diff --git a/bib/biblio.txt b/bib/biblio.txt index a6d5f7c..73aa214 100644 --- a/bib/biblio.txt +++ b/bib/biblio.txt @@ -48,76 +48,88 @@ 25. Amirkulova, D. B. & White, A. D. Recent advances in maximum entropy biasing techniques for molecular dynamics. Molecular Simulation 45, 1285–1294 (2019). -26. Promoting transparency and reproducibility in enhanced molecular simulations. Nature methods 16, 670–673 (2019). +26. Chakraborty, M., Xu, J. & White, A. D. Is preservation of symmetry necessary for coarse-graining? Physical Chemistry Chemical Physics 22, 14998–15005 (2020). -27. Chakraborty, M., Xu, J. & White, A. D. Is preservation of symmetry necessary for coarse-graining? Physical Chemistry Chemical Physics 22, 14998–15005 (2020). +27. Chakraborty, M., Ziatdinov, M., Dyck, O., Jesse, S., White, A. D. & Kalinin, S. V. Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data. Journal of Applied Physics 127, (2020). -28. Chakraborty, M., Ziatdinov, M., Dyck, O., Jesse, S., White, A. D. & Kalinin, S. V. Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data. Journal of Applied Physics 127, (2020). +28. Li, Z., Wellawatte, G. P., Chakraborty, M., Gandhi, H. A., Xu, C. & White, A. D. Graph neural network based coarse-grained mapping prediction. Chemical science 11, 9524–9531 (2020). -29. Li, Z., Wellawatte, G. P., Chakraborty, M., Gandhi, H. A., Xu, C. & White, A. D. Graph neural network based coarse-grained mapping prediction. Chemical science 11, 9524–9531 (2020). +29. Tang, J., Zhang, Y., Luehmann, A. & White, A. Augmented reality improved learning of lower-level students by empowering their participation in collaborative activities. (2020). -30. Tang, J., Zhang, Y., Luehmann, A. & White, A. Augmented reality improved learning of lower-level students by empowering their participation in collaborative activities. (2020). +30. Barrett, R., Chakraborty, M., Amirkulova, D., Gandhi, H., Wellawatte, G. & White, A. Hoomd-tf: Gpu-accelerated, online machine learning in the hoomd-blue molecular dynamics engine. Journal of Open Source Software 5, (2020). -31. Barrett, R., Chakraborty, M., Amirkulova, D., Gandhi, H., Wellawatte, G. & White, A. Hoomd-tf: Gpu-accelerated, online machine learning in the hoomd-blue molecular dynamics engine. Journal of Open Source Software 5, (2020). +31. Amirkulova, D. B., Chakraborty, M. & White, A. D. Experimentally consistent simulation of aβ21–30 peptides with a minimal NMR bias. The Journal of Physical Chemistry B 124, 8266–8277 (2020). -32. Amirkulova, D. B., Chakraborty, M. & White, A. D. Experimentally consistent simulation of aβ21–30 peptides with a minimal NMR bias. The Journal of Physical Chemistry B 124, 8266–8277 (2020). +32. Gandhi, H. A., Jakymiw, S., Barrett, R., Mahaseth, H. & White, A. D. Real-time interactive simulation and visualization of organic molecules. (2020). -33. Gandhi, H. A., Jakymiw, S., Barrett, R., Mahaseth, H. & White, A. D. Real-time interactive simulation and visualization of organic molecules. (2020). +33. Barrett, R. & White, A. D. Investigating active learning and meta-learning for iterative peptide design. Journal of chemical information and modeling 61, 95–105 (2020). -34. Barrett, R. & White, A. D. Investigating active learning and meta-learning for iterative peptide design. Journal of chemical information and modeling 61, 95–105 (2020). +34. Barrett, R., Ansari, M., Ghoshal, G. & White, A. D. Simulation-based inference with approximately correct parameters via maximum entropy. Machine Learning: Science and Technology 3, 025006 (2022). -35. Barrett, R., Ansari, M., Ghoshal, G. & White, A. D. Simulation-based inference with approximately correct parameters via maximum entropy. Machine Learning: Science and Technology 3, 025006 (2022). +35. Yang, Z., Chakraborty, M. & White, A. D. Predicting chemical shifts with graph neural networks. Chemical Science (2021). -36. Yang, Z., Chakraborty, M. & White, A. D. Predicting chemical shifts with graph neural networks. Chemical Science (2021). +36. Gandhi, H. A. & White, A. D. City-wide modeling of vehicle-to-grid economics to understand effects of battery performance. ACS Sustainable Chemistry & Engineering 9, 14975–14985 (2021). -37. Gandhi, H. A. & White, A. D. City-wide modeling of vehicle-to-grid economics to understand effects of battery performance. ACS Sustainable Chemistry & Engineering 9, 14975–14985 (2021). +37. Wellawatte, G. P., Seshadri, A. & White, A. D. Model agnostic generation of counterfactual explanations for molecules. Chemical science 13, 3697–3705 (2022). -38. Wellawatte, G. P., Seshadri, A. & White, A. D. Model agnostic generation of counterfactual explanations for molecules. Chemical science 13, 3697–3705 (2022). +38. Ansari, M., Gandhi, H. A., Foster, D. G. & White, A. D. Iterative symbolic regression for learning transport equations. AIChE Journal 68, e17695 (2022). -39. Ansari, M., Gandhi, H. A., Foster, D. G. & White, A. D. Iterative symbolic regression for learning transport equations. AIChE Journal 68, e17695 (2022). +39. Hocky, G. M. & White, A. D. Natural language processing models that automate programming will transform chemistry research and teaching. Digital discovery 1, 79–83 (2022). -40. Hocky, G. M. & White, A. D. Natural language processing models that automate programming will transform chemistry research and teaching. Digital discovery 1, 79–83 (2022). +40. Ansari, M., Soriano-Paños, D., Ghoshal, G. & White, A. D. Inferring spatial source of disease outbreaks using maximum entropy. Physical Review E 106, 014306 (2022). -41. Ansari, M., Soriano-Paños, D., Ghoshal, G. & White, A. D. Inferring spatial source of disease outbreaks using maximum entropy. Physical Review E 106, 014306 (2022). +41. Hamsici, S., White, A. D. & Acar, H. Peptide framework for screening the effects of amino acids on assembly. Science Advances 8, eabj0305 (2022). -42. Hamsici, S., White, A. D. & Acar, H. Peptide framework for screening the effects of amino acids on assembly. Science Advances 8, eabj0305 (2022). +42. Krenn, M., Ai, Q., Barthel, S., Carson, N., Frei, A., Frey, N. C., Friederich, P., Gaudin, T., Gayle, A. A., Jablonka, K. M., et al. SELFIES and the future of molecular string representations. Patterns 3, (2022). -43. Krenn, M., Ai, Q., Barthel, S., Carson, N., Frei, A., Frey, N. C., Friederich, P., Gaudin, T., Gayle, A. A., Jablonka, K. M., et al. SELFIES and the future of molecular string representations. Patterns 3, (2022). +43. Cox, S. & White, A. D. Symmetric molecular dynamics. Journal of Chemical Theory and Computation 18, 4077–4081 (2022). -44. Cox, S. & White, A. D. Symmetric molecular dynamics. Journal of Chemical Theory and Computation 18, 4077–4081 (2022). +44. Kalinin, S. V., Ziatdinov, M., Sumpter, B. G. & White, A. D. Physics is the new data. arXiv preprint arXiv:2204.05095 (2022). -45. Kalinin, S. V., Ziatdinov, M., Sumpter, B. G. & White, A. D. Physics is the new data. arXiv preprint arXiv:2204.05095 (2022). +45. Ansari, M. & White, A. D. Serverless prediction of peptide properties with recurrent neural networks. Journal of Chemical Information and Modeling 63, 2546–2553 (2023). -46. Ansari, M. & White, A. D. Serverless prediction of peptide properties with recurrent neural networks. Journal of Chemical Information and Modeling 63, 2546–2553 (2023). +46. Zhu, W., Luo, J. & White, A. D. Federated learning of molecular properties with graph neural networks in a heterogeneous setting. Patterns 100521 (2022). -47. Zhu, W., Luo, J. & White, A. D. Federated learning of molecular properties with graph neural networks in a heterogeneous setting. Patterns 100521 (2022). +47. White, A. D. Deep learning for molecules and materials. Living journal of computational molecular science 3, (2022). -48. White, A. D. Deep learning for molecules and materials. Living Journal of Computational Molecular Science 3, 1499–1499 (2021). +48. Yang, Z., Milas, K. A. & White, A. D. Now what sequence? Pre-trained ensembles for bayesian optimization of protein sequences. bioRxiv 2022–08 (2022). -49. Yang, Z., Milas, K. A. & White, A. D. Now what sequence? Pre-trained ensembles for bayesian optimization of protein sequences. bioRxiv 2022–08 (2022). +49. Gandhi, H. A. & White, A. D. Explaining molecular properties with natural language. (2022). -50. Gandhi, H. A. & White, A. D. Explaining molecular properties with natural language. (2022). +50. Seshadri, A., Gandhi, H. A., Wellawatte, G. P. & White, A. D. Why does that molecule smell? (2022). -51. Seshadri, A., Gandhi, H. A., Wellawatte, G. P. & White, A. D. Why does that molecule smell? (2022). +51. Wellawatte, G. P., Gandhi, H. A., Seshadri, A. & White, A. D. A perspective on explanations of molecular prediction models. Journal of Chemical Theory and Computation 19, 2149–2160 (2023). -52. Wellawatte, G. P., Gandhi, H. A., Seshadri, A. & White, A. D. A perspective on explanations of molecular prediction models. Journal of Chemical Theory and Computation 19, 2149–2160 (2023). +52. White, A. D., Hocky, G. M., Gandhi, H. A., Ansari, M., Cox, S., Wellawatte, G. P., Sasmal, S., Yang, Z., Liu, K., Singh, Y., et al. Assessment of chemistry knowledge in large language models that generate code. Digital Discovery 2, 368–376 (2023). -53. White, A. D., Hocky, G. M., Gandhi, H. A., Ansari, M., Cox, S., Wellawatte, G. P., Sasmal, S., Yang, Z., Liu, K., Singh, Y., et al. Assessment of chemistry knowledge in large language models that generate code. Digital Discovery 2, 368–376 (2023). +53. Wellawatte, G. P., Hocky, G. M. & White, A. D. Neural potentials of proteins extrapolate beyond training data. The Journal of Chemical Physics 159, (2023). -54. Wellawatte, G. P., Hocky, G. M. & White, A. D. Neural potentials of proteins extrapolate beyond training data. (2023). +54. Bran, A. M., Cox, S., Schilter, O., Baldassari, C., White, A. D. & Schwaller, P. ChemCrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv:2304.05376 (2023). -55. Bran, A. M., Cox, S., White, A. D. & Schwaller, P. ChemCrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv:2304.05376 (2023). +55. Ramos, M. C., Michtavy, S. S., Porosoff, M. D. & White, A. D. Bayesian optimization of catalysts with in-context learning. arXiv preprint arXiv:2304.05341 (2023). -56. Ramos, M. C., Michtavy, S. S., Porosoff, M. D. & White, A. D. Bayesian optimization of catalysts with in-context learning. arXiv preprint arXiv:2304.05341 (2023). +56. Medina, J. & White, A. D. Bloom filters for molecules. Journal of Cheminformatics 15, 95 (2023). -57. Medina, J. & White, A. D. Bloom filters for molecules. arXiv preprint arXiv:2304.05386 (2023). +57. Campbell, Q. L., Herington, J. & White, A. D. Censoring chemical data to mitigate dual use risk. arXiv preprint arXiv:2304.10510 (2023). -58. Lo, A., Pollice, R., Nigam, A., White, A. D., Krenn, M. & Aspuru-Guzik, A. Recent advances in the self-referencing embedding strings (SELFIES) library. arXiv preprint arXiv:2302.03620 (2023). +58. Medina, J. & White, A. D. Active learning in symbolic regression performance with physical constraints. arXiv preprint arXiv:2305.10379 (2023). -59. Campbell, Q. L., Herington, J. & White, A. D. Censoring chemical data to mitigate dual use risk. arXiv preprint arXiv:2304.10510 (2023). +59. Ansari, M. & White, A. D. Learning peptide properties with positive examples only. Digital Discovery 3, 977–986 (2024). -60. Medina, J. & White, A. D. Active learning in symbolic regression performance with physical constraints. arXiv preprint arXiv:2305.10379 (2023). +60. White, A. D. The future of chemistry is language. Nature Reviews Chemistry 7, 457–458 (2023). -61. Ansari, M. & White, A. D. Learning peptide properties with positive examples only. bioRxiv 2023–06 (2023). +61. Ramos, M. C. & White, A. D. Predicting small molecules solubility on endpoint devices using deep ensemble neural networks. Digital Discovery 3, 786–795 (2024). -62. White, A. D. The future of chemistry is language. Nature Reviews Chemistry 1–2 (2023). +62. Jablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D., et al. 14 examples of how LLMs can transform materials science and chemistry: A reflection on a large language model hackathon. Digital Discovery 2, 1233–1250 (2023). + +63. Promoting transparency and reproducibility in enhanced molecular simulations. Nature methods 16, 670–673 (2019). + +64. Lo, A., Pollice, R., Nigam, A., White, A. D., Krenn, M. & Aspuru-Guzik, A. Recent advances in the self-referencing embedded strings (SELFIES) library. Digital Discovery 2, 897–908 (2023). + +65. Lála, J., O’Donoghue, O., Shtedritski, A., Cox, S., Rodriques, S. G. & White, A. D. Paperqa: Retrieval-augmented generative agent for scientific research. arXiv preprint arXiv:2312.07559 (2023). + +66. Cox, S., Hammerling, M., Lála, J., Laurent, J., Rodriques, S., Rubashkin, M. & White, A. WikiCrow: Automating synthesis of human scientific knowledge (2024). + +67. Ramos, M. C., Collison, C. J. & White, A. D. A review of large language models and autonomous agents in chemistry. arXiv preprint arXiv:2407.01603 (2024). + +68. Laurent, J. M., Janizek, J. D., Ruzo, M., Hinks, M. M., Hammerling, M. J., Narayanan, S., Ponnapati, M., White, A. D. & Rodriques, S. G. LAB-bench: Measuring capabilities of language models for biology research. arXiv preprint arXiv:2407.10362 (2024). diff --git a/bib/data.yml b/bib/data.yml index 9b5e582..704314d 100644 --- a/bib/data.yml +++ b/bib/data.yml @@ -1,3 +1,66 @@ +labbench: + key: + - laurent + - lab-bench + - 2024 + tweet: https://x.com/andrewwhite01/status/1813235301973921873 + link: https://doi.org/10.48550/arXiv.2407.10362 + pdf: https://arxiv.org/pdf/2407.10362 + code: https://huggingface.co/datasets/futurehouse/lab-bench + +wikicrow: + key: + - cox + - hammerling + - wikicrow + - knowledge + - 2024 + tweet: https://x.com/SGRodriques/status/1733193844110557432 + link: https://www.futurehouse.org/wikicrow + code: https://github.com/Future-House/WikiCrow + +llmhackathon2023: + key: + - llm + - hackathon + - 2023 + tweet: https://x.com/kmjablonka/status/1668506349465116673 + link: https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00113j + pdf: https://pubs.rsc.org/en/content/articlepdf/2023/dd/d3dd00113j + +llmreview: + key: + - ramos + - collison + - autonomous + - review + - 2024 + tweet: https://x.com/andrewwhite01/status/1808505284291375380 + link: https://doi.org/10.48550/arXiv.2407.01603 + pdf: https://arxiv.org/pdf/2407.01603 + code: https://github.com/ur-whitelab/LLMs-in-science + +deepensemble: + key: + - ramos + - solubility + - ensemble + - 2024 + tweet: https://x.com/andrewwhite01/status/1780944395656744962 + link: https://pubs.rsc.org/en/content/articlelanding/2024/dd/d3dd00217a + pdf: https://pubs.rsc.org/en/content/articlepdf/2024/dd/d3dd00217a + app: https://mol.dev/ + code: https://github.com/ur-whitelab/mol.dev + +paperqa: + key: + - paperqa + - 2023 + tweet: + link: https://doi.org/10.48550/arXiv.2312.07559 + pdf: https://arxiv.org/pdf/2312.07559 + code: https://github.com/Future-House/paper-qa + alcfd: key: - ansari @@ -20,7 +83,6 @@ serverless: app: https://peptide.bio code: https://github.com/ur-whitelab/peptide-dashboard - maxent: key: - ansari @@ -311,7 +373,7 @@ nnff: - 2023 - extrapolate tweet: https://twitter.com/andrewwhite01/status/1605937402509492225 - link: https://chemrxiv.org/engage/chemrxiv/article-details/63a3bfac81e4ba77ec56166a + link: https://pubs.aip.org/aip/jcp/article/159/8/085103/2908350 pdf: https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/63a3bfac81e4ba77ec56166a/original/neural-potentials-of-proteins-extrapolate-beyond-training-data.pdf alsr: @@ -340,8 +402,8 @@ chemcrow: - cox - 2023 tweet: https://twitter.com/andrewwhite01/status/1670794000398184451 - link: https://arxiv.org/abs/2304.05376 - pdf: https://arxiv.org/pdf/2304.05376 + link: https://www.nature.com/articles/s42256-024-00832-8 + pdf: https://www.nature.com/articles/s42256-024-00832-8.pdf code: https://github.com/ur-whitelab/chemcrow-public/ outputs: https://github.com/ur-whitelab/chemcrow-runs @@ -355,15 +417,14 @@ chemfuture: link: https://www.nature.com/articles/s41570-023-00502-0 pdf: https://www.nature.com/articles/s41570-023-00502-0.pdf - bloom: key: - bloom - 2023 - medina tweet: https://twitter.com/andrewwhite01/status/1646171800940969984 - link: https://arxiv.org/abs/2304.05386 - pdf: https://arxiv.org/pdf/2304.05386.pdf + link: https://link.springer.com/article/10.1186/s13321-023-00765-1%20 + pdf: https://link.springer.com/content/pdf/10.1186/s13321-023-00765-1.pdf code: https://github.com/whitead/molbloom icl: @@ -387,10 +448,10 @@ selfies-code: pul: key: - - 2023 - positive - ansari + - 2024 tweet: https://twitter.com/andrewwhite01/status/1666104177360084995 - link: https://www.biorxiv.org/content/10.1101/2023.06.01.543289v1.abstract - pdf: https://www.biorxiv.org/content/10.1101/2023.06.01.543289v1.full.pdf+html + link: https://pubs.rsc.org/en/content/articlelanding/2024/dd/d3dd00218g + pdf: https://pubs.rsc.org/en/content/articlepdf/2024/dd/d3dd00218g code: https://github.com/ur-whitelab/pu-peptides \ No newline at end of file diff --git a/src/index.html b/src/index.html index 9edd3b9..2946b2e 100644 --- a/src/index.html +++ b/src/index.html @@ -46,7 +46,6 @@

whitelab@rochester

phd jorge medina <jmedina9@ur.rochester.edu>, - ziyue yang <zyang43@ur.rochester.edu>, sam cox <swrig30@ur.rochester.edu>, shane smictavy <smichtav@che.rochester.edu>, quintina campbell <qcampbe2@ur.rochester.edu> @@ -62,31 +61,18 @@

whitelab@rochester

bio - Andrew White is an associate of professor at University of - Rochester in chemical engineering with affiliate appointments in chemistry, biophysics, materials science, and data science. - He has a PhD in chemical engineering from University of Washington and - did postdoc training in chemistry at University of Chicago. - White's research group studies the deep learning and molecular simulation of peptides - and small molecules. He and his group - work on the adaption of deep learning to chemistry and materials, with research on - graph neural networks, explaining deep learning models, large language models, and Bayesian optimization. - Andrew has won young investigator awards from NSF and NIH, professional soceity awards in chemical eng, - teaching awards from the University of Rochester, and engineer of the year in Rochester, NY. Andrew's group - is currently funded by the DOE, NSF, and NIH. + Andrew White is a researcher with over 50 peer-reviewed publications and books across the domains of large language models in chemistry, explainable artificial intelligence, statistical mechanics, and chemical engineering. He has won junior investigator awards from the National Science Foundation and National Institutes of Health along with professional and teaching awards for excellence as a chemical engineer. Andrew is an active member of the scientific community as a peer reviewer for over 30 journals, multiple national and private grant awarding institutions, and serves on the Chemical Sciences Roundtable at the National Academy of Science. Andrew is also a science communicator with large followings on X and LinkedIn and has been interviewed in multiple publications such as the New York Times, Bloomberg, Nature, Financial Times, and Science. Andrew serves on multiple scientific advisory boards across biotech. He has contributed to the ongoing debate around safety of artificial intelligence as a GPT-4 red teamer, speaking at multiple policy summits, and visiting the White House to advise multiple agencies. twitter - @andrewwhite01 - , @ZiyueYang37, @SamCox822, + @andrewwhite01, + @SamCox822, @MichtavyShane, - - @Kyam888, + @maykcaldas, @quinnycampbell, - @4everstudent95, + @4everstudent95 @@ -96,7 +82,7 @@

whitelab@rochester

Nature, New Scientist, Financial Times, - Nature Careers, + Nature Careers @@ -104,6 +90,16 @@

whitelab@rochester

doe bes de-sc0023354, nsf cbet #1751471, nsf dge #1922591, nsf dmr #2103553, nih #R35GM137966.
previous: nsf che #1764415, nsf iis #2029095 + + alumni + + ziyue yang <@ZiyueYang37>, + geemi wellawate <@GWellawatte>, + mehrad ansari <@MehradAnsari>, + heta gandhi <@gandhi_heta>, + rainier barrett <@Rainier_B> + +