Mehrad Ansari
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View article: 32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery
32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery Open
Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent develo…
View article: Kernel learning assisted synthesis condition exploration for ternary spinel
Kernel learning assisted synthesis condition exploration for ternary spinel Open
Machine learning and high-throughput experimentation have greatly accelerated the discovery of mixed metal oxide catalysts by leveraging their compositional flexibility. However, the lack of established synthesis routes for solid-state mat…
View article: Bayesian Optimization Hackathon for Chemistry and Materials
Bayesian Optimization Hackathon for Chemistry and Materials Open
The Acceleration Consortium and Merck KGaA hosted a 2-day virtual hackathon on March 27- 28, 2024, bringing together scientists to explore, collaborate, and innovate in the field of Bayesian optimization for the physical sciences. Particip…
View article: 34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery Open
Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent develo…
View article: Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Open
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submis…
View article: Learning peptide properties with positive examples only
Learning peptide properties with positive examples only Open
Using no negative examples, we create a semi-supervised learning framework to discover peptide sequences that are likely to map to certain antimicrobial properties via positive-unlabeled learning (PU).
View article: Agent-based learning of materials datasets from the scientific literature
Agent-based learning of materials datasets from the scientific literature Open
An AI Agent for autonomous development of materials dataset from scientific literature.
View article: Agent-based Learning of Materials Datasets from Scientific Literature
Agent-based Learning of Materials Datasets from Scientific Literature Open
Advancements in machine learning and artificial intelligence are transforming materials discovery. Yet, the availability of structured experimental data remains a bottleneck. The vast corpus of scientific literature presents a valuable and…
View article: Learning Peptide Properties with Positive Examples Only
Learning Peptide Properties with Positive Examples Only Open
A bstract Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in th…
View article: Serverless Prediction of Peptide Properties with Recurrent Neural Networks
Serverless Prediction of Peptide Properties with Recurrent Neural Networks Open
We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence…
View article: History-Agnostic Battery Degradation Inference
History-Agnostic Battery Degradation Inference Open
Lithium-ion batteries (LIBs) have attracted widespread attention as an efficient energy storage device on electric vehicles (EV) to achieve emission-free mobility. However, the performance of LIBs deteriorates with time and usage, and the …
View article: Assessment of chemistry knowledge in large language models that generate code
Assessment of chemistry knowledge in large language models that generate code Open
In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes.
View article: Assessment of chemistry knowledge in large language models that generate code
Assessment of chemistry knowledge in large language models that generate code Open
In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes. To evaluate this, we produce a benchmark set of problems, and evaluate these models based on correctness …
View article: Inferring spatial source of disease outbreaks using maximum entropy
Inferring spatial source of disease outbreaks using maximum entropy Open
Mathematical modeling of disease outbreaks can infer the future trajectory of an epidemic, allowing for making more informed policy decisions. Another task is inferring the origin of a disease, which is relatively difficult with current ma…
View article: Do large language models know chemistry?
Do large language models know chemistry? Open
Mostly yes. We systematically evaluate machine learning large language models (LLMs) that generate code in the context of chemistry. We produce a benchmark set of problems, and evaluate these models based on correctness of code by automate…
View article: Simulation-based inference with approximately correct parameters via maximum entropy
Simulation-based inference with approximately correct parameters via maximum entropy Open
Inferring the input parameters of simulators from observations is a crucial challenge with applications from epidemiology to molecular dynamics. Here we show a simple approach in the regime of sparse data and approximately correct models, …
View article: Iterative symbolic regression for learning transport equations
Iterative symbolic regression for learning transport equations Open
Computational fluid dynamics (CFD) analysis is widely used in chemical engineering. Although CFD calculations are accurate, the computational cost associated with complex systems makes it difficult to obtain empirical equations between sys…
View article: Inferring Spatial Source of Disease Outbreaks using Maximum Entropy
Inferring Spatial Source of Disease Outbreaks using Maximum Entropy Open
Mathematical modeling of disease outbreaks can infer the future trajectory of an epidemic, which can inform policy decisions. Another task is inferring the origin of a disease, which is relatively difficult with current mathematical models…