jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ahmed Shoyeb Raihan | Hamed Khosravi | Srinjoy Das | Imtiaz Ahmed
Source: arXiv (Cornell University) (preprint, 2023-11-16)
Field: Materials Science — Materials Chemistry

Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization Advancements in materials play a crucial role in technological progress. However, the process of discovering and developing materials with desired properties is often impeded by substantial experimental costs, extensive resource utilization, and lengthy development periods. To address these challenges, modern approaches often employ machine learning (ML) techniques such as Bayesian Optimization (BO), which streamline the search for optimal materials by iteratively selecting experiments that are most likely to yield beneficial results. However, traditional BO methods, while beneficial, often struggle with balancing the trade-off between exploration and exploitation, leading to sub-optimal performance in material discovery processes. This paper introduces a novel Threshold-Driven UCB-EI Bayesian Optimization (TDUE-BO) method, which dynamically integrates the strengths of Upper Confidence Bound (UCB) and Expected Improvement (EI) acquisition functions to optimize the material discovery process. Unlike the classical BO, our method focuses on efficiently navigating the high-dimensional material design space (MDS). TDUE-BO begins with an exploration-focused UCB approach, ensuring a comprehensive initial sweep of the MDS. As the model gains confidence, indicated by reduced uncertainty, it transitions to the more exploitative EI method, focusing on promising areas identified earlier. The UCB-to-EI switching policy dictated guided through continuous monitoring of the model uncertainty during each step of sequential sampling results in navigating through the MDS more efficiently while ensuring rapid convergence. The effectiveness of TDUE-BO is demonstrated through its application on three different material datasets, showing significantly better approximation and optimization performance over the EI and UCB-based BO methods in terms of the RMSE scores and convergence efficiency, respectively. Bayesian optimization ; Computer science ; Process (computing) ; Bayesian probability ; Convergence (economics) ; Mathematical optimization ; Machine learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Industrial Vision Systems and Defect Detection
Authors: Wei Chen | Yigitcan Comlek | Thang V. Pham | Randall Q. Snurr
Source: Research Square (preprint, 2023-03-22)
Field: Chemistry — Inorganic Chemistry

Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks Computer science ; Nanotechnology ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Yigitcan Comlek | Thang Duc Pham | Randall Q. Snurr | Wei Chen
Source: arXiv (Cornell University) (preprint, 2023-02-17)
Field: Chemistry — Inorganic Chemistry

Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks Data-driven materials design often encounters challenges where systems require or possess qualitative (categorical) information. Metal-organic frameworks (MOFs) are an example of such material systems. The representation of MOFs through different building blocks makes it a challenge for designers to incorporate qualitative information into design optimization. Furthermore, the large number of potential building blocks leads to a combinatorial challenge, with millions of possible MOFs that could be explored through time consuming physics-based approaches. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently without any human intervention. Our approach provides three main advantages: (i) no specific physical descriptors are required and only building blocks that construct the MOFs are used in global optimization through qualitative representations, (ii) the method is application and property independent, and (iii) the latent variable approach provides an interpretable model of qualitative building blocks with physical justification. To demonstrate the effectiveness of our method, we considered a design space with more than 47,000 MOF candidates. By searching only ~1% of the design space, LVGP-MOBBO was able to identify all MOFs on the Pareto front and more than 97% of the 50 top-performing designs for the CO$_2$ working capacity and CO$_2$/N$_2$ selectivity properties. Finally, we compared our approach with the Random Forest algorithm and demonstrated its efficiency, interpretability, and robustness. Interpretability ; Bayesian optimization ; Computer science ; Categorical variable ; Multi-objective optimization ; Robustness (evolution) ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Mathematical optimization ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Michael J. Wagner | Andreas Distler | Vincent M. Le Corre | S. Zapf | Burak Baydar | Hans-Dieter Schmidt | Madeleine Heyder | Karen Forberich | Larry Lüer | Christoph J. Brabec | Hans‐Joachim Egelhaaf
Source: arXiv (Cornell University) (preprint, 2023-05-15)
Field: Materials Science — Materials Chemistry

Cutting 'lab-to fab' short: High Throughput Optimization and Process Assessment in Roll-to-Roll Slot Die Coating of Printed Photovoltaics Commercialization of printed photovoltaics requires knowledge of the optimal composition and microstructure of the single layers, and the ability to control these properties over large areas under industrial conditions. While microstructure optimization can be readily achieved by lab scale methods, the transfer from laboratory scale to a pilot production line ('lab to fab') is a slow and cumbersome process. Here, we show how we can optimize the performance of organic solar cells and at the same time assess process performance in a 2D combinatorial approach directly on an industrially relevant slot die coating line. This is enabled by a multi-nozzle slot die coating head allowing parameter variations along and across the web. This modification allows us to generate and analyze 3750 devices in a single coating run, varying the active layer donor:acceptor ratio and the thickness of the electron transport layer (ETL). We use Gaussian Process Regression (GPR) to exploit the whole dataset for precise determination of the optimal parameter combination. Performance-relevant features of the active layer morphology are inferred from UV-Vis absorption spectra. By mapping morphology in this way, small undesired gradients of process conditions (extrusion rates, annealing temperatures) are detected and their effect on device performance is quantified. The correlation between process parameters, morphology and performance obtained by GPR provides hints to the underlying physics, which are finally quantified by automated high-throughput drift-diffusion simulations. This leads to the conclusion that voltage losses which are observed for very thin ETL coatings are due to incomplete coverage of the electrode by the ETL, which cause enhanced surface recombination. Coating ; Organic solar cell ; Materials science ; Die (integrated circuit) ; Roll-to-roll processing ; Photovoltaics ; Throughput ; Optical proximity correction ; Computer science ; Photovoltaic system ; Lithography ; Optoelectronics ; Nanotechnology ; Composite material ; Engineering ; Electrical engineering ; Polymer Machine Learning in Materials Science ; Green IT and Sustainability
Authors: Moksh Jain | Tristan Deleu | Jason Hartford | Chenghao Liu | Álex Hernández-García | Yoshua Bengio
Source: arXiv (Cornell University) (preprint, 2023-02-01)
Field: Materials Science — Materials Chemistry

GFlowNets for AI-Driven Scientific Discovery Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the pace of scientific discovery. While science has traditionally relied on trial and error and even serendipity to a large extent, the last few decades have seen a surge of data-driven scientific discoveries. However, in order to truly leverage large-scale data sets and high-throughput experimental setups, machine learning methods will need to be further improved and better integrated in the scientific discovery pipeline. A key challenge for current machine learning methods in this context is the efficient exploration of very large search spaces, which requires techniques for estimating reducible (epistemic) uncertainty and generating sets of diverse and informative experiments to perform. This motivated a new probabilistic machine learning framework called GFlowNets, which can be applied in the modeling, hypotheses generation and experimental design stages of the experimental science loop. GFlowNets learn to sample from a distribution given indirectly by a reward function corresponding to an unnormalized probability, which enables sampling diverse, high-reward candidates. GFlowNets can also be used to form efficient and amortized Bayesian posterior estimators for causal models conditioned on the already acquired experimental data. Having such posterior models can then provide estimators of epistemic uncertainty and information gain that can drive an experimental design policy. Altogether, here we will argue that GFlowNets can become a valuable tool for AI-driven scientific discovery, especially in scenarios of very large candidate spaces where we have access to cheap but inaccurate measurements or to expensive but accurate measurements. This is a common setting in the context of drug and material discovery, which we use as examples throughout the paper. Computer science ; Machine learning ; Data science ; Artificial intelligence ; Scientific discovery ; Leverage (statistics) ; Pipeline (software) ; Context (archaeology) ; Uncertainty quantification ; Probabilistic logic ; Estimator ; Big data ; Data mining ; Mathematics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Computational Drug Discovery Methods
Authors: A. Cisse | Xenophon Evangelopoulos | Sam Carruthers | Vladimir V. Gusev | Andrew I. Cooper
Source: arXiv (Cornell University) (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

HypBO: Accelerating Black-Box Scientific Experiments Using Experts' Hypotheses Robotics and automation offer massive accelerations for solving intractable, multivariate scientific problems such as materials discovery, but the available search spaces can be dauntingly large. Bayesian optimization (BO) has emerged as a popular sample-efficient optimization engine, thriving in tasks where no analytic form of the target function/property is known. Here, we exploit expert human knowledge in the form of hypotheses to direct Bayesian searches more quickly to promising regions of chemical space. Previous methods have used underlying distributions derived from existing experimental measurements, which is unfeasible for new, unexplored scientific tasks. Also, such distributions cannot capture intricate hypotheses. Our proposed method, which we call HypBO, uses expert human hypotheses to generate improved seed samples. Unpromising seeds are automatically discounted, while promising seeds are used to augment the surrogate model data, thus achieving better-informed sampling. This process continues in a global versus local search fashion, organized in a bilevel optimization framework. We validate the performance of our method on a range of synthetic functions and demonstrate its practical utility on a real chemical design task where the use of expert hypotheses accelerates the search performance significantly. Bayesian optimization ; Computer science ; Machine learning ; Artificial intelligence ; Exploit ; Bayesian probability ; Surrogate model ; Task (project management) ; Bayesian inference ; Process (computing) ; Data mining ; Engineering Machine Learning in Materials Science ; Machine Learning and Algorithms ; Machine Learning and Data Classification
Authors: Vito Giovanni Castellana | Nícolas Bohm Agostini | Ankur Limaye | Vinay Amatya | Marco Minutoli | Joseph Manzano | Antonino Tumeo | Serena Curzel | Michele Fiorito | Fabrizio Ferrandi
Source: Proceedings of the 28th Asia and South Pacific Design Automation Conference (article, 2023-01-16)
Field: Materials Science — Surfaces, Coatings and Films

Towards On-Chip Learning for Low Latency Reasoning with End-to-End Synthesis The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frameworks), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This paper provides an overview of the flow in the context of the generation of accelerators for edge processing to be integrated in transmission electron microscopy (TEM) devices, focusing on use cases from precision material synthesis. We show the tool in action with an example of design space exploration for inference on reconfigurable devices with a conventional deep neural network model (LeNet). Finally, we discuss the research directions and opportunities enabled by SODA in the area of autonomous control for scientific experimental workflows. Computer science ; Workflow ; Computer architecture ; Field-programmable gate array ; Design space exploration ; High-level synthesis ; Context (archaeology) ; Compiler ; Embedded system ; Computer hardware ; Programming language Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Daniel Potemkin | Carlos Y. Soto | Ruipeng Li | Kevin G. Yager | Esther H. R. Tsai
Source: arXiv (Cornell University) (preprint, 2023-12-28)
Field: Materials Science — Materials Chemistry

Virtual Scientific Companion for Synchrotron Beamlines: A Prototype The extraordinarily high X-ray flux and specialized instrumentation at synchrotron beamlines have enabled versatile in-situ and high throughput studies that are impossible elsewhere. Dexterous and efficient control of experiments are thus crucial for efficient beamline operation. Artificial intelligence and machine learning methods are constantly being developed to enhance facility performance, but the full potential of these developments can only be reached with efficient human-computer-interaction. Natural language is the most intuitive and efficient way for humans to communicate. However, the low credibility and reproducibility of existing large language models and tools demand extensive development to be made for robust and reliable performance for scientific purposes. In this work, we introduce the prototype of virtual scientific companion (VISION) and demonstrate that it is possible to control basic beamline operations through natural language with open-source language model and the limited computational resources at beamline. The human-AI nature of VISION leverages existing automation systems and data framework at synchrotron beamlines. Beamline ; Computer science ; Automation ; Credibility ; Synchrotron ; Systems engineering ; Engineering ; Mechanical engineering ; Physics ; Optics Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Namkyeong Lee | Heewoong Noh | Gyoung S. Na | Jimeng Sun | Tianfan Fu | Zitnik, Marinka | Chanyoung Park
Source: arXiv (Cornell University) (preprint, 2023-11-17)
Field: Materials Science — Materials Chemistry

Compositional Representation of Polymorphic Crystalline Materials Machine learning (ML) has seen promising developments in materials science, yet its efficacy largely depends on detailed crystal structural data, which are often complex and hard to obtain, limiting their applicability in real-world material synthesis processes. An alternative, using compositional descriptors, offers a simpler approach by indicating the elemental ratios of compounds without detailed structural insights. However, accurately representing materials solely with compositional descriptors presents challenges due to polymorphism, where a single composition can correspond to various structural arrangements, creating ambiguities in its representation. To this end, we introduce PCRL, a novel approach that employs probabilistic modeling of composition to capture the diverse polymorphs from available structural information. Extensive evaluations on sixteen datasets demonstrate the effectiveness of PCRL in learning compositional representation, and our analysis highlights its potential applicability of PCRL in material discovery. The source code for PCRL is available at https://github.com/Namkyeong/PCRL. Stoichiometry ; Representation (politics) ; Probabilistic logic ; Flexibility (engineering) ; Computer science ; Crystal (programming language) ; Code (set theory) ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Mathematics ; Programming language ; Physical chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Geochemistry and Geologic Mapping
Authors: Nicholas Walker | John Dagdelen | Kevin Cruse | Sang‐Hoon Lee | Samuel Gleason | Alexander Dunn | Gerbrand Ceder | A. Paul Alivisatos | Kristin A. Persson | Anubhav Jain
Source: arXiv (Cornell University) (preprint, 2023-04-26)
Field: Materials Science — Materials Chemistry

Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3 Although gold nanorods have been the subject of much research, the pathways for controlling their shape and thereby their optical properties remain largely heuristically understood. Although it is apparent that the simultaneous presence of and interaction between various reagents during synthesis control these properties, computational and experimental approaches for exploring the synthesis space can be either intractable or too time-consuming in practice. This motivates an alternative approach leveraging the wealth of synthesis information already embedded in the body of scientific literature by developing tools to extract relevant structured data in an automated, high-throughput manner. To that end, we present an approach using the powerful GPT-3 language model to extract structured multi-step seed-mediated growth procedures and outcomes for gold nanorods from unstructured scientific text. GPT-3 prompt completions are fine-tuned to predict synthesis templates in the form of JSON documents from unstructured text input with an overall accuracy of $86\%$. The performance is notable, considering the model is performing simultaneous entity recognition and relation extraction. We present a dataset of 11,644 entities extracted from 1,137 papers, resulting in 268 papers with at least one complete seed-mediated gold nanorod growth procedure and outcome for a total of 332 complete procedures. Nanorod ; Computer science ; Template ; Artificial intelligence ; Nanotechnology ; Materials science ; Programming language Machine Learning in Materials Science
Authors: Rachel K. Luu | Markus J. Buehler
Source: arXiv (Cornell University) (preprint, 2023-09-15)
Field: Materials Science — Materials Chemistry

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-inspired Materials The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge has been systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model was finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further enhanced with enhanced reasoning ability, as well as with retrieval-augmented generation to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has been shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model showed impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains. Computer science ; Generative grammar ; Workflow ; Field (mathematics) ; Artificial intelligence ; Data science Machine Learning in Materials Science