlighting reduced R&amp;D costs, accelerated commercialization of new materials, and the democratization of access to advanced materials design capabilities for small and medium-sized enterprises. The paper identifies major barriers to market development, including data scarcity, limited interpretability of AI models, and discrepancies between laboratory-scale predictions and industrial-scale performance. Perspective directions are outlined, such as autonomous R&amp;D loops, extension of inverse design approaches to composite and functional materials, and the integration of sustainability and regulatory constraints into digital platforms. The results of the study can support strategic decision-making in the development of digital materials design platforms and in assessing the long-term growth potential of the industry. Interpretability ; Commercialization ; Flexibility (engineering) ; Key (lock) ; Graph ; Sustainability ; Inverse ; Modular design Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials
Authors: Zainab Fatima | Isao Ohkubo | Satoshi Ishii | Takahiro Nagata | T. Aizawa | Takao Mori
Source: Institutional Repositories DataBase (IRDB) (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Overcoming surface energy to control Cu3N epitaxial growth Crystal orientation control during copper nitride (Cu3N) epitaxial growth was achieved using reactive DC magnetron sputtering. Both the (100)-orientation and (111)-orientation were observed readily from X-ray diffraction measurements for Cu3N thin films grown on single-crystal MgO(100), MgO(111), SrTiO3(100), and sapphire(0001) substrates. The Cu3N(111) surface energy is greater than that of Cu3N(100), suggesting that the Cu3N(111) orientation has a lower formation probability than the Cu3N(100) orientation. To control the influence of surface energy, thin film growth parameters related to the thermodynamics and kinetics of epitaxial thin film growth were tuned. Growth of single (111)-oriented Cu3N epitaxial thin films, which has a higher surface energy orientation, was achieved on MgO(111) substrates. Optical band gaps of the single (111)-oriented Cu3N epitaxial thin film were 1.80 eV for direct transition and 0.82 eV for indirect transition, indicating formation of a reasonable electronic structure in single (111)-oriented Cu3N epitaxial thin films with higher surface energy. Epitaxy ; Energy (signal processing) ; Surface energy ; Surface (topology) ; Deposition (geology) Copper-based nanomaterials and applications ; Nanomaterials and Printing Technologies ; GaN-based semiconductor devices and materials
Authors: Vishal Ahuja | Shikha Chauhan | K Singh | Jaapna Dhillon | Deepak Sharma
Source: Discover Applied Sciences (article, 2025-10-21)
Field: Materials Science — Biomaterials

Multifunctional bio-nanostructure for cancer therapy: mini review The relentless pursuit of innovative cancer therapies has recently focused on the development of multifunctional bio-molecule nanostructures, fabricated with biomolecules and engineered for drug delivery. It improves the accuracy and efficiency of diagnostic procedures, enables theranostic capabilities, and allows integration of advanced treatment strategies with real-time monitoring. Besides size, the diversity of nanomaterials also relies upon the source, method of preparation and types of interactions in host body. The nanomaterials are mainly classified based on their feed or raw materials such as metals, carbon, or biomolecules-based carriers involved in preparations like mono-molecular or composites, dimensionality (0D, 1D, 2D or 3D) and the types of functionalization. Silver (Ag) and gold (Au) are the most common NMs used directly or in combination with other biomolecules for healthcare. The major applications of nanoparticles are in imaging and drug delivery while 3D structures are related to scaffolds and tissue engineering. Even with multi-fold applications, nanomaterials are facing hurdles in commercialization mainly due to the lack of standardized preparation methods with consistent properties, potential harmful effects and allergic responses of nanoparticles, and ethical concerns related to environmental and toxic effects. The accuracy and real time monitoring in clinical trials are also a concern. The integration of deep learning, machine learning, and artificial intelligence emerges as a pivotal player in advancement, and enhancement of nanomedicines or nano-formulations. The availability of regulatory framework is also required to ensure the delivery of best formulation to the patients. The current review provides an extensive exploration of the design, synthesis, and application of bionanostructures, prepared with proteins, peptides, nucleic acids, lipids, and polysaccharides for cancer therapy. It also concludes the ethical concern and environmental impact as major hurdle to overcome. The potential of nanoformulations can be fully exploited with interdisciplinary collaboration. Commercialization ; Drug delivery ; Cancer treatment ; Nanomedicine ; Cancer ; Cancer therapy Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Junwu Chen | Jeff Guo | Edvin Fako | Philippe Schwaller
Source: ArXiv.org (preprint, 2025-11-05)
Field: Materials Science — Materials Chemistry

Accelerating inverse materials design using generative diffusion models with reinforcement learning Diffusion models promise to accelerate material design by directly generating novel structures with desired properties, but existing approaches typically require expensive and substantial labeled data ($&gt;$10,000) and lack adaptability. Here we present MatInvent, a general and efficient reinforcement learning workflow that optimizes diffusion models for goal-directed crystal generation. For single-objective designs, MatInvent rapidly converges to target values within 60 iterations ($\sim$ 1,000 property evaluations) across electronic, magnetic, mechanical, thermal, and physicochemical properties. Furthermore, MatInvent achieves robust optimization in design tasks with multiple conflicting properties, successfully proposing low-supply-chain-risk magnets and high-$κ$ dielectrics. Compared to state-of-the-art methods, MatInvent exhibits superior generation performance under specified property constraints while dramatically reducing the demand for property computation by up to 378-fold. Compatible with diverse diffusion model architectures and property constraints, MatInvent could offer broad applicability in materials discovery. Property (philosophy) ; Computation ; Workflow ; Reinforcement learning ; Inverse ; Diffusion Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Topology Optimization in Engineering
Authors: Jenan Atiyah Ghafil | Bashar Ibrahim | Nihad Taha Mohammed Jaddoa
Source: World Journal of Experimental Biosciences (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Gold Nanoclusters in Cancer Photothermal Therapy: Mechanisms, Theranostic Applications, and Clinical Translation Over the Last Decade This last decade has seen the rise of gold nanoclusters (AuNCs) as a new, revolutionary class of photothermal agents for cancer therapy. The AuNCs (&lt;2 nm) have a high surface area-to-volume ratio and exhibit efficient electron-to-phonon coupling. AuNCs possess favorable renal clearance properties due to their extremely small hydrodynamic diameters. This review article provides a structured summary of advancements made in the areas of AuNC synthesis, ligand-shell engineering, environmentally friendly fabrication methodologies, and large-scale manufacturing methods during the years of 2015-2025. It also focuses on the role atomic precision and surface chemistry play in the photothermal performance, biodistribution, protein corona formation, and nano–bio interactions of AuNCs; in addition, we highlight the mechanistic details associated with non-radiative relaxation pathways, energy gap modulation, and optimizing photothermal conversion efficiency. It also provides proof of concept that AuNCs are suitable as multifunctional theranostic platforms that integrate both fluorescence and photoacoustic imaging into combination strategies of chemo-photothermal and immune-photothermal therapy, with immune checkpoint blockade to elicit a systemic immune response against tumor cells. The study demonstrates rapid renal elimination and low accumulation of AuNCs over long-term duration, while addressing some of the historical concerns associated with traditional inorganic nanoparticles. Finally, the translational barriers facing AuNCs were highlighted, including regulatory standardization, reproducibility, and cost-effectiveness. The future with artificial intelligence (AI) will support AuNCs synthesis Thus, AuNC-based photothermal therapy is an exciting new advancement in nanomedicine and is predicted to be on a fast track for use in precision oncology from the lab through to integration into clinical practice by 2030. Photothermal therapy ; Nanoclusters ; Nanomedicine ; Photoacoustic imaging in biomedicine ; Cancer ; Cancer therapy ; Tumor ablation ; Cancer treatment Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Karim Ben Hicham | Nicholas A. Jose | Mohammed I. Jeraal | Jan G. Rittig | Alexei A. Lapkin
Source: ChemRxiv (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

RAMBOAU: Risk-averse Multiobjective Bayesian Optimization with Aleatoric Uncertainty for Nanomaterial Synthesis Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under different synthesis conditions. This can be misaligned with the need for reliable synthesis protocols that consistently produce materials with desired properties. This work introduces a risk-averse multi-objective Bayesian optimization algorithm that accounts for heteroscedastic experimental uncertainty in the outcomes of different synthesis conditions using a value-at-risk approach. It employs two Gaussian processes per objective, one for the expected function value and one for its variance, and optimizes a novel risk-averse acquisition function. Benchmarking against a state-of-the-art risk-neutral algorithm shows superior performance in identifying robust Pareto fronts. Applied to optimize the production of high-purity, high-aspect-ratio ZnO nanoparticles in a continuously operated annular flow microreactor in the laboratory, the algorithm identified reliable process conditions despite significant uncertainty. This work represents an important step towards autonomous platforms that can identify synthesis conditions that are robust enough for industrial scale-up and practical implementation. Bayesian probability ; Bayesian optimization ; Multi-objective optimization ; Computer science ; Mathematical optimization ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Quentin Duez | Christine Lefebvre | Julien De Winter | Jérôme Cornil | Pascal Gerbaux
Source: The Journal of Physical Chemistry Letters (article, 2025-07-18)
Field: Chemistry — Spectroscopy

Classifying Host–Guest Topology with Ion Mobility-Mass Spectrometry and Machine Learning Elucidating the topology of host-guest complexes is essential for the rational design of supramolecular assemblies. Building on the recent success of data-driven approaches, we evaluate the combination of ion mobility-mass spectrometry (IMS-MS), density functional theory (DFT) featurization, and machine learning to predict and classify the binding modes of 1:1 complexes formed between cucurbit[6]uril (CB6) and diamine guests. Training a regression model with DFT-derived molecular descriptors and experimentally determined collisional cross sections (CCS) enables predicting the CCS of host-guest complexes with a diverse set of diamine guests. The predicted values naturally separate in two distinct groups corresponding respectively to inclusion and exclusion complexes, thereby enabling topology classification. This approach demonstrates that DFT-featurization and IMS-MS data capture well host-guest topology and provide a framework for the data-driven design of supramolecular assemblies. Ion-mobility spectrometry ; Supramolecular chemistry ; Topology (electrical circuits) ; Host (biology) ; Density functional theory ; Ion ; Mass spectrometry ; Molecule ; Chemistry ; Computer science ; Computational chemistry ; Mathematics ; Biology Mass Spectrometry Techniques and Applications ; Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes