jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ofer M. Shir
Source: ChemRxiv (preprint, 2024-01-23)
Field: Materials Science — Biomaterials

Towards AI Research Agents in the Chemical Sciences An underlying problem shared by experimental scientists is to achieve optimal behavior of their systems and arrive at new discoveries while searching over an array of controls. Accordingly, every scientific discovery may be reduced to solving a Combinatorial Optimization problem upon formulating the characteristic array of decision variables. While AI systems already excel in navigating the landscape of possible experiments, we argue herein that they will be able to drive the entire process of scientific experimental research. Especially, in response to the so-called Nobel Turing Challenge, regarding which Kitano envisioned AI Scientists, the goal of this paper is to provide a pragmatic roadmap to obtain AI Research Agents in the Chemical Sciences. We begin by reviewing the existing integration of Computational Intelligence into experimental systems, which already benefit from solving discovery/optimization problems. We mention recent discoveries in the domains of Enzymes' Design, Material Science, Quantum Mechanics, and Postharvest, in which AI systems played active roles in attaining some ground-breaking results -- thanks to being conception-free and unbiased by flawed intuition. We then devise a concrete work plan to train agents to formulate hypotheses by Deep Symbolic Reinforcement Learning, using knowledge representations based on processed scientific textbooks. We focus on the Chemical Sciences, which possess stationary Knowledge Graphs, and propose how to obtain an independent AI system at the graduate student level for ``core Chemistry''. Data science ; Computer science Diatoms and Algae Research ; Electrochemical sensors and biosensors ; History and advancements in chemistry
Authors: Indrajeet Mandal | Jitendra Soni | Mohd Zaki | Morten M. Smedskjær | Katrin Wondraczek | Lothar Wondraczek | Nitya Nand Gosvami | N. M. Anoop Krishnan
Source: ArXiv.org (preprint, 2024-12-18)
Field: Materials Science — Materials Chemistry

Autonomous Microscopy Experiments through Large Language Model Agents The emergence of large language models (LLMs) has accelerated the development of self-driving laboratories (SDLs) for materials research. Despite their transformative potential, current SDL implementations rely on rigid, predefined protocols that limit their adaptability to dynamic experimental scenarios across different labs. A significant challenge persists in measuring how effectively AI agents can replicate the adaptive decision-making and experimental intuition of expert scientists. Here, we introduce AILA (Artificially Intelligent Lab Assistant), a framework that automates atomic force microscopy (AFM) through LLM-driven agents. Using AFM as an experimental testbed, we develop AFMBench-a comprehensive evaluation suite that challenges AI agents based on language models like GPT-4o and GPT-3.5 to perform tasks spanning the scientific workflow: from experimental design to results analysis. Our systematic assessment shows that state-of-the-art language models struggle even with basic tasks such as documentation retrieval, leading to a significant decline in performance in multi-agent coordination scenarios. Further, we observe that LLMs exhibit a tendency to not adhere to instructions or even divagate to additional tasks beyond the original request, raising serious concerns regarding safety alignment aspects of AI agents for SDLs. Finally, we demonstrate the application of AILA on increasingly complex experiments open-ended experiments: automated AFM calibration, high-resolution feature detection, and mechanical property measurement. Our findings emphasize the necessity for stringent benchmarking protocols before deploying AI agents as laboratory assistants across scientific disciplines. Microscopy ; Computer science ; Nanotechnology ; Materials science ; Physics ; Optics Machine Learning in Materials Science
Authors: Nathaniel H. Park | Tiffany J. Callahan | James L. Hedrick | Tim Erdmann | Sara Capponi
Source: arXiv (Cornell University) (preprint, 2024-08-21)
Field: Materials Science — Materials Chemistry

Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design Molecular property prediction and generative design via deep learning models has been the subject of intense research given its potential to accelerate development of new, high-performance materials. More recently, these workflows have been significantly augmented with the advent of large language models (LLMs) and systems of autonomous agents capable of utilizing pre-trained models to make predictions in the context of more complex research tasks. While effective, there is still room for substantial improvement within agentic systems on the retrieval of salient information for material design tasks. Within this context, alternative uses of predictive deep learning models, such as leveraging their latent representations to facilitate cross-modal retrieval augmented generation within agentic systems for task-specific materials design, has remained unexplored. Herein, we demonstrate that large, pre-trained chemistry foundation models can serve as a basis for enabling structure-focused, semantic chemistry information retrieval for both small-molecules, complex polymeric materials, and reactions. Additionally, we show the use of chemistry foundation models in conjunction with multi-modal models such as OpenCLIP facilitate unprecedented queries and information retrieval across multiple characterization data domains. Finally, we demonstrate the integration of these models within multi-agent systems to facilitate structure and topological-based natural language queries and information retrieval for different research tasks. Workflow ; Foundation (evidence) ; Catalysis ; Computer science ; Nanotechnology ; Chemistry ; Materials science ; Organic chemistry ; Database ; Political science Catalytic Processes in Materials Science
Authors: Claudia Bizzarri | Manuel Tsotsalas
Source: Advanced Energy and Sustainability Research (article, 2024-12-30)
Field: Chemistry — Inorganic Chemistry

Data‐Driven Innovation in Metal‐Organic Frameworks Photocatalysis: Bridging Gaps for CO<sub>2</sub> Capture and Conversion with FAIR Principles Metal‐organic frameworks (MOFs) have emerged as key materials for carbon capture and conversion, particularly in photocatalytic CO 2 reduction. However, inconsistent reporting of essential parameters in the literature hinders informed decisions about material selection and optimization. This perspective highlights the need for a user‐friendly, centralized database supported by automated data extraction using natural language processing tools to streamline comparisons of MOF materials. By consolidating crucial data from scientific literature, such a database promotes efficient decision‐making in material selection for CO 2 capture and utilization. Emphasizing the significance of open‐source initiatives and the principles of FAIR data—ensuring data are Findable, Accessible, Interoperable, and Reusable—a collaborative approach to data management and sharing is advocated for. Making the database‐accessible worldwide enhances data quality and reliability, fostering innovation and progress in CO 2 capture and conversion using MOF materials. Additionally, such databases are valuable in creating artificial intelligence tools to assist researchers in the discovery and synthesis of MOF materials for CO 2 capture and conversion. Interoperability ; Data extraction ; Computer science ; Bridging (networking) ; Metal-organic framework ; Data sharing ; Key (lock) ; Database ; Data science ; World Wide Web ; MEDLINE ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Catalytic Processes in Materials Science
Authors: Qianggang Ding | Santiago Miret | Bang Liu
Source: arXiv (Cornell University) (preprint, 2024-10-26)
Field: Materials Science — Materials Chemistry

MatExpert: Decomposing Materials Discovery by Mimicking Human Experts Material discovery is a critical research area with profound implications for various industries. In this work, we introduce MatExpert, a novel framework that leverages Large Language Models (LLMs) and contrastive learning to accelerate the discovery and design of new solid-state materials. Inspired by the workflow of human materials design experts, our approach integrates three key stages: retrieval, transition, and generation. First, in the retrieval stage, MatExpert identifies an existing material that closely matches the desired criteria. Second, in the transition stage, MatExpert outlines the necessary modifications to transform this material formulation to meet specific requirements outlined by the initial user query. Third, in the generation state, MatExpert performs detailed computations and structural generation to create new materials based on the provided information. Our experimental results demonstrate that MatExpert outperforms state-of-the-art methods in material generation tasks, achieving superior performance across various metrics including validity, distribution, and stability. As such, MatExpert represents a meaningful advancement in computational material discovery using langauge-based generative models. Data science ; Computer science ; Computational biology ; Biology Machine Learning in Materials Science ; Mineral Processing and Grinding ; Image Processing and 3D Reconstruction
Authors: A. Gilad Kusne | Austin McDannald | Brian DeCost
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Learning material synthesis–process–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning Autonomous materials research labs require the ability to combine and learn from diverse data streams. Property (philosophy) ; Piecewise ; Fusion ; Bayesian probability ; Process (computing) ; Function (biology) ; Artificial intelligence ; Computer science ; Data mining ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Image Processing and 3D Reconstruction ; X-ray Diffraction in Crystallography
Authors: Maitreyee Sharma Priyadarshini | Nikhil Thota | Rigoberto Hernandez
Source: Journal of Chemical Information and Modeling (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

ReLMM: Reinforcement Learning Optimizes Feature Selection in Modeling Materials A challenge to materials discovery is the identification of the physical features that are most correlated to a given target material property without redundancy. Such variables necessarily comprise the optimal search domain in subsequent material design. Here, we introduce a reinforcement learning-based material model (ReLMM) as a tool for analyzing a given database in identifying a minimal or near minimal subset of physical features for the design of a material with a given target property. We aim for minimality in the selected subset with respect to its size─smaller being better─ while maintaining the desired accuracy of the prediction. We have shown, using synthetic multiscale data sets, that ReLMM can identify the relative importance of features, and thus help identify which should be selected across scales. In the context of semiconducting materials, ReLMM can be used to improve the prediction of the band gap by identifying which features should be selected in model building. For metal halide perovskites, ReLMM was seen to find a near minimal data set at least as well as, if not better than, state-of-the-art feature selection tools such as LASSO and XGBoost. We also found that our domain-science oriented approach can be used to uncover the hierarchical structure of a material from a database consisting of molecular-scale, mesoscale and device-scale features and labels in complementarity with an earlier hierarchical model called NestedAE. Computer science ; Reinforcement learning ; Property (philosophy) ; Redundancy (engineering) ; Feature selection ; Complementarity (molecular biology) ; Artificial intelligence ; Context (archaeology) ; Feature (linguistics) ; Machine learning ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Thermoelectric Materials and Devices
Authors: Luning Sun | Yuzhuo Yuan | Yuan Yao | Yanyan Li | Hao Zhang | Xing Xie | Xiting Wang | Fang Luo | David Stillwell
Source: arXiv (Cornell University) (preprint, 2024-12-04)
Field: Materials Science — Materials Chemistry

Large Language Models show both individual and collective creativity comparable to humans Artificial intelligence has, so far, largely automated routine tasks, but what does it mean for the future of work if Large Language Models (LLMs) show creativity comparable to humans? To measure the creativity of LLMs holistically, the current study uses 13 creative tasks spanning three domains. We benchmark the LLMs against individual humans, and also take a novel approach by comparing them to the collective creativity of groups of humans. We find that the best LLMs (Claude and GPT-4) rank in the 52nd percentile against humans, and overall LLMs excel in divergent thinking and problem solving but lag in creative writing. When questioned 10 times, an LLM's collective creativity is equivalent to 8-10 humans. When more responses are requested, two additional responses of LLMs equal one extra human. Ultimately, LLMs, when optimally applied, may compete with a small group of humans in the future of work. Creativity ; Psychology ; Linguistics ; Computer science ; Social psychology ; Philosophy Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Hyuk Jun Yoo | Nayeon Kim | Hee‐Seung Lee | Daeho Kim | Leslie Ching Ow Tiong | Hyobin Nam | Chansoo Kim | Seung Yong Lee | Kwan‐Young Lee | Dong-Hun Kim | Sang Soo Han
Source: Advanced Functional Materials (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Bespoke Metal Nanoparticle Synthesis at Room Temperature and Discovery of Chemical Knowledge on Nanoparticle Growth via Autonomous Experimentations (Adv. Funct. Mater. 27/2024) Metal Nanoparticle Synthesis In article number 2312561, Kwan-Young Lee, Donghun Kim, Sang Soo Han, and co-workers introduce an autonomous experimentation platform for bespoke synthesis of metal nanoparticles. This work highlights the value of autonomous experimentation platform, which offer twofold benefits of enhancing material developmental efficiency and elucidating novel chemical knowledge by analyzing the datasets accumulated from the operations of AI robotic platforms. Bespoke ; Materials science ; Nanoparticle ; Nanotechnology ; Metal ; Metallurgy ; Business Machine Learning in Materials Science ; Laser-Ablation Synthesis of Nanoparticles ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Alexander W. Dowling
Source: Systems and Control Transactions (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Machine Learning for Sustainable Molecular-to-Systems Engineering Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Recent advances in AI and ML are highlighted in four contemporary application areas in chemical engineering design: (1) equitable energy systems, (2) decarbonizing the power sector, (3) circular economies for critical materials, and (4) next-generation heating and cooling. These examples illustrate how AI and ML enable more sophisticated interdisciplinary multiscale models, faster optimization algorithms, more accurate uncertainty quantification, smarter and faster data collection, and incorporation of diverse stakeholders into decision-making processes, improving the robustness of engineering and policy designs while focusing on the multifaceted goals and constraints in wicked problems. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Joanna Przybysz | Ken J. Jenewein | Mária Minichová | Tomáš Hrbek | Thomas Böhm | Tatiana Priamushko | Serhiy Cherevko
Source: ACS Materials Letters (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Key Aspects in Designing High-Throughput Workflows in Electrocatalysis Research: A Case Study on IrCo Mixed-Metal Oxides With the growing interest of the electrochemical community in high-throughput (HT) experimentation as a powerful tool in accelerating materials discovery, the implementation of HT methodologies and the design of HT workflows has gained traction. We identify 6 aspects essential to HT workflow design in electrochemistry and beyond to ease the incorporation of HT methods in the community's research and to assist in their improvement. We study IrCo mixed-metal oxides (MMOs) for the oxygen evolution reaction (OER) in acidic media using the mentioned aspects to provide a practical example of possible workflow design pitfalls and strategies to counteract them. Key (lock) ; Workflow ; Electrocatalyst ; Throughput ; Computer science ; Chemistry ; Electrode ; Computer security ; Operating system ; Database ; Electrochemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Yuma Iwasaki
Source: Science and Technology of Advanced Materials Methods (article, 2024-09-02)
Field: Materials Science — Materials Chemistry

Autonomous search for materials with high Curie temperature using <i>ab initio</i> calculations and machine learning Efficient exploration of vast material spaces is a challenging task in materials science. Autonomous material search methods utilizing machine learning and ab initio calculations have emerged as powerful alternatives to traditional material discovery through synthesis and analysis, which is time-consuming and scope-limited. Although autonomous search methods have already been applied to various material spaces, they have not explored the extensive material space of Curie temperatures. Herein, we show a simulation-based autonomous search method that suggests ternary alloys with high Curie temperatures. The material space – consisting of disordered ternary magnetic alloys – is explored through Korringa – Kohn – Rostoker coherent potential approximation and Bayesian optimization. Over a continuous 10-day search, the system proposed several alloys – CoAuIr, CoPtPd, and CoFeBi – with Curie temperatures surpassing that of pure face-centered cubic Co. Although the insights gained through these predictions require further experimental and theoretical validation, this study demonstrates that autonomous material search methods can potentially accelerate material discovery and optimize material properties. Autonomous material search accelerates discovery of ternary alloys with high Curie temperatures, surpassing pure Co, using machine learning and ab initio calculations. Curie temperature ; Ternary operation ; Ab initio ; Materials science ; Bayesian optimization ; Curie ; Space (punctuation) ; Statistical physics ; Computer science ; Ferromagnetism ; Artificial intelligence ; Condensed matter physics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Microstructure and mechanical properties
Authors: Mahboubeh Motadayen | Nehru Devabharathi | Shweta Agarwala
Source: ChemRxiv (preprint, 2024-03-25)
Field: Materials Science — Materials Chemistry

Advancing Sustainability: Biodegradable Electronics and New Materials through AI and Machine Learning Artificial Intelligence (AI) and machine learning (ML) are revolutionizing science and engineering by enabling researchers to analyze vast amounts of data, uncover patterns, and make predictions with unprecedented accuracy. The integration of AI and ML techniques are driving innovation across disciplines, paving the way for groundbreaking discoveries and technological advancements. On another corner, sustainability in core engineering disciplines is taking hold. Much work has been done on transient technologies, with a particular emphasis on transient electronics. The research in this domain explores the new materials, architectures, and functionalities of devices with a time-bound lifetime. We present a whole landscape view of the field with a focus on the most recent developments, focusing mainly on transitory materials such as metals, polymers, and semiconducting materials. The development and optimization of commercially viable materials are being accelerated by the rapid integration of AI and molecular design tools for high-throughput experimentation. There is a discussion of the difficulties in expanding data-driven technologies from small molecules to polymers, highlighting the importance of AI in finding new molecular designs and revamping existing molecules for innovative applications. The paper emphasizes how crucial it is to define and standardize polymer systems for ML models to generate a cohesive data collection system for AI and automation improvements. It also highlights the need for improvements in ML methods to fully utilize the advantages of data-driven polymer chemistry, highlighting the significance of reliable and varied datasets for predictive models in the synthesis of polymers. The article's conclusion addresses the necessity of fundamental studies in polymer classification and standardization to fully capitalize on the potential of polymer development. Computer science ; Electronics ; Automation ; Sustainability ; Data science ; Field (mathematics) ; Domain (mathematical analysis) ; Nanotechnology ; Systems engineering ; Artificial intelligence ; Engineering ; Mechanical engineering ; Materials science ; Electrical engineering Machine Learning in Materials Science
Authors: A. S. Messecar | S. M. Durbin | Robert Makin
Source: MRS Communications (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Quantum and classical machine learning investigation of synthesis–structure relationships in epitaxially grown wide band gap semiconductors Materials science ; Epitaxy ; Semiconductor ; Optoelectronics ; Quantum ; Band gap ; Engineering physics ; Condensed matter physics ; Nanotechnology ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Semiconductor materials and interfaces ; Advanced Semiconductor Detectors and Materials
Authors: Angela M. Slates | Shana L. McAlexander | J. Dennis Nolan | Juan Pablo | Junhong Chen | Harley T. Johnson | L. Catherine Brinson
Source: Science Advances (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Partnerships and collaboration drive innovative graduate training in materials informatics Holistic and intentional training prepares next-generation materials informatics leaders and workforce for expedited materials discovery and design. Informatics ; Workforce ; Training (meteorology) ; Engineering management ; Knowledge management ; Computer science ; Medical education ; Engineering ; Medicine ; Political science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electronic and Structural Properties of Oxides