jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Subhasis Kundu
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

AI-Driven Copilot: Revolutionizing Scientific Discovery and Innovation through Self-Learning Systems This paper investigates the transformative impact of AI-powered copilots on scientific research and innovation. It explores how self-learning systems possess the potential to revolutionize hypothesis generation, experimental design, and data analysis across various scientific disciplines. The study examines the integration of autonomous AI agents into research workflows, emphasizing their ability to accelerate discovery processes and enhance the capabilities of human researchers. This paper explores the challenges and ethical implications of using AI copilots in scientific research. It presents case studies showcasing successful applications of AI-driven research assistants in areas like drug discovery, materials science, and climate modeling. Additionally, it proposes a framework for developing and implementing AI copilots in research environments, emphasizing the significance of human-AI collaboration and responsible innovation. Keywords — Artificial Intelligence, Scientific Discovery, Self- Learning Systems, Research Automation, Human-AI Collaboration, Innovation Acceleration, Ethical Considerations, AI Copilots, Drug Discovery, Materials Science, Climate Modeling. Transformative learning ; Workflow ; Computer science ; Data science ; Scientific discovery ; Automation ; Knowledge management ; Engineering ethics ; Artificial intelligence ; Engineering ; Sociology ; Psychology ; Cognitive science Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Zijie Huang | Yizhou Sun | Wei Wang
Source: arXiv (Cornell University) (preprint, 2023-07-10)
Field: Materials Science — Materials Chemistry

Generalizing Graph ODE for Learning Complex System Dynamics across Environments Learning multi-agent system dynamics has been extensively studied for various real-world applications, such as molecular dynamics in biology. Most of the existing models are built to learn single system dynamics from observed historical data and predict the future trajectory. In practice, however, we might observe multiple systems that are generated across different environments, which differ in latent exogenous factors such as temperature and gravity. One simple solution is to learn multiple environment-specific models, but it fails to exploit the potential commonalities among the dynamics across environments and offers poor prediction results where per-environment data is sparse or limited. Here, we present GG-ODE (Generalized Graph Ordinary Differential Equations), a machine learning framework for learning continuous multi-agent system dynamics across environments. Our model learns system dynamics using neural ordinary differential equations (ODE) parameterized by Graph Neural Networks (GNNs) to capture the continuous interaction among agents. We achieve the model generalization by assuming the dynamics across different environments are governed by common physics laws that can be captured via learning a shared ODE function. The distinct latent exogenous factors learned for each environment are incorporated into the ODE function to account for their differences. To improve model performance, we additionally design two regularization losses to (1) enforce the orthogonality between the learned initial states and exogenous factors via mutual information minimization; and (2) reduce the temporal variance of learned exogenous factors within the same system via contrastive learning. Experiments over various physical simulations show that our model can accurately predict system dynamics, especially in the long range, and can generalize well to new systems with few observations. Ode ; Ordinary differential equation ; Computer science ; Parameterized complexity ; Generalization ; System dynamics ; Graph ; Regularization (linguistics) ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Differential equation ; Mathematics ; Applied mathematics ; Algorithm Machine Learning in Materials Science ; Time Series Analysis and Forecasting ; Advanced Graph Neural Networks
Authors: Dmitry S. Polyanichenko | Bogdan Protsenko | Nikita V. Egil | Oleg O. Kartashov
Source: Materials (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization The most in-demand instrumental methods for new functional nanomaterial diagnostics employ synchrotron radiation, which is used to determine a material's electronic and local atomic structure. The high time and resource costs of researching at international synchrotron radiation centers and the problems involved in developing an optimal strategy and in planning the control of the experiments are acute. One possible approach to solving these problems involves the use of deep reinforcement learning agents. However, this approach requires the creation of a special environment that provides a reliable level of response to the agent's actions. As the physical experimental environment of nanocatalyst diagnostics is potentially a complex multiscale system, there are no unified comprehensive representations that formalize the structure and states as a single digital model. This study proposes an approach based on the decomposition of the experimental system into the original physically plausible nodes, with subsequent merging and optimization as a metagraphic representation with which to model the complex multiscale physicochemical environments. The advantage of this approach is the possibility to directly use the numerical model to predict the system states and to optimize the experimental conditions and parameters. Additionally, the obtained model can form the basic planning principles and allow for the optimization of the search for the optimal strategy with which to control the experiment when it is used as a training environment to provide different abstraction levels of system state reactions. Computer science ; Reinforcement learning ; Abstraction ; Representation (politics) ; Decomposition ; Artificial intelligence ; Distributed computing Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Muyu Lu | Fengyu Gao | Xiaolong Tang | Linjiang Chen
Source: ChemRxiv (preprint, 2023-11-17)
Field: Materials Science — Materials Chemistry

GPT-4-powered analysis and prediction of experiments through an effective chain-of-thought prompting strategy: a case study of selective catalytic reduction of NOx with NH3 by metal-oxide composites The chemistry laboratories of the future are poised to undergo a significant transformation, largely driven by the advent and integration of large language models (LLMs) into everyday tasks and workflows. As we stand at the threshold of this exciting fusion of chemistry research with artificial intelligence (AI), a pressing question arises: "In what ways can LLMs enhance and contribute to the field of chemistry?" This study investigates a potential application of LLMs in interpreting and predicting experimental outcomes based on various experimental variables, by leveraging the human-like reasoning and inference capabilities of LLMs to process and analyse the presented information. This study centres on the selective catalytic reduction of NOx with NH3 using metal-oxide composites, presenting a chemistry challenge for evaluating GPT-4's ability to discern correlations. We specifically assess how GPT-4 relates experimental variables—including the catalyst's composition, synthesis conditions, and operational parameters—to the corresponding outcome, namely the catalytic performance. We implement the Chain of Thought (CoT) concept to create reasoning paths that establish relationships, which are then reinput into GPT-4 to generate inferences. This involves formulating a series of logical steps, akin to human problem-solving, to uncover connections within the data. The results of these reasoning paths serve as a feedback loop, thereby enriching the depth and accuracy of GPT-4's subsequent inferences. Here, we introduce a specialized CoT prompting strategy, termed "Ordered-and-Structured" CoT (OSCoT), wherein each individual feature or aspect of the problem is systematically and sequentially examined. This contrasts with the baseline "one-pot" CoT (OPCoT) approach, where all information is processed in a simultaneous manner. The OSCoT strategy's sequential analysis of each problem aspect significantly enhances the quality and precision of the inferences generated by GPT-4. Our findings show that GPT-4, powered by OSCoT, can accurately predict catalytic performances for both binary and ternary metal-oxide composites. Reduction (mathematics) ; Perception ; Computer science ; Chemistry ; Nanotechnology ; Artificial intelligence ; Materials science ; Epistemology ; Mathematics ; Philosophy Machine Learning in Materials Science
Authors: Zhigalin, Alexander
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning Machine learning has the potential to automate molecular design and drastically accelerate the discovery of new functional compounds. Towards this goal, generative models and reinforcement learning (RL) using string and graph representations have been successfully used to search for novel molecules. However, these approaches are limited since their representations ignore the three-dimensional (3D) structure of molecules. In fact, geometry plays an important role in many applications in inverse molecular design, especially in drug discovery. Thus, it is important to build models that can generate molecular structures in 3D space based on property-oriented geometric constraints. To address this, one approach is to generate molecules as 3D point clouds by sequentially placing atoms at locations in space -- this allows the process to be guided by physical quantities such as energy or other properties. However, this approach is inefficient as placing individual atoms makes the exploration unnecessarily deep, limiting the complexity of molecules that can be generated. Moreover, when optimizing a molecule, organic and medicinal chemists use known fragments and functional groups, not single atoms. We introduce a novel RL framework for scalable 3D molecular design that uses a hierarchical agent to build molecules by placing substructures sequentially in 3D space, thus attempting to build on the existing human knowledge in the field of molecular design. In a variety of experiments with different substructures, we show that our agent, guided only by energy considerations, can efficiently learn to produce molecules with over 100 atoms from many distributions including drug-like molecules, OLED-like molecules, and biomolecules. Reinforcement learning ; Scalability ; Field (mathematics) ; Limiting ; Chemical space ; String (physics) ; Process (computing) ; Space (punctuation) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Suryanarayana Maddu | Scott Weady | Michael Shelley
Source: arXiv (Cornell University) (preprint, 2023-08-13)
Field: Materials Science — Materials Chemistry

Learning fast, accurate, and stable closures of a kinetic theory of an active fluid Important classes of active matter systems can be modeled using kinetic theories. However, kinetic theories can be high dimensional and challenging to simulate. Reduced-order representations based on tracking only low-order moments of the kinetic model serve as an efficient alternative, but typically require closure assumptions to model unrepresented higher-order moments. In this study, we present a learning framework based on neural networks that exploit rotational symmetries in the closure terms to learn accurate closure models directly from kinetic simulations. The data-driven closures demonstrate excellent a-priori predictions comparable to the state-of-the-art Bingham closure. We provide a systematic comparison between different neural network architectures and demonstrate that nonlocal effects can be safely ignored to model the closure terms. We develop an active learning strategy that enables accurate prediction of the closure terms across the entire parameter space using a single neural network without the need for retraining. We also propose a data-efficient training procedure based on time-stepping constraints and a differentiable pseudo-spectral solver, which enables the learning of stable closures suitable for a-posteriori inference. The coarse-grained simulations equipped with data-driven closure models faithfully reproduce the mean velocity statistics, scalar order parameters, and velocity power spectra observed in simulations of the kinetic theory. Our differentiable framework also facilitates the estimation of parameters in coarse-grained descriptions conditioned on data. Closure (psychology) ; Differentiable function ; A priori and a posteriori ; Computer science ; Scalar (mathematics) ; Artificial neural network ; Statistical physics ; Solver ; Inference ; Algorithm ; Applied mathematics ; Mathematics ; Artificial intelligence ; Mathematical optimization ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Protein Structure and Dynamics
Authors: Gordana Dodig-Crnković
Source: arXiv (Cornell University) (preprint, 2023-09-25)
Field: Chemistry — Organic Chemistry

Morphological Computing as Logic Underlying Cognition in Human, Animal, and Intelligent Machine This work examines the interconnections between logic, epistemology, and sciences within the Naturalist tradition. It presents a scheme that connects logic, mathematics, physics, chemistry, biology, and cognition, emphasizing scale-invariant, self-organizing dynamics across organizational tiers of nature. The inherent logic of agency exists in natural processes at various levels, under information exchanges. It applies to humans, animals, and artifactual agents. The common human-centric, natural language-based logic is an example of complex logic evolved by living organisms that already appears in the simplest form at the level of basal cognition of unicellular organisms. Thus, cognitive logic stems from the evolution of physical, chemical, and biological logic. In a computing nature framework with a self-organizing agency, innovative computational frameworks grounded in morphological/physical/natural computation can be used to explain the genesis of human-centered logic through the steps of naturalized logical processes at lower levels of organization. The Extended Evolutionary Synthesis of living agents is essential for understanding the emergence of human-level logic and the relationship between logic and information processing/computational epistemology. We conclude that more research is needed to elucidate the details of the mechanisms linking natural phenomena with the logic of agency in nature. Computational logic ; Cognitive science ; Agency (philosophy) ; Philosophy of logic ; Dynamic logic (digital electronics) ; Cognition ; Computer science ; Epistemic modal logic ; Epistemology ; Artificial intelligence ; Description logic ; Multimodal logic ; Psychology ; Philosophy ; Engineering Chemical synthesis and alkaloids ; Plant and Biological Electrophysiology Studies ; Evolutionary Game Theory and Cooperation
Authors: Frazier N. Baker | Ziqi Chen | Adu-Ampratwum, Daniel | Ning, Xia
Source: arXiv (Cornell University) (preprint, 2023-09-06)
Field: Materials Science — Materials Chemistry

RLSynC: Offline-Online Reinforcement Learning for Synthon Completion Retrosynthesis is the process of determining the set of reactant molecules that can react to form a desired product. Semi-template-based retrosynthesis methods, which imitate the reverse logic of synthesis reactions, first predict the reaction centers in the products, and then complete the resulting synthons back into reactants. We develop a new offline-online reinforcement learning method RLSynC for synthon completion in semi-template-based methods. RLSynC assigns one agent to each synthon, all of which complete the synthons by conducting actions step by step in a synchronized fashion. RLSynC learns the policy from both offline training episodes and online interactions, which allows RLSynC to explore new reaction spaces. RLSynC uses a standalone forward synthesis model to evaluate the likelihood of the predicted reactants in synthesizing a product, and thus guides the action search. Our results demonstrate that RLSynC can outperform state-of-the-art synthon completion methods with improvements as high as 14.9%, highlighting its potential in synthesis planning. Retrosynthetic analysis ; Synthon ; Reinforcement learning ; Computer science ; Artificial intelligence ; Set (abstract data type) ; Process (computing) ; Product (mathematics) ; Offline learning ; Reinforcement ; Machine learning ; Online learning ; Programming language ; Engineering ; Mathematics ; World Wide Web ; Chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Chemical Synthesis and Analysis
Authors: Xu Yang | Xiao Yang | Weiqing Liu | Jinhui Li | Peng Yu | Zeqi Ye | Jiang Bian
Source: arXiv (Cornell University) (preprint, 2023-10-17)
Field: Materials Science — Materials Chemistry

Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&amp;D Cycle In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making. Although data-centric R&amp;D has been pivotal in harnessing these solutions, it often comes with significant costs in terms of human, computational, and time resources. This paper delves into the potential of large language models (LLMs) to expedite the evolution cycle of data-centric R&amp;D. Assessing the foundational elements of data-centric R&amp;D, including heterogeneous task-related data, multi-facet domain knowledge, and diverse computing-functional tools, we explore how well LLMs can understand domain-specific requirements, generate professional ideas, utilize domain-specific tools to conduct experiments, interpret results, and incorporate knowledge from past endeavors to tackle new challenges. We take quantitative investment research as a typical example of industrial data-centric R&amp;D scenario and verified our proposed framework upon our full-stack open-sourced quantitative research platform Qlib and obtained promising results which shed light on our vision of automatic evolving of industrial data-centric R&amp;D cycle. Computer science ; Data science ; Domain (mathematical analysis) ; Domain-specific language ; Software engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational and Text Analysis Methods
Authors: ACII
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-04-26)
Field: Materials Science — Materials Chemistry

April 2023: Top Cited Articles in Computational Intelligence Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.<br> <br> Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the computational intelligence. Computer science Machine Learning in Materials Science ; Machine Learning and Data Classification ; Internet of Things and AI
Authors: Steve Paul | Wenyuan Li | Brian Smyth | Yuzhou Chen | Yulia R. Gel | Souma Chowdhury
Source: arXiv (Cornell University) (preprint, 2023-03-15)
Field: Materials Science — Biomaterials

Efficient Planning of Multi-Robot Collective Transport using Graph Reinforcement Learning with Higher Order Topological Abstraction Efficient multi-robot task allocation (MRTA) is fundamental to various time-sensitive applications such as disaster response, warehouse operations, and construction. This paper tackles a particular class of these problems that we call MRTA-collective transport or MRTA-CT -- here tasks present varying workloads and deadlines, and robots are subject to flight range, communication range, and payload constraints. For large instances of these problems involving 100s-1000's of tasks and 10s-100s of robots, traditional non-learning solvers are often time-inefficient, and emerging learning-based policies do not scale well to larger-sized problems without costly retraining. To address this gap, we use a recently proposed encoder-decoder graph neural network involving Capsule networks and multi-head attention mechanism, and innovatively add topological descriptors (TD) as new features to improve transferability to unseen problems of similar and larger size. Persistent homology is used to derive the TD, and proximal policy optimization is used to train our TD-augmented graph neural network. The resulting policy model compares favorably to state-of-the-art non-learning baselines while being much faster. The benefit of using TD is readily evident when scaling to test problems of size larger than those used in training. Computer science ; Reinforcement learning ; Graph ; Robot ; Artificial intelligence ; Theoretical computer science ; Topology (electrical circuits) ; Distributed computing Supramolecular Self-Assembly in Materials ; Advanced Graph Neural Networks ; Topological and Geometric Data Analysis